&EFA
United States
Environmental Protection
Agency
Industrial Environmental Research
Laboratory
Cincinnati OH 45268
EPA-600/9-8C
June 1980
Research and Development
Oil Shale
Symposium
Sampling, Analysis
and Quality
Assurance
March 1979
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. “Special’ Reports
9. Miscellaneous Reports
This document is available to the public through the National Technical Informa-
tion Service. Springfield, Virginia 22161.
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OIL SHALE SYMPOSIUM:
SAMPLING, ANALYSIS AND QUALITY ASSURANCE
MARCH 1979
by
Charles Gale (Editor)
Charles H. Prien Center for Oil Shale Studies
Denver Research Institute
University of Denver
Denver, Colorado 80208
Grant No. R806156
Project Officer
Paul E. Mills
Program Operations Office
Industrial Environmental Research Laboratory
Cincinnati, Ohio 45268
INDUSTRIAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Industrial Environmental Research
Labortory-Cincinnati, U.S. Environmental Protection Agency, and approved
for publication. Approval does not signify that the contents necessarily
reflect the views and policies of the U.S. Environmental Protection Agency,
nor does mention of trade names or commercial products constitute endorse-
or recomendation for use.
11
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FOREWORD
When energy and material resources are extracted, processed converted,
and used, the related pollutional impacts on our environment and even on our
health often require that new and increasingly more efficient pollution
control methods be used. The Industrial Environmental Research Laboratory-
Cincinnati (IERL-Ci) assists in developing and demonstrating new and
improved methodologies that will meet these needs both efficiently and
economically.
This report contains the proceedings of the Oil Shale Symposium:
Sampling, Analysis, and Quality Assurance sponsored by IERL-Ci which met in
Denver, Colorado, March 26-28, 1979. The presented papers discussed the
experiences of researchers in the chemical, biological, and physical
sciences that apply to oil shale sampling analysis, and quality assurance.
This report will serve as a state-of-the-art guide to those who are involved
in oil shale related endeavors, a large and rapidly growing group. For
further information contact the Branch of Oil Shale and Energy Mining,
Energy Pollution Control Division (IERL-Ci).
David G. Stephan
Director,
Industrial Environmental Research Laboratory
Cincinnati
111
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ABSTRACT
This report presents the papers given at the IERL-Ci “Oil Shale Sympo-
sium: Sampling, Analysis, and Quality Assurance,” March 26-28, 1979,
Denver, Colorado.
This symposium brought together expert scientists from a variety of
disciplines, their papers present methodologies for pollution analysis
relevant to the oil shale industry. Cooperation and information exchange
among academic, industrial, and governmental researchers were prime objec-
tives.
.Topics discussed include: pollutants which can and should be charac-
terized and quantified, media to be examined, health effects, sampling and
analysis methods, quality assurance needs, future directions of methodology,
reference materials, and instrumentation development. Opinions from govern-
mental, industrial, and academic researchers concerning the future needs in
these areas are presented.
This report was submitted in fulfillment of Grant No. R806156 by Denver
Research Institute under the partial sponsorship of the United States En-
vironmental Protection Agency. This report covers a period from September,
1978, to October, 1979. Work was completed as of February 15, 1980.
iv
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CONTENTS
Disclaimer. ii
Foreword iii
Abstract iv
Acknowledgment x
Papers Contributed:
Quality Assurance and Pollution Control Technology
Research 1
Paul E. Mills
EPA’s Quality Assurance Program for Water and Waste
Analyses 3
John A. Winter
EPA Regulatory/Research Program 12
Terry L. Thoem , A. Christianson, E. Harris,
E. Bates and W. McCarthy
Quality Assurance as Imposed by Federal, State and
Local Regulations 22
Reed Clayson and Harry McCarthy
Sampling Design for Baseline Studies of the Colorado
Oil Shale Region 32
Ronald W. Kiusman , Charles D. Ringrose,
Robert J. Candito and Bruce Zuccaro
Ambient Air Sampling and Analytical Procedures for
Oil Shale Development Areas 58
David C. Sheesley
EPA R&D Efforts in the Development of Oil Shale
(Luncheon Address) 59
Steven R. Reznek
A Conceptual Model for an Integrated Environmental
Assessment on Oil Shale Tract C-b 63
P.T. Haug and G.M. Van Dyne
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Groundwater Quality Sampling Approaches for Monitoring
Oil Shale Development 86
G.C. Slawson, Jr . and L.G. McMillion
Field Sampling, Laboratory Analysis and Data Handling
QA Water Regulations on States 10].
Douglas M. Skie
Quality Assurance in Sampling arid Analysis of
Oil Shale Retorting Operations . 102
R.N. Fleistand , L.L. Morriss and R.A. Atwood
Use of Zeeman Atomic Absorption Spectroscopy for
the Measurement of Mercury in Oil Shale Gases 109
D.C. Girvin , T. Hadeishi and J.P. Fox
A Sampling and Analysis Procedure for Gaseous
Sulfur Compounds from Fossil Fuel Conversion . . 124
S.K. Gangwal , D.G. Nichols, R.K.M. Jayanty,
D.E. Wagoner and P.M. Grohse
Fugitive Dust and Offgas Analysis Methods Applied
at the Paraho Facility 140
R.N. Heistand and Jack E. Cotter
Intercomparison Study of Elemental Abundances in
Raw and Spent Oil Shales 159
T.R. Wildeman , J.P. Fox, J.C. Evans and J.S. Fruchter
Interlaboratory, Multimethod Study of an In Situ
Produced Oil Shale Process Water 182
0.5. Farrier , J.P. Fox and R.E. Poulson
Role of Organic Compounds in the Mobility of Trace Metals 211
Mary A. Caolo , John Stanley, R.R. Meglen
and R.E. Sievers
Retort Water Particulates. . . 226
J.P. Fox
Analysis of Paraho Oil Shale Products and Effluents:
An Example of the Multitechnioue Approach 251
J.S. Fruchter , J.C. Evans, R.W. Sanders
and C.L. Wilkerson
Applications of Dissolved Organic Carbon
Fractionation Analysis to the Characterization
of Oil Shale Processing Waters 273
Jerry A. Leenheer and David S. Farrier
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Sampling Strategies in Groundwater Transport and
Fate Studies for In Situ Oil Shale Retorting 286
K.D. Pimentel , D.H. Stuermer and M.M. Moody
The Determination of Fluorine in Oil Shale
Related Matrices Using Graphit.e Furnace
Molecular Absorption 303
Robert Meglen and Alexandra Krikos
Sample Size Required for Analysis of Oil Shale of
Widely Varying Grade and Particle Size 311
James F. Carley
Sampling and Handling of Oil Shale Solids and Liquids 326
Thomas R. Wildeman
Factors to Consider in the Design of a Water Quality
Monitoring Network 343
Thomas G. Sanders and Robert. C. Ward
Quantitation of Individual Organic Compounds in
Shale Oil 355
L.R. Hilpert , H.S. Hertz, W.E. May, S.N. Chesler,
Sit. Wise, F.R. Guenther, J.M. Brown and R.M. Parris
Isolation and Identification of Organic Residue
from Processed Oil Shale 363
D.L. Maase , V.D. Adams, D.L. Sorensen
and D.B. Porcella
Polar Constituents of a Shale Oil: Comparative
Composition with Other Fossil-Derived Liquids 390
I.B. Rubin , N.A. Goeckner and B.R. Clark
Proton and Carbon-13 NMR Studies on Naphtha and
Light Distillate Hydrocarbon Fractions Obtained
from In Situ Shale Oil 402
D.A. Netzel , D.R. McKay, F.C•. Guffey, R.A. Heppner,
S.D. Cooke and D.L. Vane
A Continuous Flow Bioassay Technique for Assessing the
Toxicity of Oil-Shale-Related Effluents: Preliminary
Results with Two Species of Caddisfly Larvae 416
Peter P. Russell , Vincent H. Resh and Thomas S. Flynn
Biological Monitoring of Oil Shale Products and
Effluents Using Short-Term Ger.etic Analyses 431
T.K. Rao , J.L. Epler, M.R. Guerin, J.J. Schmidt-
Collerus and L. Leffler
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Dosimetry of Coal and Shale Derived Crude Liquids
as Mouse Skin Carcinogens 443
J.M. Holland , L.H. Smith, S.S. Chang, T.J. Stephens,
B.R. Clark and R.O. Rahn
The Carcinogenicity of Various Shale Oils and
Shale Oil Products 455
W. Barkley , K.L. Stemmer, J. Agee, R.R. Suskind
and E. Bingham
Chromosome Aberrations and Loss of Some Cell Functions
Following In Vitro Exposure to Retorted Oil Shale. . 464
Agnes N. Stroud
Detection of Chemical Mutagens in Spent Oil
Shale Using the Ames Test 473
J.G. Dickson , J.H. Manwaring, V.D. Adams,
D.B. Porcella and D.L. Sorensen
Flow Cytometric Methods for Assaying Damage
to Respiratory Tract Cells 492
John Steinkamp , Julie S. Wilson and Z.V. Svitra
Biological Monitoring Methodologies for Oil
Shale Area Surface Waters with Emphasis on
Macroinvertebrate Sampling Techniques 506
Wesley L. Kinney , Charles E. Hornig and
James E. Pollard
The Biology of a Plains Stream, Salt Wells Creek,
in an Oil Shale Area, Southwestern Wyoming 518
Morris J. Engelke, Jr .
Aquatic Toxicity Tests on Inorganic Elements
Occurring in Oil Shale 519
W.J. Birge , J.A. Black, A.G. Westerman and
J.E. Hudson
An Analytical Method for Assessing the Quality, by
Microbial Evaluation, of Aqueous Effluents Obtained
from an In Situ Oil Shale Process 535
W. Kennedy Gauger , Stephen E. Williams,
David S. Farrier and John C. Adams
Monitoring of Retorted Oil Shale Effects on Surface
Soil Nitrogen Fixation Processes: A Resource for
Design and Management of Land Reclamation Programs 546
D.A. Klein , L.E. Hersman and S-V. Wu
v i ii
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The Effects of Soil Phosphorus on Growth and
Endomycorrhizal Development in Plant Species
Native to Colorado’s Oil Shale Region 555
Jean E. Kiel
The Effect of Retorted Oil Shale on VA Mycorrhiza
Formation in Soil from the Piceance Basin of
Northwestern Colorado . . . . 566
Suzanne Schwab and F. Brent Reeves
Appendices
A. About the Authors . . 577
B. List of Attendees 582
ix
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ACKNOWLEDGMENTS
The cooperation of each author who presented the results recorded in
these proceedings is gratefully acknowledged. The efforts of the many tech-
nical reviewers who selected these papers and made suggestions for improve-
ments were appreciated. The session leaders kept the program on schedule
while allowing for stimulating, thought-provoking discussion periods follow-
ing each presentation.
By presenting some of the most current work in oil shale environmental
studies, the symposium attracted a large audience from the national and inter-
national scientific comunity. The organizers and participants were jointly
responsible for the success of the symposium.
x
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QUALITY ASSURANCE AND POLLUTION CONTROL TECHNOLOGY RESEARCH
Paul E. Mills
Quality Assurance Officer
Industrial Environmental Research Laboratory
U.S. Environmental Protection Agency
Pollution is a fact of life.
It’s an undesirable consequence of both natural and manmade processes.
What can we do to prevent or control pollution, rather than clean up
after it has occurred?
Our goals should be to minimize the environmental impact which may be
caused by development of the oil shale industry, and to do this with a
minimum of economic impact.
We need to confirm the effectiveness and applicability of pollution
control concepts. And we need to prove that the extraction processes used
will not have any unmanageable adverse effects.
It is the responsibility of EPA’s Industrial Environmental Research
Laboratories to develop and demonstrate new and improved pollution control
technology that will help us meet our nation’s energy needs. To accomplish
this, research must conform to the highest quality assurance standards.
Sampling and analytical services are an important aspect of most research
and demonstration projects. Decisions of both technical and economic impor-
tance are made based on the data generated by these sampling and analytical
programs.
The development and testing of effective poLlution control equipment is
the product of the efforts of scientists and engineers, working together
within social, economic, and regulatory constraints.
The pollution control technology now available be capable of doing
the job we need for oil shale development. But the effective use and ad-
vancement of that technology depends upon our ability to communicate mean-
ingful data as the basis for evaluations. At each step in the research
process we must have a commitment not only to exchange information, but to
assure the accuracy, reliability, and overall quality of our information.
Pollution control research requires a series of measurements. These
include:
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o Measurements to define and describe the pollutant.
o A measurement of the extent of pollution, and a measurement
of the effectiveness of controls in reducing pollution.
The key word is measurement . Scientists provide procedures for mea-
suring the physical, chemical, and biological parameters which characterize
pollution problems and solutions. Pollution control technology researchers
use these measurements to devise techniques and equipment that can reduce
pollution. This research is dependent on the quality of measurements if
effective controls are to be developed.
Quality Assurance encompasses all actions taken by an organization to
achieve accurate and reliable research results. An established Quality
Assurance program is essential for any organization to produce valid sam-
pling and analytical data.
Quality Assurance is an interiral part of the management of a total
research system. Since decisions are made from the data, and the data come
from samples, it is essential that the procedures for sample collection,
handling, analysis, and data interpretation be trustworthy.
But in addition to accurate, reliable information and the right equip-
ment, the positive attitudes of people who know what they’re doing is essen-
tial.
We need your commitment toward the planning, development, and applica-
tion of the appropriate control technology for oil shale development.
To achieve this will require an interactive, cooperative approach.
One example is this Oil Shale Symposium. Many of you in the audience
are acknowledged experts. You can present the state-of-the-art in methodol-
ogies; you can help define research problems, and establish directions for
future research. For this effort, cooperation and information exchange
among all researchers is the prime objective.
We must openly discuss the problems facing us, and be aware of what is
currently being done. It is in everyone’s interest to work together to
establish and verify methodologies, collect data, exchange information, and
assure the quality of scientific research.
It is imperative that we cooperate to assure the nation develops our
vital resources in a manner which is economically viable and environmentally
sound.
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EPA’S QUALITY ASSURANCE PROGRAM FOR WATER AND WASTE ANALYSIS
John A. Winter
U.S. Environmental Protection Agency
Environmental Monitoring and Support Laboratory
Cincinnati, Ohio 45268
ABSTRACT
The paper has described EPA’s Quality Assurance program for water and
waste analyses of all parameters required under the present environmental
legislation. The program includes:
1. Development of manuals and guidelines for quality control,
sampling, sample preservation and other support needs,
2. Distribution of quality control check samples,
3. Maintenance of a permanent repository for priority pollutants
and all other trace organics of interest to the agency,
4. Conduction of formal studies to validate selected methodolo-
gy,
5. Conduction of formal evaluation studies of laboratory per-
formance and determination of acceptance for approval or
formal certification, and
6. Operation of a formal equivalency program for approval of
alternate test procedures as required under the FWPCA Amend-
ments and the National Interim Primary Drinking Water Regu-
lations.
INTRODUCTION
Measurements of environmental samples are used in the assessment of
health effects, the setting of environmental standards and guidelines, and
the enforcement of environmental regulations. EPA established a quality
assurance program to assure that these measurements are reliable, and hence,
legally defensible, through development and implementation of uniform quali-
ty control procedures.
Table 1 describes responsibilities for quality assurance in EPA.
Program management was assigned to the Office of Research and Development
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for uniform application of the same criteria in a national program. How-
ever, the technical responsibility was divided into three analytical areas
of air, water, and radiation analyses because the methods of sample collec-
tion and analyses differ, the relevant laws or sections of laws differ, and
the time tables for regulation differ for each area.
TABLE 1. QUALiTY ASSURANCE PROGRAM IN EPA
Program Management Office of Monitoring & Technical Support
ORD, Washington, DC 20460
Radiation Environmental Monitoring & Support Laboratory
Environmental Research Center
Las Vegas, NV 89109
Air Environmental Monitoring & Support Laboratory
Environmental Research Center
Research Triangle Park, NC 27711
Water Environmental Monitoring & Support Laboratory
Environmental Research Center
Cincinnati, OH 45268
Regional Coordination Ten Regional Quality Assurance Coordinators
In addition, each Regional Administrator in EPA designated one person,
the Regional Quality Assurance Coordinator, to be the focal point for all
quality assurance activities in the Federal, state and local agencies under
his regional jurisdiction. The only change, as QA activities have expanded,
has been appointment of separate coordinators for air analyses and water
analyses in some regions.
QUALITY ASSURANCE PROGRAM
The Quality Assurance Program for water and wastewater analyses assign-
ed to the Environmental Monitoring and Support Laboratory at Cincinnati
(EMSL-CI) has five major functions as shown in Table 2.
Manuals and Guidelines
EMSL-CI has developed the guidance for quality control in sampling,
sample preservation, laboratory operations and analyses. EMSL-CI has also
provided the technical support necessary for selection of methodology and
quality assurance required under the Certification program for water supply
laboratories.
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TABLE 2. EPA’S QUALITY ASSURANCE PROGRAM FOR WATER/WASTEWATER ANALYSES
1. Development of Manuals and Guidelines.
Handbook for Analytical Quality Control in Water and Waste-
water Laboratories, EMSL-CI, 1979.
Sampling and Sample Preservation Manual, EMSL-CI, in prepara-
tion.
Input to: Manual for Interim Certification of Laboratories
involved in Analyzing Public Water Supplies, Office of Drink-
ing Water, 1978.
2. Provision of Quality Control Check Samples to within-labora-
tory Quality Assurance Programs for all parameters under the
1 aws.
3. Conauction of formal method validation studies for all param-
eters under the laws.
4. Conduction of Performance Evaluation Studies for laboratory
approval and/or formal certification for all parameters under
the laws.
5. Equivalency of Alternate Test Procedures.
Reference-Type Samples
The Quality Assurance Program for water and wastewater analyses uses
reference-type samples for all of its activities in method selection, method
validation, intralaboratory quality control, performance evaluation and
certification. The samples are used as knowns in intralaboratory quality
control activities or as unknowns in interlaboratory evaluations and method
studies.
The EPA samples are prepared as concentrates in Youden pairs 1 using
water or an organic liquid as solvent. Exact amount of high purity chemi-
cals are weighed, dissolved and brought to volume wIth ultrapure water or
other pure solvent to form the sample concentrate containing multiple param—
eters. The analyst in the user-laboratory dilutes the sample concentrates
to volume according to instructions, to produce samples with established
True Values. The “true value” concept is a key factor in EPA’s sample
design because it permits the establishment of accuracy and precision. Bias
and interference are then measured by comparison of the recoveries of
identical spikes into laboratory-pure and natural water or wastewater.
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USES OF THE REFERENCE-TYPE SAMPLES
Quality Control Samples
Quality Control (QC) samples are furnished without charge to interested
.governmental, industrial, commercial, and private laboratories for use as
secondary checks on their within-laboratory quality control programs. The
samples are intended as independent measures of technique and performance,
not as replacements for the standards, replicates or spike samples run
routinely as part of the laboratory’s own QC program.
There is no certification or other formal evaluative function resulting
from the use of QC samples. No reports are prepared and there is no re-
quirement for use of specific methodology in these QC analyses.
Method Validation Studies
The Environmental Monitoring and Support Laboratory conducts formal
interlaboratory studies to evaluate methods selected by EPA for its rnanu ils
sucb as: Methods for Chemical Analysis of Water and Wastes, Biological
Field and Laboratory Methods for Measuring the Quality of Surface Waters
and Effluents , and Microbiological Methods for Monitoring the Environment,
Water and Wastes . Federal, state, local, and industrial laboratories take
part in these roundrobin studies which carry deadlines and conclude with
preliminary reports distributed to all participants and formal study reports
prepared thereafter. In these reports, laboratories are identified only by
code number.
Performance Evaluation Studies
In this second type of interlaboratory study, samples are used as
unknowns to measure laboratory performance. Analytical results are rated
acceptable/nonacceptable as judged against preestablished performance limits
and the ratings are used by EPA programs for informal accreditation and
legally-required certification of laboratories.
Sample Types and Parameters
The chemical, biological and microbiological parameters in the Quality
Assurance Program respond to the three water laws, to energy-related water
monitoring needs and to the impact of the Toxic Substances Control Act and
Resource Conservation and Recovery Act on the water laws.
Except for natural materials (solids, chlorophyll and petroleum hydro-
carbons, etc.), the reference samples are prepared as concentrates in sealed
glass ampuls. When diluted to volume with distilled or natural water,
according to instructions, the concentrations of constituents range from
minimal detectable levels to those found in heavily polluted streams.
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Available Quality Control Samples for Water Quality Analyses
Demand Analyses--BOO, COO, TOC
Mineral/Physical Analyses--sodium, potassium, calcium, magnesium, pH,
sulfate, chloride, fluoride, alkalinity/acidity, total hardness.
total dissolved solids, and specific conductance
Mercury--organic and inorganic
Trace Metals--aluminum, arsenic, beryllium, cadmium, chromium, cobalt,
copper, iron, lead, manganese, mercury, nickel, selenium, vana-
dium, and zinc
Cyanide--simple and complex
Total Nonfilterable, Total Filterable, and Total Volatile Residue
Linear Alkylate Sulfonate--LAS, the anionic surfactant standard requir-
ed for the MBAS Test
Nitrilotriacetic Acid--phosphate substitute for detergents
Chlorophyll--spectrophotometric analyses
Chlorophyll--fluorometric analyses
Petroleum Hydrocarbons--two crude oils, #2 fuel oil and Bunker C, for
characterization analyses
Pesticides--aidrin, dieldriri, DOT, DDE, ODD, heptachior, chiordane
Polychlorinated Biphenyls--Aroclor 1254 and 1016
Volatile Organics--six-nine compounds, including THM’s
Available Quality Control Samples for Drinking Water Analyses
Nitrate/Fluoride
Trace Met.als-—arsenic, barium, cadmium, chromium, lead, mercury, sele-
nium, silver
Herbicides--2-4D, 2,4,5-TP
Pesticides--Endrin, lindane, met.hoxychlor, toxaphene
Turbidity
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Sample Types in Preparation :
Phthalate Esters Purgeables
Haloethers Acrolein, Acrylonitrile
Chlorinated Hydrocarbons Cyanide
Nitrobenzenes/Isophorone Antimony, Silver and Thallium
Nitrosarnines Residual Chlorine
Benzjdjnes Oil and Grease
Phenols (specific) Trihalomethanes
Polynuclear Aromatics Phenol--4AAP Method
Pesticides/PCBs Microbiology--Quantitative Samples
Aquatic Biology--Diatom, plankton, periphyton and macroinvertebrate
samples
REPOSITORY
In 1979. a permanent EPA repository was established by EMSL-Cincinnati
to provide calibration standards and other pure organic compounds to EPA,
other Federal, state and local laboratories as required under the water and
wastewater regulations. The first series to be prepared will be 114 prior-
ity pollutants, but the repository will be expanded to include other toxic
compounds and trace organics of interest to the Agency.
EQUIVALENCY
An added program function in Quality Assurance, Equivalency, is based
on the practical needs of the regulations. Both the Federal Water Pollution
Control Act Amendments and the Safe Drinking Water Act regulations specify
analytical methodology but recognize a need for adjustment in the use of
methods and the development of new methodology. This adjustment is de-
scribed under the Alternate Test Procedure mechanism of the regula-
tions. 2 If an industry, permittee or water treatment facility has a
methodology or instrument which produces results equivalent to the specified
methodology, the applicant is given an opportunity to provide analytical
data to prove this equivalency and obtain approval for use of the Alternate
Test Procedure (AlP) to satisfy requirements under the law.
There are two types of Alternate Test Procedure (ATP) approval based on
the extent of method use:
1. Limited Use--Requests for limited use applications may be
initiated by a discharger, water utility or by a private,
state or EPA regional laboratory for use within that region
or state. The proposed alternate method is forwarded to the
appropriate State Director or EPA Regional Administrator. If
the state and regional review determines the proposed method
is unacceptable, the application may be denied and the appli-
cant notified. If the application appears acceptable it is
forwarded to the Director, EMSL-CI for a technical review and
recommendation. EMSL-CI nas at this time three options:
1) recommend approval, 2) recommend denial, or 3) request
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additional information. Recommendations for approval or
denial are based on the technical information supplied with
the application, statistical review of data submitted, and
external technical review. Applications for approval of
alternate procedures for radiochemical analyses are sent for
review to the Environmental Monitoring and Support Laboratory
--Las Vegas and applications for drinking water analyses are
sent for review to the Municipal Environmental Research
Laboratory--Ci nci nnati.
2. Nationwide Use--This broad approval is a mechanism available
to instrument and analytical system manufacturers to permit
use of an alternate test procedure by any person to monitor
effluents or water supply samples in the program where ap-
proval has been granted. Applications for nationwide use of
ATP are sent to the Director, EMSL-CI.
Requirements of Proposals for Alternate Methodology-The proposer of an
alternate method must provide:
1. A detailed writeup of the analytical method.
2. Literature references supporting the alternate test method.
3. Satisfactory analytical data comparing the approved method
and the alternate method on the specific waters or waste-
waters to which the alternate method will be applied.
In first considering the application, the reviewer will consider wheth-
er approval -is needed under the law. The proposed method may simply be a
permissible option to the approved method. If judged an alternate method by
the reviewer, he will examine the data provided with the request to deter-
mine if there is sufficient comparative data and if the data support the
alternate procedure. The reviewer will then approve or disapprove the AlP
sufficiency and acceptability of the data. If additional data are required.
there are specific test protocols for limited use and nationwide use, as
shown in Tables 3 and 4.
TABLE 3. DATA REQUIREMENTS FOR LIMITED USE
APPROVAL OF ALTERNATE TEST PROCEDURE
1. Sources--One for permit holder or drinking water system.
Five for state or regional use.
2. Three samples from each source.
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3. Four replicate ahalyses each by the proposed and approved method.
Sources x Samples x Replicates x Methods — Total Analyses
3 4 2 — 24-120
TABLE 4. DATA REQUIREMENTS FOR NATIONWIDE
APPROVAL OF ALTERNATE TEST PROCEDURES
1. Five Industrial (Discharge) Sources Identified by Standard Indus-
trial Classification (SIC) Code or Five Drinking Water Sources.
2. Six samples from each source.
3. Four replicate analyses each, by the proposed and approved method.
Sources Samples Replicates Methods = Total Analyses
5 6 4 2 240
Statistical Testing--For applications involving the submission of
comparability data, the Equivalency Staff applies statistical techniques to
the data as shown in Table 5.
TABLE 5. STATISTICAL PROTOCOL FOR APPROVAL OF ALTERNATE TEST PROCEDURES
1.
Calculate mean and
standard deviation.
2.
Test for outliers.
3.
Check distribution
for normality.
4.
Test for equality
among within-sample standard deviations.
5.
Test for equality
of pooled within-sample variance.
6.
Test for equality
of method means.
7.
Conclusions.
Final Approval--The final approving authority for limited use applica-
tion resides with the EPA Regional Administrator. Nationwide approval for
the National Pollutant Discharge Elimination System (NPDES) analyses resides
with the Assistant Administrator, Office of Research and Development, EPA
Headquarters. National Interim Primary Drinking Water Regulations (NIPDWR)
nationwide approvals are made by the Deputy Assistant Administrator, Office
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of Drinking Water, EPA Headquarters. Notification of nationwide approval
will be made in the Federal Register , and in EPA’s Quality Assurance News-
letter, published quarterly by EMSL-Cincinnati. Approval or denial of the
AlPs is based on the regional review and the technical review and recomnien-
dation of EMSL.-Cincinnati.
REFERENCES
1. Youden, W.J. “Statistical Techniques for Collaborative Test,” AOAC,
Washington, DC, 1967.
2. “Parameters and Test Procedures, P.L. 92-500,” Federal Register ,
Vol. 38, No. 199, Tuesday, October 16, 1973, pp. 28758-28760.
3. “Amended Parameters and Test Procuedures, P.L. 92-500,” Federal
Register , Vol. 41, No. 232, Wednesday, December 1, 1976, pp. 52780-
52786.
4. “National Jnterim Primary Drinking Water Regulations,” Federal
Register , Vol. 40, No. 248, Wednesday, December 24, 1975, pp. 59566-
58587.
11
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EPA REGULATORY,’RESEARCH PROGRAM
1. Thoem, A. Christianson, E. Harris,
E. Bates and W. McCarthy
U.S. Environmental Protection Agency
ABSTRACT
Legislation in the form of the Clean Air Act, the Clean Water Act, the
Safe Drinking Water Act, and the Resource Conservation and Recovery Act
provide the primary framework for regulations which control potential envi-
ronmental impacts a5sociated with oil shale development. Uncertainty over
environmental requirements has been raised by some developers as a con-
straint to oil shale development. This paper attempts to dispel that
notion.
Results to date of EPA research programs conducted to characterize
residuals from oil shale processes, to develop appropriate monitoring meth-
odologies and to demonstrate mitigating pollution control practices are
discussed.
INTRODUCTION
EPA has legislative mandates (Figure 1) to protect air and water
quality, to insure a safe drinking water supply, and to provide for an
environment conducive for the enjoyment of man on this earth. In order to
accomplish these goals, EPA is involved in a partnership with state and
local environmental agencies (Figure 2) in the planning, implementation and
enforcement of legislation and regulations. EPA and the State environmental
agencies recognize that environment.al considerations play a role in the
determination of answers to the question of oil shale. How much? When?
This paper will (1) highlight the existing EPA environmental regulatory
requirements for the oil shale industry, (2) describe EPA research directed
toward answering the most important oil shale environmental questions facing
regulators of the oil shale industry, (3) discuss the interrelationship
between the regulatory and research effort, (4) discuss the relationships
which EPA has attempted to develop with other agencies, with the industry
and with the public in both the research and regulatory areas, (5) provide
results, answers, and updates on progress and activities of EPA during the
past year, (6) list outstanding environmental issues which need to be
answered prior to the development of an oil shale industry, and (7) conclude
with a discussion of the EPA Region VIII (and to a certain degree Agency)
position on the way oil shale development could and should proceed.
12
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REGULATORY ACTIVITIES
EPA is responsible for various regulatory activities which affect the
construction and operation of oil shale facilities. Enabling legislation
and implementing regulations in the form of the Clean Air Act Amendments of
1977 (P.L. 95-95), the Clean Water Act Amendments of 1977 (P.L. 95-217), the
Safe Drinking Water Act of 1974 (P. L. 93- 523), the Resource Conservation arid
Recovery Act of 1976 (P.L. 94-580), the Toxic Substances Control Act of 1976
(P.L. 94-469), and to a lesser extent the Noise Control Act of 1972 (P.L.
92—574) and the Federal Insecticide, Fungicide, and Rodenticide Act of 1975
(P.L. 94-140) establish the regulatory framework through which EPA operates.
Of course, the National Environmental Policy Act of 1969, (P.L. 91-190) is
also a significant piece of environmental legislation.
Under the Clean Air Act oil shale developers must (1) employ Best
Available Control Technology (BACT), (2) insure that National Ambient Air
Quality Standards (NAAQS) are not violated, (3) not cause Prevention of
Significant Deterioration (PSD) ambient air quality increments to be vio-
lated, (4) not significantly degrade visibility in Class I areas nd
(5) perhaps obtain one year of baseline data prior to applying for a PSD
permit to construct and operate. Region VIII has issued PSD permits for two
developers (C-a and C-b), has proposed a permit for a commercial scale
facility (Colony), and has received applications and/or letters requesting
applicability determinations from seven other developers (Union, Paraho,
TOSCO, Equity, Geokinetics, Occidental and DOE). BACT has been defined in
the form of allowable emissions iimits and control device operational
characteristics.
The Clean Water Act contains requirements in Sections 301 and 404 for
potential permits for an oil shale developer. A (NPDES) permit must be
obtained under requirements of Section 402 if water is discharged to a
navigable stream. A timetable for meeting the BPT and BAT effluent limita-
tions was defined in the Clean Water Act by Congress (Figure 3). Specific
effluent guidelines have not been promulgated for oil shale facilities.
NPDES limits on core drilling, pump test activities and the initial retort-
ing phase have been established by the state and EPA. A Section 404 permit
must be issued by the Army Corps of Engineers and concurred upon by EPA if
any dredge and fill operations take place in a navigable stream.
Underground injection control (UIC) regulations to be promulgated under
the Safe Drinking Water Act govern the injection or reinjection of any
fluids. Permits will probably be required for in situ operations and for
mine dewatering reinjection. The State of Colorado requires reinjection
permits under existing regulations. Monitoring and mitigation measures to
prevent the endangerment of the groundwater system will be requirements
under these UIC regulations.
The Resource Conservation and Recovery Act (RCRA) will govern the
disposal of solid wastes generated by an oil shale facility. Criteria for
the identification of hazardous wastes were proposed by EPA in December
1978. Performance standards and monitoring requirements for hazardous
13
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wastes were also proposed. Permits requiring safe disposal of hazardous
wastes will have to be obtained from EPA or a state by an oil shale devel-
oper. EPA is presently evaluating how oil shale process wastes should be
categorized within the hazardous/solid waste system.
Testing of effects, recordkeeping, reporting, and conditions for the
manufacture and handling of toxic substances will be defined for oil shale
developers under the auspices of the Toxic Substances Control Act of 1976.
An inventory of all commercially produced chemical compounds has been com-
piled and is expected to be published by June 1979. Shale oil and its
refined products are expected to be grandfathered under this system.
RESEARCH PROGRAM
EPA 1 s energy research program must be responsive to Program Office and
Regional Office regulatory needs. Increased emphasis upon oil shale
research activities within EPA occurred in the 1974-75 period with the
concurrent occurrence of several factors including (1) the organization of
the EPA Office of Energy, Minerals and Industry (OEMI); (2) the effects of
the Arab Embargo and the launching of the Federal Prototype Oil Shale Leas-
ing Program; and (3) the implementation of a congressionally mandated $100
million per year Interagency Energy/Environment Program. OEMI implements
and coordinates EPA ’s energy related environmental/industry research and
development efforts and also serves as the overall manager of the comprehen-
sive Interagency Energy/Environment Research and Development Program. This
program has established a mechanism to plan, coordinate, and fund research
and development for clean energy use and pollution control technology activ-
ities within the 17 participating governmental agencies. Since the states
in EPA’s Region VIII contain major energy resources, including oil shale.
the Region VIII Office works very closely with OEMI to plan and utilize the
results from the R&D Energy Program.
The Research Program has been organized into five major categories.
Figures 4 and 5 list for 1978 fiscal year, budgets for Energy-Related Pro-
cesses and Effects, Processing, Overall Assessments, Extraction and Han-
dling, and End Use. The energy-related processes and effects category has
four significant subdivisions: health effects, ecological effects, measure-
ment and monitoring, and environmental transport studies.
The total budget in support of the EPA Oil Shale Program in Fiscal Year
(FY) 78 was $3.76 million as compared to $3.14 million in FY 77. Although
the funding by category for FY 1979 is presently not available, the magni-
tude of the effort is currently only slightly larger than for FY 1978. An
influx of funds into the program could be expected, however, if the commer-
cialization of our nation’s oil shale reserves is given primary importance
in the National Energy Plan-Il. The agencies participating in this program
include: the Department of Energy, U.S. Geological Survey, National Bureau
of Standards, U.S. Department of Agriculture, the Department of Navy, and
the National Institute of Environmental Health Sciences.
14
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Within EPA, 10 separate laboratories conduct or contract oil shale-
related environmental studies. The Office of Energy, Minerals and Industry.
Headquarters, acts as coordinater for the Interagency Program, but also
contracted work in the area of overall assessments. OEMI’s Industrial and
Environmental Research Laboratory in Cincinnati (IERL-Ci) funds and manages
research on processing, overall assessments, and extraction and handling.
Research laboratories in Ada, Oklahoma; Athens, Georgia; D iuth, Minnesota;
Las Vegas, Nevada; and Research Triangle Park, North Carolina conduct
research studies in the processes and effects area. Shale oil product (end
use) studies are managed and funded by both OEMI’s Industrial Environmental
Research Laboratory at Research Triangle Park (IERL-RTP) and the Ann Arbor
(Michigan) Emission Control Technology Division (ECTD) of the Office of Air,
Noise and Radiation.
Specific objectives of the EPA Oil Shale Program are two-fold: (1) the
program is to support the regulatory goals of the Agency (Figure 6); (2) the
research is to be directed towards ensuring that any oil shale industry to
be developed will be accomplished in the most environmentally acceptable
manner that is reasonably possible. To these ends, EPA is continuing to
assess the research needs and environmental concerns expressed by the
Department of Energy (DOE) and the oil shale industry.
Research is especially being directed to find solutions for the envi-
ronmental problems expressed by the Department’s Laramie Energy Technology
Center, and the active developers. The Office of Research and Development!
EPA is focusing on those efforts identified by the Laramie Center, since
Laramie has a key role within DOE for managing and developing the technology
of oil shale oevelopment.
OEMI is also providing the lead in the development of various oil
shale/environment documents and reports such as the “Oil Shale and the
Environment,” “Oil Shale Research Overview,” “Who’s Who in Oil Shale,”
“Program Status Report: Oil Shale,” and “Pollution Control Guidance for Oil
Shale Development.” OEMI has also formalized the interaction with industry
in the form of a forum for the purpose of transferring results of EPA—
sponsored research to industry and to catalyze cooperative research in
mutual areas of environmental interest.
In 1974, in order to insure that there is internal coordination within
EPA on oil shale research activities and needs, an intraagency Oil Shale
Work Group was formed consisting of those EPA research staff who were
engaged in the performance of oil shale research. The Regional Office is
represented in order to provide researchers with information on development
activities, liaison with developers, and regional regulatory needs.
Let me turn your attention to some of EPA’s ongoing research activi—
ties. IERL-Ci has been studying the extraction and handling of raw shale
and disposal of spent shale waste. Studies are underway to determine sur-
face stability, water movement, water quality and revegetation of spent oil
shale; to assess the environmental impact of leachates from raw mined oil
shale; to define the nature, quantity, and composition of fugitive dust from
15
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mining, hauling, crushing, and transfer activities, to quantify the trace
element composition of two cores from the Naval Oil Shale Reserve; and to
assess the air emissions from oil oil shale operations and waste sites.
Results to date on spent shale revegetation indicate that it can success-
fully be revegetated with the use of nitrogen and phosphorous fertilization,
and irrigation, coupled with intensive management. Soil cover may be
necessary in some cases. It has also been learned that boron and molybdenum
accumulate in tissues of plants grown on spent shale.
IERL-Ci is also addressing retorting environmental concerns and pollu-
tion control technology. Research has been conducted in the areas of
pollutant characterization, environmental analytical methods development,
assessment of wastewater treatment and control technology, air pollution
control for oil shale retorting, and overview of environmental problems.
Plans call for the construction and field testing of portable pilot scale
modules for air and water treatment methods to be tested on process streams.
EMSI-Las Vegas is managing an effort to design and implement an optimum
groundwater monitoring network. As a first step in this effort, a co pen-
dium of reports on processes and process effluents had been completed. A
second document addresses factors to be considered in the design of a
groundwater monitoring network. Efforts to date have been completed in the
Unitah Basin and work is progressing for a monitoring design for the
modified in situ process in the Piceance Basin. EMSL-LV has also performed
field efforts in the White River drainage of Utah and Colorado designed to
define optimum surface water physical, chemical, and biological monitoring
methods.
The ERL-Athens oil shale effort is aimed at characterization of retort
effluent waters and the development of instruments and methods to character
ize energy related wastes. Characterization of organic and inorganic
compositions of potential wastewaters and of spent shale leachates is in
progress.
The ERL-Ada program attempts to relate chemical changes in groundwater
to the characteristics of the native rock and to changes that occur due to
mining and retorting. By studying the transport process it is hoped that
environmental impacts can be predicted more accurately.
Biological and health effects are being studied by ERL-Duluth, HERL-
RIP, and ERL-Guif Breeze. The ERL-Duluth program is providing baseline
information on the aquatic environment existing prior to oil shale develop-
ment and is also performing bioassays on retort process waters from the
Paraho operation at Anvil Points. [ RI-Gulf Breeze is studying the constit-
uents of petroleum hydrocarbons which may accumulate in the marine food
chain which may eventually be consumed by man. The HERL-RIP oil shale
research program consists of a multitude of effects studies. Carcinogenic,
mutagenic, and teratogenic studies of shale oil derived products, byproducts
and wastes are being performed in both in vivo and in vitro laboratory
experiments.
16
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Air and water quality assurance programs are funded and managed by
EMSL-RTP and EMSL-Ci, respectively. EPA research staff also manage the oil
shale efforts performed by other Federal agencies involved in the inter—
agency energy/environment program. Finally, oil shale development is one of
the resources which was subjected to a Technology Assessment of Western
Energy Resource Development which is being sponsored by OEMI.
In response to many requests from oil shale developers, DOE, the
states, and the public, EPA is preparing a document entitled “Pollution
Control Guidance for Oil Shale Development.” This document attempts to
capsulize the potential environmental impacts of an oil shale facility!
industry. It also attempts to crystal ball the levels of pollution control
which may be required of oil shale developers. This joint effort among
researchers, program office staff, and regional office staff is an example
of how the EPA oil shale program is tied together.
ANSWERS/UPDATES
I would next like to address several issues which have either been
consistently discussed or have been raised in the past by the oil shale
industry, other agencies and the public. This discussion will hopefully
insure that we are all- thinking on the same wavelength.
First, the number of permits required for a facility is consistently
raised as a constraint. EPA is -investigating opportunities for permit
consolidation but I must ask the obvious question--How many of these permits
are environmentally related? I might add that we have compiled a list of
these permits and find that of the total number of permits and approvals, 15
are Federal. Second, the high background air quality levels of particulate,
hydrocarbon, and ozone have been considered and discussed at length. EPA
has responded in the form of development of rural fugitive dust policy,
consideration of revocation of the hydrocarbon standard, revision of the
ozone standard and the acknowledgement of the need for special consideration
of high background rural ozone concentrations. Third, it has been argued
that the inclusion of fugitive dust from oil shale activities is not con-
sistent with the intent of PSD. The promulgated PSD regulations do not
require consideration of fugitive dust emissions in evaluating compliance
with PSD increments. Fourth, concern has been iaised over the level of the
proposed NSPS for electric utility facilities combusting oil shale derived
products. It must be made perfectly clear that these standards will apply
only to those facilities which sell more than one-third of their produced
electricity and have a unit capacity of greater than 250 million Btu per
hour. SO 2 emissions will be limited to a floor of 0.2 to a ceiling of 1.2
pounds per million Btu coupled with an 85 percent reduction. An option of
an 80 percent reduction is being considered for use of synthetic fuels. The
particulate limit will be set at 0.03 pounds per million Btu coupled with a
99 percent reduction. However, the percent reduction does not apply to
liquid or gaseous fuels. The NO limit for synthetic oils or gas will be
0.5 pounds per million Btu. ConveXhtional oil and gas limits are 0.3 and 0.2
respectively. Emission rates may be averaged over a 24-hour period. Fur-
ther, I should add that one of the oil shale developer’s PSD applications
would indicate compliance with the 9as-fired NSPS.
17
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A fifth issue involves the Resource Conservation and Recovery Act. EPA
is evaluating how high volume wastes should be addressed based upon comments
received from the oil shale industry at the EPA public hearings. A sixth
issue involves the PSU Class I/Class II designations and the necessity for
redesignation. It is EPA’s feeling the the development of a well control-
led, environmentally sound facility should be able to exist within the
constraints of a Class II designation. If Class III is needed it should not
be needed until the oil shale industry becomes very mature.
Finally, uncertainties in the regulatory framework have been discussed.
I would acknowledge that there are instances to support these statements.
However, it is EPA’s firm belief that environmental requirements are not a
show-stopper for small modules or even the first couple of commercial facil-
i ties.
UNANSWERED ENVIRONMENTAL ISSUES
Mining and conversion of oil shale will degrade air quality, will
consume precious water resources, may degrade surface and/or groundwater
quality, will create solid and hazardous wastes to be disposed of properly,
and will create significant population growth in a predominantly rural
setting which translates into potential social and economic problems. That
these things will occur is a given . . . the question is the magnitude and
the significance of the occurrence. Key questions such as the following
exist.
1. How much groundwater will be intercepted during mining?
2. What will the quality of potential discharges be?
3. Can groundwater quality be protected during and after in situ
retorti ng?
4. Can processed shale be disposed of properly without degrading
ground or surface water quality?
5. Will revegetation of processed shale be successful over the
long term?
6. What are the concentrations of various sulfur species in
retort offgas streams?
7. What will be the air quality and visibility impact on the
Flat Tops Wilderness Area (nearest Class I area)?
8. What are the expected trace element concentrations in air,
water, and solid waste residual streams?
9. Is the conventional pollution control technology directly
applicable to oil shale residuals? Is it as effective?
18
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10. What is the expected population growth associated with the
development of an oil shale industry?
Answers to the above questions (and perhaps other questions not yet
posed) will in part determine the ability of individual plants and of an oil
shale industry to be compatible with the desired environment for oil shale
country.
Answers to some of the above questions may be partially answered by
theoretical research work and limited-scope field investigations in the
absence of any oil shale facilities. Answers to the remaining questions
will necessarily be developed through rigorous testing programs and data
analyses performed on facilities representative of commercial size.
CONCLUSIONS
EPA recognizes that the development of the oil shale resources must
play a role in satisfying the Nation’s energy appetite. We are acutely
cognizant of the need to accommodate national energy needs within a sound
and reasonable environmental framework. We also recognize that there are
uncertainties in air emissions, water quantity/quality information, solid
waste characteristics, etc. EPA has and continues to acknowledge these
uncertainties in the form of flexibility in permits granted to date. How-
ever, these uncertainties and unanswered questions coupled with our legisla-
tive mandates dictate that we take a position against the implementation of
a large, e.g., 300,000 to 500,000 bpd industry until these uncertainties are
resolved. An EPA preferred development option would be for the industry to
construct and operate commercial-scale modules of different surface and in
situ retort technologies as a first step. A second step would be the opera-
tion of a few (two or three) commercial facilities in order to answer
remaining environmental questions and to assess cumulative impacts.
Representative mining rates and methods should be evaluated. A maximum of
150,000 bpd should be developed and evaluated prior to Federal and state
decisions being made which would allow or promote additional industry
growth. A Prototype Leasing Program is a well designed program which should
proceed to completion before additional leasing is proposed. EPA is not
receptive to nor supportive of any plans or incentives which would encourage
the rapid development of a large industry. There are too many environmental
uncertainties associated with oil shale development to permit this magnitude
of development.
19
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FIGURE 1 FIGURE 2
EPA LEGISLATIVE MANDATES
CLEAN AIR ACT PL 95-95
AMENDMENTS OF 1977
N [ TES
CLEAN WATER ACT PL 95-217
AMENDMENTS OF 1977
SAFE DRINKING WATER PL 93-523 - . ..
AIR-
ACT OF 1974
RESOURCE CONSERVATiON PL 94-580
WATER-
& RECOVERY ACT OF 1976 -
TOXIC SUBSTANCES PL 94-469 - SOLID WASTES
CONTROL ACT OF 1976
RADIATION.
NOISE CONTROL PL 92-574
ACT OF 1972 .
NOISES
FEDERAL INSECTICIDE, PL 94-140
FUNGICIDE, 8 RODENTICIDE
ACT OF 1975
EFFLUENT LIMITATIONS
JULY 1,1977 BEST PRACTICABLE
FY ‘78 OIL SHALE TECHNOLOGY
FUNDING SUMMARY JULY 1,1984 BESTAVAILABLE
TECHNOLOGY FOR
EPA/INTERAGENCY TOXIC POLLUTANTS
BEST CONVENTIONAL
TECHNOLOGY FOR
ENERGY RELATED $ 1719 k POLLUTANTS SUCH AS
PROCESSES 8 EFFECTS BOO, TSS, PH, FECAL
COLIFORM, 8 THERMAL
PROCESSING 1360
THREE YEARS BEST AVAILABLE
OVERALL ASSESSMENTS 331 AFTER PRO- TECHNOLOGY FOR ALL
MULGATION OTHER IDENTIFIED
EXTRACTION 8 280 OF SPECIFIC POLLUTANTS
HANDLING
EFFLUENT
END USE 65 LIMITATION BUT
NOT LATER
THAN
TOTAL $ 3755k
JULY 1, 1987
FIGURE 4 FIGURE 3
20
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FY 1978
FIGURE
RESEARCH
L
U U U U U U I U U U U U U U U
FIGURE 6
5
TOTAL:
3755
U
UI U U U
. 1L!tEGULATIONS
21
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QUALITY ASSURANCE AS IMPOSED BY
FEDERAL, STATE, AND LOCAL REGULATIONS
Mr. Reed L. Clayson
Dr. Harry E. McCarthy
Science Applications, Inc.
1546 Cole Boulevard, Suite 210
Golden, Colorado 80401
This paper has two parts. The first part paints a bleak picture of oil
shale quality assurance as imposed by Federal, state, and local government.
Many or most of you undoubtedly have first-hand experience with the problems
I will enumerate. The point of view in this assessment is that of a systems
analyst charged with analyzing the total regulatory structure and reducing
it to a system suitable for computer analysis.
The second part of the paper is more optimistic and timely. It de-
scribes efforts now underway to make the regulatory structure in the oil
shale region more tractable, understandable, and efficient. All of us can
contribute to this effort, and my paper concludes with an outline of the
improvement program and its potential relationship to the oil shale commun-
i ty.
Since this is a gathering of scientists and engineers, one can infer
that most of those present believe that the universe is one of law and
order, and that man is capable of understanding this law and order. We all
plan our lives to a large degree upon the assumption of this orderliness in
nature, and we would like to plan our business activities upon an assumption
that success is the predictable outcome of intelligent planning, creativity,
and dedication.
It is strange that we cherish orderliness in nature, yet tolerate or
even foster disorderliness in that which we jointly create: our culture.
This paper attempts to demonstrate that all those concerned with oil shale
are in danger of creating one of the most disorderly situations imaginable.
If you will permit me to call something vitally important a game, then I can
say that we may be structuring a game in which the greater one’s planning.,
creativity, and dedication, the greater one’s losses; the greater one’s
dedication to success, the greater one’s risk of failure.
As an example, the call for papers to this symposium notes that the
“U.S. Environmental Protection Agency has the responsibility to ensure that
development of this (oil shale) resource proceeds in an environmentally
acceptable manner.” This statement has all the elements of orderliness.
First, ‘it lists a single agency, clothes that agency with sole responsibil-
22
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ity for enforcement of a major rule of the game, and implies the objective
or goal of the game. The agency, of course, is EPA, the implied objective
is that “development of the oil shale resource proceed,” .and the rule which
EPA is to enforce is that the players must stay within the environmental
bounds. We note that development of the oil shale resource means that we
need to make the transition from small-scale pilot programs, which are
capable of yielding a limited amount of data, to a reasonably full-scale
commercialization. We consider the benefits attainable through concerted
efforts and healthy competition, and we conclude that there will be several
teams, each hoping to score the most goals through intelligent planning.
creativity, and dedication.
So far, so good. We have a goal, we have players, and we have a ref—
eree. Also, we know that there are supposed to be environmental bounds.
But how are they set? Referring again to the call for papers to this sympo-
sium, we take somewhat out of context the statement that “Quality assurance
(is) the critical review and acceptance of applicable methods and standards
by a peer review system.” This definition leaves room for us to talk about
quality assurance as the rules of the game, and to infer that ideally these
rules should be clearly established by our peers, that is, people who have
equal standing with us in rank, quality, or accomplishment.
This definition of quality assurance, while much broader than alterna-
tive definitions concerned only with the reliability of monitoring data, is
consistent with the role outlined for EPA, and will be used throughout this
paper.
All of this can be summarized as shown in Figure 1. One can, visualize
that dedicated players participating in such a game would probably score
some goals, providing the bounds were not made so narrow or irregular as to
make the game physically impossible to play.
Unfortunately, as we start to examine how the game is really set up, we
begin to wonder whether we’ve missed the real intent of the game. Our
company, Science Applications, Inc., has been making a systems analysis of
the oil shale regulatory system at the Federal, state, and county level.
We’ve reached a midway point at which we can comment upon how the real game
varies from the ideal game.
Figure 2 can serve as a point of departure for visualizing the regula-
tory system. This figure is an abstract of a 6 feet by 4 feet generic oil
shale permitting chart developed during Phase I of our study. Each circle
or node on the chart represents a permit, license, or approval involved in
the life cycle of a typical oil shale project. Each diamond is a decision
point which helps to establish the paths to be followed in a given case.
So far, we have referred only to EPA as the referee for our oil shale
game. However, if one counts the number of cognizant or lead agencies for
an oil shale development in Colorado, one finds that there are 56 referees
in the generic case. These are mostly at the Federal and state level;
23
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Figure 1
“ IDEAL” OIL SHALE DEVELOPMENT
PLAYERS
OIL SHALE
DEVELOPMENT
24
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Figure 2
SIMPLiFIED REPRESENTATION OF GENERIC OIL SHALE PERMITTING PROCESS
U I
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because this is a generic flow chart, it is difficult to show the county
referees in proper fashion.
This impressive array of referees does not line up together at the
start of the game. EPA is highly visible, but some of the others are not.
It is rather like a game in which new referees continually materialize as
the contest proceeds.
The game is unusual in its treatment of fouls or penalties. In ordi-
nary games, the officials confer in the case of suspected fouls, and in most
games a team is allowed more than a single foul. One may be penalized on a
disputable call by the referees, but the game is structured so that play can
continue as long as the teams pay their penalties and give evidence of
attempting to remain within bounds.
In contrast, in the regulatory game, anyone with a whistle can stop
play indefinitely. The call may be controversial, and all the referees but
one may wish the game to continue, but the oil shale game stops until the
dissenting referee whistles the team back into play. It is difficult to say
how, many “sudden stoppers” there are in a complete oil shale game, but each
cognizant agency has one or more.
Another significant feature of the oil shale game is its variability
with time. We estimate that the entries in our oil shale data base will
have a half-life of 10 years or less. A complete league game, by which I
mean one which goes the full commercialization route, is likely to last
beyond 10 years. Now, in most games, rule changes are imposed between
games. Conversely, in our oil shale game a team may be sidelined as the
result of decisions that were perfectly legal when they were made. The
results of this type of game are likely to include continual revisions in
strategy and a number of costly retrof its. Under such circumstances, it is
possible that the team which pushes ahead most aggressively, without waiting
for the dust to settle, will suffer the heaviest penalties. The contrast
between this “new game” and the ideal game is shown in Figure 3.
Another characteristic of successful games is that the referees are as
inconspicuous and inobtrusive as possible. Their duties are well-defined.
Where their authority or responsibilities overlap, they hold conferences as
necessary and present a clear ruling to the players.
Again, these characteristics are not typical of the oil shale game. In
our analysis, we classified permits by the type of environmental concern
being protected, for example, air quality, water quality, or cultural
values. Here, we reasoned that each government agency has one or more areas
of responsibility and competence, and that redundancy of agencies should
decline or disappear when classification of permits was introduced. How-
ever, this assumption proved false. Taking water quality as an example, we
found that the 30 permits, licenses, and approvals in this path were admin-
istered by 14 separate agencies.
26
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PLAYERS
OIL SHALE
DEVELOPMENT
‘ I i
0
r
U,
z
r
rn
0
rn
I-
0
-V
Ill
z
—4
0
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OIL SHALE
DEVELOPMENT
-------
This early in our study, we lack a reliable quantitative measure of
redundancy in data requirements, but it is apparent that considerable
redundancy exists. In some instances, the developer is required to repack-
age and resubmit the same basic information over and over again, as each
referee in turn required evidence that the player is within bounds. This
process has pitfalls for the player. He may design to a particular, known
requirement or standard, and submit evidence of compliance, only to discover
later that there is another, more stringent requirement imposed or adminis-
tered by another agency.
Figure 4 summarizes the perceived problems in the existing regulatory
system: a multiplicity of agencies involved in the process, requirements
which increase with the passage of time, redundancies in data requirements,
and variable interpretations of statutes and regulations. Many good reasons
could be cited for each of the apparent defects in the system. However,
from the standpoint of systems analysis, the net impact of the system is of
primary concern. In this respect, the oil shale regulatory process reminds
one of a question once posed by Dr. Samuel Johnson--
“Consider, sir, how should you like, though conscious of your
innocence, to be tried before a jury for a capital crime, once a
week.”
That approximates the net effect of a multitude of agencies, each with
life-or-death authority over a given project, and with diverse and sometimes
changing viewpoints regarding what is right and what is legal.
To summarize these findings, it would appear that a careful observer,
perhaps newly deplaned or desaucered from Mars, might study the situation
and decide that our oil shale regulatory game was cleverly designed to
conceal its real purpose: while ostensibly designed to insure that devel-
opment of our oil shale resource proceeds in an environmentally acceptable
manner, its real intent is to ensure that development of our oil shale
resource is postponed indefinitely. We, however, conclude that the men from
Mars are wrong. The ostensible intent equals the real intent, but the game
is so poorly defined that its real goal is in jeopardy.
How do we restructure the game? First, it seems clear that no single
citizen or organization has the knowledge, skill, or authority to define or
institute the needed changes. Oil shale development affects all levels of
society, if only because we all use energy, buy from and sell to each other,
and exist in a common physical environment. We have painstakingly set up a
system of laws and a government of checks and balances, and this system has
served us well. In such a society, we as professionals must avoid the traps
of indolence and arrogance. We must attempt to raise the level of debate,
and place it upon an objective plane. There is need for a concerted effort
to do the following things.
1. State the options and objectives, in terms of what the devel-
opment or abandonment of oil shale can mean to each repre-
sentative group.
28
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Figure 4
0 SUMMARY OF PERMITTING PROBLEMS
Multiplicity of Agencle>
\ COE
\ Treasury
\CO« | FCD
MLRB /«*-
\ OCX |puclnor« A?"
\ COC [zoning/
Develope
Requirements Change
With Time
Start Mid End
Point Point Point
Set Set Set
Redundancies in Data
Requirementa
Figure 5
INTERACTIVE CLASSIFICATION OF PROJECT
29
-------
2. Define the sense of the regulatory system, including classi-
fication of all system elements as complementary, redundant,
or conflicting.
3. Define the likely impact of each element of the regulatory
system upon the energy objectives and the sense of the regu-
latory system.
4. Design a spectrum of improved regulatory systems, ranging
from one which fulfilled all senses of the regulatory system
at minimum cost to the oil shale objectives to one which
complied with all objectives of the oil shale development
with minimum adverse impact upon the oil shale environment.
The Department of Energy, with the cooperation of many other organiza-
tions, is sponsoring the development of an automated information system
which could facilitate these tasks. This information system, known as
PERMISSO, will contain a data base of information on all permits, licenses,
and approvals considered to be relevant to oil shale development in
Colorado, Utah, and Wyoming.
PERMISSO will be an interactive or conversational system, with online
response to requests for information. Figure 5 illustrates the interactive
development of the typical input scenario. The system leads the user
through a sequence which identifies the parameters which define the permit
subset. The user can specify more than one scenario, and he can also spec—
ify the level of output data.
At the highest output level, the user obtains summaries of the number
of agencies involved at each stage of the development, operation, and post-
operation process, together with the nominal or average time requirements
associated with each stage of the permitting process. At the next level, he
obtains time-phased flow diagrams relevant to his operation. At the detail-
ed level, he finds suu iaries of each permit or approval considered pertinent
to his scenario, plus detailed lists of the technical data requirements
imposed by the regulatory system. The existence of PERMISSO will make it
relatively easy to discover redundancies and inconsistencies in the regula-
tory process, and to predict the impact of potential changes in the system.
Many people in government and industry have contributed information
needed for the design of the system. The States of Colorado, Utah, and
Wyoming have expressed their desire to improve the regulatory structure in
their areas, and plans for using PERMISSO as a tool in a comprehensive
review process are being formulated. Federal and local agencies are expect—
ed to participate in these activities.
Thus, there is a widespread desire to improve the imposed quality
control structure, and tools are being developed to facilitate such improve-
ment. My hope is that all members of the oil shale community will be equip-
ped to make a vital contribution to the decision process as it relates to
energy and the environment. Our analyses suggest that each developer’s
30
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quality assurance program must be consistent with the total regulatory
structure, and that the regulatory structure must be optimized with respect
to human needs. Current world events suggest that we can no longer afford
the luxuries of confusion and inefficiency in our control of energy develop-
ment.
31
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SAMPLING DESIGN FOR BASELINE STUDIES OF
THE COLORADO OIL SHALE REGION
Ronald W. Kiusman, Charles 0. Ringrose,
Robert J. Candito, and Bruce Zuccaro
Department of Chemistry-Geochemistry
Colorado School of Mines
Golden, Colorado 80401
ABSTRACT
Sampling of large volumes of heterogeneous material for trace element
studies requires considerable care in sample design. The trace element
baseline studies of the oil shale region are used as an example of the
problems encountered in sampling soils, stream sediments, and plants over
relatively large areas.
Hierarchial or nested analysis of variance is being used in sampling
surficial materials in the Colorado portion of the oil shale area. This
technique permits sampling in an economical way that will reveal trends in
the baseline, if present, that are not an artifact of the sampling. Natural
materials such as soils and stream sediments (in an area of horizontal
sedimentary rocks) tend to be relatively heterogeneous on a small-scale, but
can be homogeneous on a large-scale. The analysis of variance technique
allows the determination of the geographic scale where the bulk of the
natural variance occurs. This in turn permits sampling at an interval that
makes efficient baselines. The elements of most concern are those which are
geochemically mobile under the alkaline conditions found in the oil shale
region or volatile under the reducing conditions of retorting.
In the initial study of soils over the Piceance Creek Basin, natural.
large-scale trends were found for Zn, Li, Fe, Cu, B, Ca, Mg, and a few other
elements of lesser importance. Stream sediments exhibit significant varia-
tion between drainages and at an extremely small-scale.
Intermediate scale analysis of variance studies and grid studies were
done on areas surrounding oil shale Tracts C-a and C-b. Detectable geo-
chemical trends were observed for Li, Mo, B, As, and organic carbon in soils
and Mo in Big sage on tract C-a and vicinity. The natural trends are due to
subtle geologic variations and would be expected based upon outcrops of the
uppermost portion of the Parachute Creek member in the western part of the
area. Tract C-b is entirely on the overlying Uinta formation and natural
trends in the area are weaker or nonexistent. Weak trends exist for Li and
pH in soil and for B and Mo in Big sage.
32
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INTRODUCTION
The error in monitoring or determining the composition of a natural
system can be divided into several components. In the simplest cases these
might be, sampling location and sampling procedure error, measurement or
analytical error, interpretative and manipulative error. Most attention is
generally directed toward the measurement systems and their accuracy and
precision. This work will be devoted to the sampling problems.
Study of physically large systems, heterogeneous systems, or systems
that exhibit considerable variation with time present special sampling
problems. In general, sampling and sampling location errors can be expected
to be larger than the other contributions to total error in systems of this
type. Frequently, the least amount of effort is devoted to reducing this
component of the error.
The chemistry of surface soils, plants, and stream sediments of the
Colorado oil shale region are being studied to establish baseline concentra-
tions. This is a case of quantifying the composition of several physicafly
large systems of unknown homogeneity. For the top 1cm of soil this is a
target population of approximately 10’ 4 g spread over 5,000 sq km. The
description of this material must be in a manner that represents an effi-
cient use of field and laboratory resources.
For purposes of this study, a baseline is defined to be a reference
that not only describes the mean and limits of concentration inherent in
nature, but also quantifies the geographic scale of variability. An effec-
tive way of expressing this baseline is with a geochemical map. If the area
is homogeneous this is an excellent means. If the variability is local, too
many samples will be required to develop a stable or reproducible geochemi-
cal map. In this case, a mean and expected range is a more appropriate way
of expressing the baseline. Constructing a contour map when the majority of
the variance is at local geographic scales results in a map that appears
satisfactory but is an artifact of the samples and may not be reproducible.
REGIONAL SOILS STUDY
The sampling design for the study of the soils in the Colorado oil
shale area or Piceance Creek Basin is a partially unbalanced nested or
hierarchial analysis of variance design described by Miesch of the U.S.
Geological Survey. 1 ’ 2 The regional study was considered reconnaissance in
nature, with objectives of ascertaining the general chemical character of
the soils, regional trends if any, and developing guidelines for sampling
design in more localized studies. 3 Figure 1 is a simplified geologic map of
the area, showing the locations of Tracts C-a and C-b and the major drain-
ages of the area.
The initial sampling design, as used in the field, was balanced.
Within each of 36 townships (36 square miles each), two sections (1 square
mile each) were chosen at random. Within each section, two samples of
surface soil were collected 100 meters apart (Figure 2). The soil develop-
33
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Colorado
I5e
Tu [ Uinta Fm.
Tg ] Green River
Twl Wosotch Fm.
IO
15 miles
Figure 1. Simplified Geologic Map of the
Colorado Portion of the Oil Shale Area.
34
-------
39"30
109°
108°30'
tupartownship
township
1* .1
/-4»»
n
i i,
i »'
1
• « *
4 •<
samples
• ridge top
• valley bo! torn
Figure 2. Index Map (top) Showing Location of Study Area,
Lease Tracts C-a and C-b and Soil Sampling Localities. Map (bottom)
Showing Soil Sampling Design and Number of Samples at Each Locality.
35
-------
ment and plant ecosystem exhibit substantial physical differences between
the relatively flat ridge tops and flat valley floors. It was expected that
chemical differences might also be observed, so this feature was incorpo-
rated into the design. All four samples within a given township were
collected either from ridge tops or valley bottoms. Ridge top and valley
sampling alternated according to a checkerboard design. Four townships were
grouped into “supertowaships” (Figure 2). By randomly eliminating one
sample from one section in each township, an unbalanced design at the sample
level resulted in a reduction of analytical load by 25 percent. These
samples were analyzed by the U.S. Geological Survey. Thirty-two randomly
selected samples of the 108 used in this design were replicated in analysis.
Soil samples were a composite of the top 1 inch taken over an area at
least 10-meters in radius. They were sieved through a 4 mesh stainless
steel sieve and stored in stream sediment bags. Stream sediments from the
regional study were collected at one location from a sand bar away from
overhanging banks. In the more localized studies, stream sediments were
composited over at least lOm of stream channel. All samples were field
sieved to -4 mesh and stored in stream sediment bags. The samples were
split to 12-15g and ground to -200 mesh in tungsten carbide for trace ele-
ment analysis.
The final design consists of six levels. Level 1 is physiographic and
will determine if there are geochemical differences between soils on ridge
tops and those in valley bottoms; level 2 was designed to examine geochemi-
cal differences at geographic scales greater than 19km (between supertown—
ships); level 3 at scales from 3 to 19km (between townships); level 4 at
scales from 0.1-3km (between samples); level 5 at a scale of lOOm (between
samples); level 6 estimates analytical precision and includes errors due to
sample inhomogeneity, sample preparation and analysis.
The model is defined as:
X.. p+a.+b..+c... +d.. +e.. +f.. (1)
ijklmn i ijk ijkl ijklm i,jklmn
where i ‘is the mean of all the nested samples, a. is the physiographic
component, b.. is the regional component (> 19km), k is the 3-19km com-
ponent, is the 0.1-3km component, e. is tt lOOm component, and
‘ ijklmn is 3 ”t he analytical component. Th jrt tal population variance is:
(2)
x a b c d e f
and is calculated as the sample variance:
(3)
In the interpretation of the analysis of variance design, ratios of the
variances at different levels of the sampling model are examined. The
variance ratio (V) is the ratio of the variance among the localities to the
variance within them. For example, the larger the variance ratio:
36
-------
N S 2
a (4)
Dv
the more significant is the physiographic component of variance. The same
procedure can be applied to the next component of variance in order to
determine if there is a regional component of variance for any given ele-
ment.
Table 1 summarizes the distribution of the variance among the six
levels for each of 37 elements. it must be emphasized that this was a
reconnaissance survey and many of the determinations were by semiquantita-
tive optical emission spectrography. As a result, the analytical component
of variance is large for many elements such as Sb. In this case, the only
useful information is that the analytical technique is not adequate. Most
of the geographic variance for most elements occurs between sections (0.1 to
3km). Of the 37 elements listed in Table 1, 27 have significant variance at
the section level (Al, Ca, Fe, Mg, K, Si, Na, Ti, As, B, Ba, Be, Ca, Cr, Cu,
Ga, Hg, Li, Mo, Pb, Rb, Sc, Sr, V, Y, Yb, and Zn).
Only 10 elements (Al, K, Si, Na, Ti, F, Li, Ni, Zn and total C) have
significant variance components at the sample level (100-meter distance) and
only five elements (Ca, Na, Hg, Sr, and total C) have significant variance
components at the township level (3-19km). More than one-third of the
elements listed in Table 1 (14 of 37) have significant variability between
supertownships (> 19km). These 14 elements include Ca, Fe, Mg, Si, Ti, B,
Be, Cr, Cu, Ga, Li, Y, Yb, and Zn. Although soil development and character-
istic vegetation on ridge tops visibly differ from that in valley bottoms,
there were no significant elemental differences between soil samples col-
lected on ridge tops and those collected in valley bottoms. This means that
the ridge top and valley bottom samples can be viewed as part of the same
population. it was expected that there would be differences between ridge
tops and valley bottoms due to differences in soil development and vegeta-
tive cover.
By a quantitative examination of the distribution of the variance it
can be determined that the sampling was adequate to describe the variability
for five elements (Fe, Be, Cu, Li, and Zn) in a map form. If most of the
variance is at the upper geographic scales, a relatively limited number of
samples will produce a stable (reproducible) geochemical map. If the var-
iance is concentrated at local scales as is the case for As, only small-
scale sampling will allow a map representation of the As concentrations in
soils. In this case, sampling and analytical costs restrict the description
of the basinwide data to a mean, range, and deviation.
All five elements for which stable geochemical maps can be made show
higher concentrations in the southern part of the basin and three (Cu, Li,
and Zn) exhibit well-defined trends in that direction. Concentrations of
soil Zn increase from S8ppm in the northeastern part of the basin to almost
lOOppm in the southwestern part. Figure 3 illustrates the Zn distribution.
37
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Figure 3. Regional Distribution of In (ppm) in Surface Soils in the
Piceance Creek Basin, Colorado. Values are Supertownship Means.
38
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TABLE 1. ANALYSIS OF VARIANCE OF SURFACE SOIL CHEMISTRY,
PICEANCE CREEK BASIN, COLORADO
Between
Variance Components as Percentage of Total
Variance
Between
Total
Ridge Tops
Super-
Between
Between
Between
Logarithmic
Element Variance
and
Valley Bottoms
Townships
(>19km)
Townships
(3-19km)
Sections
(0.1-3km)
Samples
(0-lOOm)
Analytical
Error
Al 0.0044 0 18 0 58* 14* 10
Ca .1596 0 28* 15* 48* 9 0
Fe .0080 0 27* 0 38* 2 33
Mg .0291 0 28* 3 57* 1 11
K .0075 2 7 0 80* 6* 5
Si .0038 0 22* 0 46* 22* 10
Na .0473 0 4 41 46* 6* 3
Ti .0064 1 28* 0 54* 9* 8
Total C .0535 1 14 26* 19 31* 10
As .0914 0 0 0 81* 6 13
B .0173 0 18* 0 57* 5 21
Ba .0313 0 0 0 35* 6 58
Be .0264 0 24* 0 37* 0 39
Co .0624 0 12 0 33* 0 55
Cr .0452 0 17* 0 37* 0 46
Cu .0821 0 28* 0 18* 1 52
F .0331 3 7 0 15 34* 41
Ga .0677 0 10* 0 59* 0 32
Ge .1378 0 0 11 0 27 62
Hg . 1631 2 10 36* 31* 4 17
Li .0363 5 23* 0 58* 10* 5
Mn .0476 0 0 8 4 0 88
Mo .0976 0 11 0 48* 7 33
Nb .1055 0 1 0 17 0 82
Ni .0694 0 10 0 24 36* 30
Pb .1029 0 6 0 39* 0 55
-------
TABLE 1. (CONT.)
Variance Components as Percentage of Total Variance
Between Between
Total Ridge Tops Super- Between Between Between
Logarithmic and Townships Townships Sections Samples Analytical
Element Variance Valley Bottoms (>19km) (3-19km) (0,1-3km) (0-lOOm) Error
Rb .0127 0 6 0 77* 6 11
Sb .1867 1 2 0 0 10 86
Sc .0491 0 5 0 37* 0 59
Se .0947 1 0 15 0 25 59
Sn .3488 0 2 0 0 33 65
Sr .0350 1 19 18 31* 5 27
V .0396 0 7 0 52* 0 41
Y .0330 0 9* 0 42* 0 49
Yb .0986 0 8* 0 38* 0 54
Zn .0083 1 31* 12 32* 19* 5
Zr .0359 2 1 0 28 2 66
-------
A more detailed discussion of the results of the first phase soil studies is
published.
REGIONAL STREAM SEDIMENTS STUDY
As in the case of the regional soils study, the initial stream sediment
studies were of a reconnaissance nature. 4 There were two components to the
regional stream sediment study; one to determine if anomalous drainages of
areas existed, and the other a hierarchial analysis of variance design to
quantify geographic variability and enable planning of the more localized
studies to follow. Only the analysis of variance portion of the work will
be described here.
An estimate of the natural variability in five major drainages was
determined using the hierarchial analysis of variance design of Miesch’’ 2
but modified to fit a one-dimensional stream channel. 4 Figure 4 illustrates
the various geographic scales in the stream channel sampling, which parti-
tion the variance in a 10km stream channel segment. Within each of these
2km segments, two 200 meter segments were picked at random, one within each
1km segment. Then each 100 meter segment. Finally within each 20m segment,
4-. 2km . — 2km
1- .
- tl+-
200m 20 0m
I÷1 m L÷ 1OOm-
-41 I4-- I k—
20m 20m
14-- lOm —4 4— lOm
-I I__s II III a In 1 1. _ A I LI I
-41k- —* 1k-
im im
Figure 4. Details of Stream Sediment Analysis of Variance Sample Design.
10km >1
41
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two samples were collected over a distance of 1 meter, one within each lOm
segment. The variance is partitioned in a manner analogous to equation (3),
but with five components, all geographic scales.
One of the hierarchial stream sediment sampling models is randomly
placed in each of the five major streams of the basin as shown in Figure 5.
The dashed lines across the streams show the predefined limits of the main
stem that will possibly be in the 10km segment to be sampled. The dashed
boxes show the actual position of the randomly placed 10km hierarchial model
in the individual stream drainages.
The streams in the Roan Plateau area (Piceance, Black Sulfur, Yellow
Creeks) dissect the Uinta sandstone. The heads of these streams flowing
easterly or westerly cut into the upper portion of the Parachute Creek
member. This moderate dissection contrasts with the extreme dissection of a
large stratigraphic interval by Roan Creek and Parachute Creek (Figures 1,
5).
The analysis of variance of the nested design for the major streams in
the Piceance Basin is summarized in Table 2 for a few elements.: From the
table two variance components stand out: the variance from 0-lOm for the
individual streams, and the variance between streams for all stream data.
The significance from 0-lOm for Mo cannot be tested because the 0-lOm scale
of variance and the analytical variance are combined in the lowest level,
but the high percentage of the variance at this lowest level is an indica-
tion that 0-lOin would be significant if a test could be made. In general,
the consistency of the significance at the 0-lOm scale may be reflection of
the power (degrees of freedom) of the sample design being concentrated at
the lower levels. Except for Zn, there are significant differences between
streams for all the constituents. A similar study done in the same area and
in Utah also brought out the significant difference between streams for many
other elements. 5 Semiquantitative optical spectrography data for B in
sediments from Roan and Black Sulfur Creeks indicate an elevated concentra-
tion (geometric mean of 44ppm) with most of the variance concentrated at
intermediate geographic scales. Summmary data published by McNeal and
others 5 gives geometric means of 6.5 and 7.8ppm, respectively for As and F
in stream sediments. The As is elevated in concentration with respect to
the earth’s crust but not with respect to an average scale. The F is sub-
stantially depleted, possibly due to solubility under the alkaline condi-
tions of the basin.
In the analysis of variance component of the regional reconnaissance
stream study a total of 80 samples were collected (16 from each of five
streams). From this rather limited sampling we can obtain a number of
relatively important pieces of information about stream sediments. A pre-
liminary estimate of means, deviation, and ranges can be obtained through
additional sampling and will improve the estimates. The most important
information is that individual streams are geochemically different and there
is a large component of variance in the 0-lOm scale. This implies that in
future sampling of stream sediments, all drainages should be sampled, they
42
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TABLE 2. GEOCHEMICAL SUMMARIES FOR STREAM SEDIMENTS
OF MAJOR CREEKS IN THE PICEANCE BASIN
(Data given in parts per million;
ponent is significantly different
an asterisk (*) indicates variance corn-
from zero at the 0.05 probability level)
Total
Log 10
Element Stream Variance
Analysis of
Percent
Logarithmic Variance
of Total Variance
Between 1-10
Streams km
lOOm lOOm
-1km -10km
0-
lOin
Anal.
Error
All
Roan
Black Sul.
Piceance
Parachute
Yellow
.0066
.0134
.0030
.0011
.0012
.0036
73* .2 0 3 17
- 0 13 0 87
— 15 1 0 76
- 0 0 21 79
- 5 4 0 91
- 38 0 4 58
**The analytical component is included in
a-appears.
the 0-lOm component where
Mo All
.0052
Roan
.0159
Black Sul.
.
.0365
Piceance
.0143
Parachute
.0044
Zn
Hg
2 3*
10 0
3 10
0 9
0 37
0 9
33*
47
37*
91*
40
48
All
Roan
Black Sul.
Piceance
Parachute
Yellow
Organic All
Carbon Roan
Black Sul
Pi ceance
Parachute
Yell ow
36 18
-. 27
- 46
- 0
- 14
- 30
66* 6*
- 8
- 38
- 11
- 0
- 1
69* 12*
- 20
- 50
-. 60
- 17
1
6
8
7
17
4
0
9
14
8
9
6
12
33
73
0
0
0
5
1
.0614
.0162
• 0375
0214
• 0333
• 0085
1831
0835
1416
.0198
.0011
.0326
0 0 20*
6 0 80*
0 13 43*
0 23 54*
6 0 61*
0 26 0
3 0
6 4
8 0
10 0
43 12.4
22 0
16*
71*
39*
30
22
76
43
-------
StJPI!IARY STATISTICS
Geometric
Mean
Geometric
Deviation
Geometric
Error
Expected
95% Range
4.2 1.64 1.09 1.6-11.1
3.5 1.28 - 2.1-5.7
2.9 1.41 1.09 1.5-5.6
4.2 1.33 - 24.0-7.4
9.5 1.14 7.3-12.3
3.3 1.34 1.8-5.9
61.0 1.20 1.03 28.6-129.0
63.8 1.28 1.08 39. 9-102.0
53.3 1.12 1.01 42.5-66.0
67.8 1.08 1.00 58.1-79.0
68.9 1.06 1.02 61.3-77.0
52.5 1.13 1.04 41.7-66.0
.29 1.67 1.12 .11-78.0
.58 1.27 1.07 .37-.92
.36 1.44 1.08 .18-.73
.23 1.35 1.09 .13-41.0
.28 1.39 1.19 .16-.49
.15 1.19 1.14 .12-.19
.19 2.47 1 .15-5. 55
1.26 1.87 1 .36-4. 41
.79 2.01 1 .20-3.19
1.02 1.30 - .60-1.72
2.54 1.07 1 2.22-2.9
.24 1.46 1 .11-.51
44
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15°
Rio Blanco
E23
Figure 5. Locations of Stream Sediment Analysis
of Variance Sample Design.
45
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can be sampled at intermediate channel distances, and the individual samples
should be a ocmposite of at least 1Dm of channel length.
TRACT C-a AND C-b STUDIES
The data from the regional soils study 3 and the regional stream sedi-
ment study 4 were used in planning the sampling for more localized studies.
Plant materials were also added in the localized studies. Stream sediments
were collected at the same time as soils and associated plants but generally
have not been analyzed because of analytical capacity and remain for future
studies. The results of the tract studies will be confined to soils and
associated plants. The plant materials sampled included: Big sage (Arte-
misia tridentata) , subspecies tridentata and wyorningensis , Indian ricegrass
( Oryzopsis hymenoides ) and Western wheatgrass ( Agropyron Smithii) .
Surface soil and plant samples were collected in a grid pattern over an
area 8 miles by 6 miles, incorporating oil shale Tract C-a and over an area
9 miles by 5 miles, incorporating oil shale Tract C-b. The C-a study was
the subject of a thesis by Candito 6 and C-b, a thesis by Zuccaro. 7 The grid
sampling interval was 1/2 mile (0.8km) which was determined as an appropri-
ate interval from the earlier study of Ringrose and others. 3
A smaller-scale analysis of variance design was done in the local
studies to confirm the results of the regional studies when applied to areas
in the 40-50 sq mile range. The design consisted of randomly selecting four
sections (1 sq mi) in the study area and collecting soils and plants anal-
ogous to Figure 6. The 1 square mile was quartered, two selected at random,
quartered again, two selected at random, quartered a third time, and two
sample sites collected 5Dm apart. The sample variance is distributed as:
5 2 =5 2 +S +S 2 +S +S 2 (5)
The levels are: variance at greater than 1.6km level, variance at the
0.4—1.6km level, 50m-O.4km level, variance between sample localities at the
SOm level, and at the lowest level, analytical variance.
The trace element data exhibit a distribution that approaches lognormal
as is generally the case for geochemical data. The data were log-
transformed which changes the distribution to normal. Consequently, geo-
metric means and deviations are calculated for the summary statistics
(Tables 3, 4, 5, 6).
The geometric deviation calculated from log-transformed data can be
adjusted for analytical error. This is particularly useful in estimating a
concentration range for a material in a large area. The adjusted geometric
deviation is calculated:
(GD)n = [ (GD) 2 — (GE)2]½ (6)
where (GD) is the normalized geometric deviation, GD is the geometric
deviation a d GE is the geometric deviation of the analytical replicates. A
46
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95 percent expected range for the materials of the sampled area can be
estimated according to Miesch:’
95% Expected Range = GM to GM(GD) 1.96 (7)
(GD) ’ .96
The variance ratio (V, analogous to equation 4) is used to determine
the significance of the regional component of variance. In the local con-
text, the highest level is between sections (>1.6 km or 1 mi) to the sum of
the variance at all lower levels. With the variance ratio, the effective
number of random samples, N per square mile, required for a reasonable
representation can be determi&ed graphically.’
The maximum permissible error variance for a balanced design (E ) is
the ratio of the sum of all the variances except the highest level t Nr:
E S 2 S 2 S 2 S 2
_b c d e
r N
r
(Q’
Figure 6. Schematic Analysis of Vaniance Design
Used in Local Soil Sampling Studies.
47
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TABLE 3. GEOMETRIC MEANS AND DEVIATIONS AND VARIANCE RESULTS FOR TRACE C-a AND VICINITY SOILS
95%
u.ber
Expected
Element Range
Geometric
Mearl*
Geometric
Deviation*
Variance of
Ratio** Nr Er E Samples
Hg(ppb) 30.0-58.0 42.0 1.18 253.0
Zn(ppm) 39.0-123.0 70.0 1.33 0.02 45.0 0.019 0.0002 253.0
Li(ppm) 9.0-40.0 20.0 1.43 0.41 3.0 0.011 0.003 253.0
B (ppm) 78.0-195.0 123.0 1.26 253.0
Mo(ppm) 0.46-4.7 1.6 1.70 0.23 4.0 0.011 0.003 253.0
As(ppm) 4.0-18.0 8.7 1.43 1.36 2.0 0.0071 0.0048 32.0
Organic
Carbon(%) 0.46-2.9 1.2 1.58 1.03 3.0 0.013 0.010 253.0
pH 7.1-8.5 7.8* 0.353* 253.0
*Arithmetic mean and standard deviation for pH (pH is a log measurement).
**If the estimated variance between sections is zero; Nr Er E 5 are not calculated.
-------
TABLE 4. GEOMETRIC MEANS, DEVIATIONS, AND VARIANCE
RESULTS FOR TRACT C-a AND VICiNITY PLANT MATERIALS
Element
and
Media
95%
Expected
Range
Geometric
Mean
Geometric
Deviation
Variance
Ratio**
Nr
Er
E
Number
of
Samples
Big sage
Zn(ppm)
B (ppm)
Mo(ppm)
0.43-11.0
21.5-42.6
0.25-1.7
2.2
30.3
0.65
2.26
1.19
1.63
0.02
0.46
0.22
45.0
2.0
5.0
0.004
0.0024
0.0089
0.001
0.0006
0.0026
32.0
249.0
243.0
Indian
ricegrass
Ha(ppb)
Zn(ppm)
B (ppm)
Mo(ppm)
1].0-52.O
0.49-18.0
3.0-32.0
0.48-2.3
24.0
2.6
10.0
1.1
1.47
2.46
1.81
1.48
0.05
0.02
0.24
60.0
45.0
4.0
0.00059
0.0004
0.0068
0.00061
0.0001
0.0017
32.0
32.0
32.0
32.0
Western
wheatgrass
Hg(ppb)
Zn(ppm)
B (ppm)
Mo(ppm)
7.5-70.0
5.1-18:0
7.7-28.0
0.54-2.4
23.0
9.6
15.0
1.2
1.75
1.38
1.38
1.45
--
--
--
--
--
--
--
--
;
--
--
25.0
25.0
25.0
**Jf the estimated variance between sections is zero;
Nr Er E 5 are not calculated:
-------
TABLE 5. GEOMETRIC MEANS, DEVIATIONS, AND VARIANCE
RESULTS FOR TRACT C-b AND VICINITY SOILS
95%
Expected
Element Range
Geometric
Mean*
Geometric
Deviation*
Variance
Ratio**
Nr
Ee
E 5
Number
of
Samples
B (ppm) 73.0-208.0
136.0
1.28
0.0
--
--
--
40.0
F (ppm) 334.0-645.0
483.0
1.17
0.43
4.0
0.001
0.0004
58.0
Li(ppm) 21.4-26.2
23.8
1.20
0.54
3.0
0.001
0.0006
242.0
Hg(ppb) 16.8-26.3
21.5
2.39
0.0
243.0
Mo(ppm) 0.0-4.02
0.91
1.56
0.24
5.0
0.047
0.002
241.0
Zn(ppm) 63.1-67.7
65.4
1.16
0.42
3.0
0.001
0.0004
242.0
Organic
Carbon(%) 0.0-6.73
1.52
2.60
0.40
4.0
0.020
0.008
243.0
pH 7.0-8.73
7.87
0.43
0.16
6.0
0.040
0.012
243.0
*Arithmetic mean and standard deviation for pH (pH is a log measurement).
**If the estimated variance between sections is zero; N, Er E 5 are not calculated.
-------
TABLE 6. GEOMETRIC MEANS, DEVIATIONS, AND VARIANCE RESULTS
FOR TRACT C-b AND VICINITY PLANT MATERIALS
Element
95%
and
Expected
Geometric
Geometric
Number
Media
Range
Mean
Deviation
of
Ratio Nr Er E 5 Samples
Big sage B (ppm) 24.7-29.6 27.1 1.22 0.13 7.0 0.001 0.0002 242.0
Mo(ppm) 0.0-3.63 0.35 1.64 0.94 3.0 0.012 0.008 242.0
F (ppm) 1.66-12.5 8.36 1.40 0.026 29.0 0.002 0.0003 36.0
01
Indian B (ppm) 2.13-17.7 9.18 1.49 1.53 2.0 0.007 0.005 57.0
ri cegrass
Mo(ppm) 0.03-2.16 0.97 1.64 2.21 2.0 1.106 0.010 55.0
F (ppm) 0.0-3.08 0.75 2.81 0.78 3.0 0.045 0.024 56.0
Western B (ppm) 3.11-14.6 8.36 1.42 0.08 10.0 0.003 0.0008 42.0
wheatgras s
Mo(pp,n) 0.28-1.16 0.52 1.39 0.38 4.0 0.004 0.001 40.0
F (ppm) 0.0-4.81 0.82 3.83 0.61 3.0 0.088 0.037 43.0
-------
The observed error variance, E . is found by:
s 2 + S 2 + S 2 + S 2
c d e (9)
Nb Nb•Nc Nb*N•Nd Nb•N•Nd•N
where N to N are the number of nested levels at each of the sublevels of
the mo l.2 ?f Er is greater than E , a reproducible map can be drawn of
the study area.
The variance mean ratio (V ) is used to determine the stability of maps
derived from the grid data. 2 iP V is equal to 1.0, gross difference can be
shown, and if V is 3.0 or grea !’er a geochemical map is quite stable and
representative o the actual distribution. Generally, the number of samples
required for a stable map is 2-4 samples per square mile. The grid sample
program for C-a was 221 samples/48 sq mi. These are actually 4.4/sq mi if
the grid were extended to infinity. The 1/2 mile grid interval was again
confirmed as a reasonable balance between need and analytical capability.
Tables 3, 4, 5, 6 contain the variance ratio (V), the required number of
samp1es per square mile for a stable map (N,,), the maximum permissible error
variance (Er) the observed error variance (E ), and the number of samples
analyzed at this point in time in the studies. S
As examples of maps derived from this data, Figures 7 and 8 are trend
surface maps of the grid samples for Mo in soils and Big sage for Tract C-a
and vicinity. Most constituents analyzed in the surface soils and sage
exhibit a regional variance component. Mercury, B, and soil pH have a
greater proportion of the variance at more local levels and in the case of
Hg, at the analytical level. When mapping soils on a basinwide scale and
computing means for supertownships (144 sq mi) it is possible to map Zn
(Figure 3), but on the local scale (48sq mi), it is not possible to map
soil Zn (N in Table 3) using individual samples collected on a 1.2 mile
grid. Fig I es 9 and 10 are hand contoured maps of the grid samples for Mo
in soils and Big sage for Tract C-b and vicinity. These figures are includ-
ed as examples of the application of the data. Additional maps for other
constituents and a more detailed discussion including the relationships to
the geology of the area. are contained in the theses by Candito 6 and
Zuccaro.
SU 4ARV
Trace element baselines are being established for surficial materials
in the Colorado oil shale region. The sampling of such large volumes of
materials dispersed over large areas presents special problems in sampling.
A two-stage sampling procedure employing hierarchial analysis of variance
has been employed to establish reliable estimates of means, deviations and
expected ranges for trace elements in soils, stream sediments, and plants of
the area. The analysis of variance technique allows the determination of a
sampling interval which allows the mapping of the distribution of trace
elements that is not an artifact of the sampling. Sample design allows the
52
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en
GO
miles
Figure 7. Mo (ppw) in Surface Soils of Tract C-a and Vicinity.
-------
yi
0
Figure 8.
MILES
Mo (ppm) in Big Sage of Tract C-a and Vicinity.
-------
Ul
en
Scot
I Miles
<-o.90 I 1
0.90-1.58
1.58-2.15
>2.!5
Figure 9. Mo (ppm) in Surface Soils of Tract Ob and Vicinity.
-------
en
en
Scale
Miles
< 0.60 [
0.60-1,01 F
i.01-1.36 IfV^J
> 1.36 ill
Figure 10. Mo (ppm) in Big Sage of Tract C-b and Vicinity.
-------
determination of reliable baselines upon which the prediction of future
impact and the measurement of actual impact can be measured.
ACKNOWLEDGEMENTS
This study is part of the environmental Trace Substances Research
Program of Colorado, under Dr. Williard Chappell of the University of
Colorado. Much of the analytical work was done under the direction of
Dr. Robert Meglen. Access to Colorado oil shale tract C-a and C-b was
facilitated by the cooperation of personnel of Project Rio Blanco and the
Occidental Oil Shale Corporation. The U.S. Department of Energy provided
support through Contract No. EY-76-S--02-4017.
REFERENCES
1. Miesch, A.T. , “Sampling Designs for Geochemical Surveys--Syllabus for a
Short Course,” U.S. Geol. Survey, Open-File Rept. 76-772, 1976, p. 117.
2. Miesch, A.T. , “Geochemical Survey of Missouri--Methods of Sampling,
Laboratory Analysis and Statistical Reduction of Data,” U.S. Geol.
Survey, Prof. Paper 954-A, 1976, p. 37.
3. Ringrose, C.D. , R.W. Klusman, and W.E. Dean, “Soil Chemistry in the
Piceance Creek Basin,” In: Geochemical Survey of the Western Energy
Regions, U.S. Geol. Survey, Open-File Rept. 76-729, 1976, pp. 101-111.
4. Ringrose, C.D., “A Geochemical Survey of Stream Sediments of the
Piceance Creek Basin, Colorado,” M.S. Thesis, Colorado School of Mines,
Golden, CO, 1976, p. 100.
5. McNeal, J.M., G.L. Feder, C.D. Ringrose, and R.W. Kiusman, “Stream
Sediment Chemistry in the Oil Shale Region,” In: Geochemical Survey of
the Western Energy Regions, U.S. Geol. Survey, Open-File Rept. 76-729,
1976, pp. 121-130.
6. Candito, R.J. , “A Geochemical Baseline Study of Surficial Materials in
the Vicinity of Oil Shale Tract C-a, Rio Blanco County, Colorado,” M.S.
Thesis, Colorado School of Mines, Golden, CD, 1977, p. 101.
7. Zuccaro, B. , “A Trace Element Survey of Surficial Materials on Colorado
Oil Shale Tract C-b and Vicinity, Rio Blanco County, Colorado, M.S.
Thesis, Colorado School of Mines, Golden, GO, 1978, p. 130.
57
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AMBIENT AIR SAMPLING AND ANALYTICAL
PROCEDURES FOR OIL SHALE DEVELOPMENT AREAS
D. C. Sheesley
Northrop Services, Inc.
for Environmental Monitoring and Support Laboratory
U.S. Environmental Protection Agency
ABSTRACT
Quality Assurance criteria are used to evaluate sampling and analytical
procedures and assess methods for measurement potential in ambient air that
s relatively clean. Precision and accuracy of sampling and analytical
procedures are used to place methods in three categories: Most reference
and equivalent or compliance methods are in category 1. Category 2 contains
those methods which are recognized in the literature and have a relatively
high frequency of use. Promising methodology is discussed as a third cate-
gory, although an indepth selection of these relatively new methods has not
been included in the procedures presented for measurement of ambient air
pollutants anticipated in oil shale development.
Ambient Air monitoring objectives are discussed by analyzing the re-
quirements of developing baseline concentrations and air quality parameters.
Standards, sampling sites, meteorology, and modeling, and selection of
procedures are seen as controlling factors in developing data in the low
concentration range of pollutants of oil shale development Areas in the
West.
(Paper presented at Symposium but not submitted for publication in the
Proceedings. For more information, contact the author.)
58
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EPA R&D EFFORTS IN THE DEVELOPMENT OF OIL SHALE
LUNCHEON ADDRESS
Dr. Steven R. Reznek
Deputy Assistant Administrator
for Energy, Minerals, and Industry
U.S. Environmental Protection Agency
Our Nation came to believe, without question, that investment of money
and labor to develop natural resources would be rewarded by a growing econo-
my. The inherent limitations to this traditional wisdom have now been
demonstrated. We know that in the short term the cost of energy--that is
the capital and labor required to produce usable energy--will increase.
Furthermore, the potential environmental problems of oil shale, coal
and nuclear energy are much greater than those of petroleum and natural gas,
and will require increased expenditures if they are to be solved.
Although we have all witnessed some of the near term economic, politi-
cal and environmental implications of the closing of the petroleum age, none
of us can forecast accurately what the future has in store. The energy
crisis may mean a protracted and gradually worsening economic recession,
lack of opportunity for our young people, and decreasing social mobility.
It may mean rapidly degrading environmental quality and exhausting our
supplies of clean air, clean water, and productive land.
On the other hand, the cost of energy may rise to the point where
widely available and environmentally benign sources will be used to meet
society’s economic and social needs.
Let me try to give you some perspective on the size of the environmen-
tal impact that a substantial oil shale industry may have. Assuming that
oil shale will yield 25 gallons of crude oil for each ton of rock refined,
we will need to process a weight of shale that is one and one-half times as
much as the weight of coal we presently mine to produce the crude oil we
currently import. If we are really to use the 600 billion barrels of crude
oil that are potentially available in the Green River shale formation, we
will have to divert enormous resources of land, capital and labor.
The major environmental problems include the large quantities of dust
from the mining operations, airborne emissions from retorting processes, and
the large amounts of spent shale.
The oil shale industry will be located in the semiarid region of the
Colorado River Basin where the demand for a limited water supply is already
59
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critical. Oil shale processing and the revegetation of spent shale will
consume large amounts of water, a critically limited resource.
There is concern that the leaching of the soluble salts will increase
the salinity of streams and groundwater. Potentially toxic runoff from
spent shale could also threaten public health.
The data on which we base these environmental concerns is limited to
pilot plant experience. As we develop full scale production, new and cer-
tainly equally significant problems will become apparent.
Control technology can minimize the environmental impact of energy
conservation processes. Regulations can prevent pollutants from causing
serious and widespread environmental degradation. Establishment of such
standards requires a constant and careful evaluation of existing technolo-
gies. Some important questions, however, must be asked: Will control
technologies reduce pollutants to acceptable levels? What are the realistic
costs of these controls, both in dollars and in energy loss? Where is the
balance between energy losses and environmental gains?
Ideally, with the potential evolution of conversion processes and their
“built-in” environmental controls, an energy conversion technology may
evolve that is both economically and environmentally sound.
In the final quarter of the 20th century the focus is shifting to the
development of a “total program.” Environmental controls are developed
concurrently with our new energy industries. The result is inherently
cleaner than our previous after-the-fact practices.
Advanced fossil fuel environmental control technology is the key tool
necessary to bring about the total program for oil shale conversion. Such a
program requires both commitment and cooperation from the industrial devel-
opers, the environmental researchers and regulatory agencies. Successful
development of environmental controls concurrent with development of energy
production processes will avoid the more costly and less efficient task of
reconfiguring the technology.
EPA AND THE FEDERAL ENERGY/ENVIRONMENT RESEARCH AND DEVELOPMENT PROGRAM
The Federal Interagency Energy/Environment Research and Development
Program is an 11 agency effort comitted to energy development with environ-
mental protection. Major program goals are to:
o Safeguard health and the environment without unduly delaying the
accelerated development and use of domestic energy resources.
o Anticipate environmental impacts of energy conversion technologies
and stimulate the development of cost-effective environmental
controls.
o Promote the transfer of energy-related environmental information.
60
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The Office of Energy, Minerals, and Industry (OEMI), within EPA’s
Office of Research and Development, plans and coordinates this Interagency
Program. Through this program, OEMI provides support to numerous other
Federal agencies including the Department of Energy (DOE) and the National
Institute for Occupational Safety and H alth (NIOSH).
FY-78 Interagency Program funding was $100 million, of which $26 mil-
lion was passed on by EPA to other Federal agencies. This funding supports
a number of energy-related programs, including characterization and monitor-
ing of pollutants, transport processes, ecological effects, health effects,
integrated assessment studies and environmental control technology.
In October 1978 the headquarters office of OEMI and its Cincinnati
Laboratory (IERL) initiated a closer working relationship between EPA and
the industrial firms interested in developing oil shale. Two meetings were
held with industry to gather ideas on the environmental and regulatory
problems that must be faced prior to bringing oil shale to production. On
January 23-24, 1979, in Denver, senior management from EPA’s Office of
Energy, Minerals, and Industry, representing the R&D effort, and Region
VIII, representing the regu’atory function met with top management from
25 companies. Management from DOE and DOE’s Area Oil Shale Office were also
in attendance. Industry heard both DOE and EPA express a positive attitude
towards the prospect for oil shale commercialization. The industry repre-
sentatives were encouraged by the idea of cooperative research.
EPA Research
EPA’s commitment to cooperative research has been to provide funding
for research on the following topics: characterization and control of
retort emissions, solid waste handling, revegetation, ambient air quality
and groundwater pollution.
Technology Transfer
EPA-IERL-CI is assembling the Pollution Control Guidance Document for
Oil Shale Development. This document will be EPA’s policy defining “good
environmental practice” at this stage of the industry’s development. Sec-
tions include sampling, analysis and monitoring of emissions, effluents, and
solid wastes; and suggested interin’ standards for air emissions, water
effluents and solid waste disposal for the major retorting processes. Also
included are discussions of state-of-the-art of oil shale development;
procedures for air monitoring; applicable Federal, state, and local laws and
regulations; analytical procedures and quality assurance manuals; and a
catalog of existing Federal, state, and locally required permits.
This Oil Shale Symposium on Sampling, Analysis and Quality Assurance is
an important part of a major effort underway to make America less dependent
on foreign sources of energy. In the years to come, the Nation must con-
serve the energy it has, harness new sources, and work to develop the energy
resources already identified, including its vast oil shale deposits. Effi-
cient, economical ways of extracting energy from these resources must be
61
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developed while, at the same time, giving full credence to the basic impera-
tive that energy resource development cannot come at the expense of the
quality of our health or of the natural environment.
Providing the pollution control guidelines that can grow with the
industry and its technology is a novel concept. This symposium focuses on
the research c ecessary tc define the environmental part of the oil shale
production processes.
We thank you for your contribution to this effort.
62
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A CONCEPTUAL MODEL FOR AN INTEGRATED ENVIRONMENTAL ANALYSIS
ON OIL SHALE TRACT C-b
P.1. Haug
Office of Planning, Inventory, and Environmental Coordination
Bureau of Land Management
3825 East Mulberry (SAU-LMP)
Fort Collins, Colorado 80524
and
G.M. Van Dyne
Department of Range Science
Colorado State University
Fort Collins, Colorado 80523
ABSTRACT
In accordance with lease stipulations requiring that system interrela-
tionships be addressed in the environmental baseline program, a conceptual
model of the oil shale Tract C-b ecosystem was developed around 2.5 years of
hydrological, meteorological, and ecological baseline data. A systematic
procedure was used to organize, classify, summarize, integrate, and synthe-
size the baseline data into categories of key ecosystem components and
processes. The operator’s detailed development plans for the oil shale were
used to identify anticipated perturbations to the ecosystem. These pertur-
bations were integrated into the conceptual model along with the key compo-
nents and processes. Volume 5 of the final report for the Environmental
Baseline Program serves six purposes: (1) it summarizes most baseline
information in nearly 500 time-series graphs that depict behavior of many
components and processes in the ecosystem measured during the 2.5-year
period; (2) it serves as a cross-reference to other volumes in the report;
(3) it develops a conceptual model of the Tract C-b ecosystem; (4) it
permits users to track potential impacts qualitatively through the ecosystem
using this conceptual model; (5) it assists users in planning a monitoring
program; and (6) it assists in planning mitigation of potential impacts.
INTRODUCTION AND BACKGROUND
This paper describes why and how a conceptual model was constructed to
integrate engineering plans for oil shale development with environmental
baseline data on Tract C-b. The model is useful for various types of envi-
ronmental analyses, such as tracing potential impacts through the ecosystem,
planning monitoring, and developing mitigation measures.
63
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The C-b Tract
Oil Shale Tract C-b is a Federal Lease Tract of approximately 5,100
acres located in the Piceance Basin. Rio Blanco County, Colorado. Develop-
ment of Tract C-b is governed by terms and conditions of the Federal Oil
Shale Prototype Leasing Program administered by the Area Oil Shale Supervi-
sor, Geological Survey, U.S. Department of the Interior. The environmental
stipulations attached to the Federal Oil Shale Lease require that a two-year
environmental baseline field study be conducted on the tract and 1-mile
surrounding area prior to oil shale development.
The study, the Tract C-b Environmental Baseline Program, was initiated
on 1 November 1974 and completed on 31 October 1976. Although this was the
official period of investigation, some data were collected as early as July
1974 and as late as November 1976.1 The total period of data collection.
therefore, was about 2.5 years. During this period, data were collected and
analyzed on surface water, groundwater, air quality, meteorology, flora and
fauna, soils, geology, and archeology.
Development of the tract is by C-b Shale Oil Venture of Ashland Oil,
Inc., and Occidental Oil Shale, Inc.. operator.
Ideal Baseline Studies
Under ideal conditions, environmental baseline field studies would be
guided at the outset by a conceptual, and possible mathematical, model that
identifies the most important ecosystem components and processes to measure.
After a thorough literature search combined, perhaps, with some field recon-
naissance, a preliminary model would be developed to identify gaps in exist-
ing data and information about key indicator variables and ecosystem stress
points.
Only then would extensive field studies be planned and implemented.
Data from these studies would feed back to the modelers, who would revise
the model and identify areas where the field program needs to be revised,
etc. Ideally, this iterative cycle between model and field studies would
continue without interruption into a monitoring and mitigation phase
throughout the life to the project.
In the case of Tract C-b, environmental baseline field studies were
begun much before the engineering plans were complete. The field studies
were conducted by experts in many disciplines, each working generally with-
out regard for interrelationships between contiguous disciplines and beyond.
Finally, after 2.5 years of baseline studies and major revisions in the
oil shale operator’s detailed development plan, the conceptual modeling
began, largely to satisfy a clause in the oil shale lease requiring that
system interrelationst ips be addressed. The results of that modeling effort
appeared as Volume 5 of the Environmental Baseline Program Final Report. 1
Much of the remainder of this paper discusses the philosophy and methods of
that conceptualization effort.
64
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SYNTHESIS OF BASELINE STUDY INFORMATION
The fundamental challenge was to portray ecosystem interrelationships
on Tract C-b, based on a 2.5-year plethora of data that had been collected
somewhere on or near the tract at different times and different locations.
The challenge was met by viewing the problem in a systems perspective and
using the tool of a dynamic model to assist in organizing ideas and infor-
mation.
Models and Modeling
Modeling means many things to many people. In its broadest sense, we
all model whenever we think: we abstract in our minds essentials from the
real world, analyze the situations, decide on the basis of our conceptual
understanding (our mental model”), and act on that decision.
More formally, modeling means documenting our abstract thinking. For
example, models can take the form of three-dimensional objects (working
models, scale models), two-dimensional representations of the three-
dimensional world (engineering drawings, topographic maps), simple narrative
statements, more abstract conceptualizations of real-world interrelation-
ships (diagrams, matrices, and equations), and computer programs that solve
those equations.
In general, a model is simply a way of organizing, classifying, sum-
marizing, integrating, and synthesizing information about the real world,
usually in order to reach some conclusions or decisions about that world.
A Systems Viewpoint
A system is a collection of objects which, by their interaction and
interdependence, form an entirety that functions in a particular way. Often
components of that entirety interact to confer on the system certain emer-
gent properties that might not readily be inferred from a study of the
component parts separately; first-, second-, and higher-order interactions
between and among components cause the system to behave uniquely as a func-
tional unit, rather than as the simple sum of all the component activities.
In other words, because of synergistic effects, the system takes on behav-
loral characteristics that transcend the characteristics of its component
parts.
Components of a system are called variables , because they vary through
time and space. Usually variables are of two types: driving variables and
state variables. Driving variables provide input to the system from out-
side; in a sense, they “drive” the system. These components are external to
the system in that they affect the ecosystem without themselves being
affected by it.
Components that influence each other through abiotic or biotic feedback
are called state variables . These components are within the system, and
their condition reflects the state of the system at any given time. State
65
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variables interact with each other sufficiently to affect each other’s
behavior.
In living systems, variables are linked to each other by flows of
matter or energy, and they interact with each other through these flows.
Certain system processes act as valves to regulate the flows: as the rate
of a process changes, the flow rate changes, and the state variable depen-
dent on that flow is affected, thus becoming an indicator of the change.
Processes are regulated by driving variables and, often through feedback, by
state variables.
The Ecosystem
In ecology, a system may be an organism, a species population, or a
total ecosystem. We were concerned with a complex of ecosystems that make
up Tract C-b and its immediate vicinity. The ecosystems on Tract C-b are
more or less naturally evolved collections of living things interacting with
each other and with their nonhiving environment to form recognizable units
characterized by unique emergent properties.
In an ecosystem, driving variables are usually natural abiotic compo-
nents. Examples are insolation and precipitation. However, they also
include certain man—influenced inputs, such as pesticides, herbicides,
grazing management, or influences deriving from such activities as oil shale
development.
State variables are chosen to describe the ecosystem of interest as
economically as possible. A state variable could be a single species of
plant, a life form (grass, forb, shrub, tree, etc.), or even all plants in
the system. Selection of state variables depends on the level of resolution
at which we want to describe the system and on the importance of each compo-
nent.
Common processes in ecosystems are photosynthesis, respiration, birth,
death, etc. Thes example processes control the flow of carbon through the
system. Other processes regulate other flows.
METHODOLOGY FOR CONCEPTUALIZATION
Our synthesis activity took place concurrently with that of the devel
opment of four other volumes summarizing 2.5 years of baseline studies on
the tract. These covered regional and temporal setting (Volume 1), meteo-
rology, air quality, and noise (Volume 2), hydrology (Volume 3), and ecology
(Volume 4).
We had several opportunities to meet with the field investigators as
they wrote and revised drafts of the above volumes. One of us (Van Dyne)
also viewed the tract, both by air and on the ground, to develop a general
impression of the system under consideration.
66
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First we attempted to define ecosystem response units of landscape on
the tract and to identify the main abiotic, autotrophic, and heterotrophic
components in each unit. Then we focused on currencies flowing through
these components. Next, we identified the physical and biological processes
responsible for these flows. Finally, we examined the factors controlling
these flows, considering those occurring naturally in the environment and
those related to potential oil shale development.
Interpretation of the baseline data and information of earlier volumes
was governed by simple modeling criteria: to identify and describe inter-
relationships within the tract s environmental system, it was necessary to
systematically organize an extremely large data base in order to minimize
loss of information, and to rationally reduce that data base to a workable
size.
A method of interactive functional aggregation was used to combine
ecosystem components, ecosystem processes, and oil shale development activi-
ties into manageable groups or classes. However, before important compo-
nents could be identified and chosen for subsequent discussion from among
innumerable driving and state variables, some selection criteria were neces-
sary.
Choice of driving variables and abiotic state variables (e.g. , soil
moisture) was governed by the importance of these variables in the baseline
studies and through discussions with principal investigators for the various
disciplines. The biotic state variables were grouped according to their
functional roles in the ecosystem (e.g., trophic level, life form). We used
a principle of inclusivity in defining biotic state variables: the objec-
tive was to include 90 to 95 percent of any trophic group, by biomass.
within the component categories. Some specifics follow.
Ecosystem Response Units (ERU )
An ecosystem response unit is a geographical, or spatial, unit that
(1) possesses common vegetative, topographic, elevational, and edaphic (or
aquatic) characteristics, and (2) responds to environmental influences more
or less uniformly throughout. Effectively, response units are considered to
be small ecosystems within the larger environmental system of the entire
tract area.
The 13 ecological communities predominant on the site were aggregated
into five ERUs, primarily on the basis of common vegetative physiognomies
and topographic locations (Table 1). The vegetation provides an excellent
link between the abiotic components and the animals. When integrated into
plant-animal-topographic response units, these functional spatial units are
considered as small ecosystems that constitute the large environmental
system of Tract C-b.
Upon this natural environmental system, two additional ERUs are being
imposed by oil shale development: upland rehabilitated sites and bottomland
rehabilitated sites.
67
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TABLE 1. AGGREGATIONS OF PLANT COMMUNITIES INTO ECOSYSTEM RESPONSE
UNITS FOR A CONCEPTUAL MODEL OF THE C-b SYSTEM j’
Plant Community!
Habitat Types
Corresponding Ecosystem
Response Unit
Subscript I
Pii on-Juniper
Upland Forest
1
Chained Rangeland
Bunchgras $
Sagebrush
Mountain Shrub
General Upland
2
Rabbi tbrush
Greas ewood
Sagebrush
Wildrye
Riparian
General Bottomland
3
Meadow
Meadow
4
Stream
Pond/Marsh
Aquatic
5
Upland Rehabilitated
6
Bottomland Rehabilitated
7
Ecosystem Components
The next major step was to identify all the state and driving variables
that might be important. For Tract C-b this required reviewing and reorga-
nizing species lists and vegetative communities into functional components
at a medium level of resolution. (The level of resolution is the degree to
which component parts are combined or separated, hence, the amount of detail
presented). This time-consuming process required interactive consultations
with disciplinary specialists to determine which species could be aggregated
into which groups in a reasonable way.
In addition, different ecosystem components occur in different ERU5;
where possible, data were used to associate specific components with ERUs.
In some cases, as with driving variables, a component occurs in all ERUs.
From an original list of 47 possible driving variables on the tract, 15
were chosen for the model. They are classified as climatic, resource man-
agement, or oil shale development variables. Natural driving variables of
68
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the system include precipitation, air temperature, wind, and solar radia-
tion. Management variables are forest cutting and livestock grazing.
Emissions and other disturbances are included as oil shale development
variables.
The 240 species of vascular plants on the site were classified first by
life form: trees, shrubs, annual grasses and forbs, perennial graminoids,
and perennial forbs. Similarly, the 168 vertebrate animals on the tract
were aggregated first by taxonomic category (shrews, bats, ungulates, etc.),
then more broadly (small, medium-sized, and large mammals, waterfowl, etc.),
and again by function (omnivore, carnivore, herbivore, etc.).
The final aggregate grouping of state variables is presented in Table
2. Subsystems are based on flow currencies, which are explained below.
Acronyms are used for rapid identification in model diagrams. The J. sub-
scripts refer to ecosystem response units in which a variable is found
(Table 1); the J subscripts are the life forms of the plants and animals in
the model.
Time Dynamics
A primary concern of the ecosystem analyst is the behavior of system
components and processes through time. By tracking time-variant behavior of
several related components and processes it is often possible to infer
cause-effect relationships through statistical analysis. Understanding such
relationships is fundamental to sound modeling.
For this reason, virtually all data for driving variables and state
variables from Tract C-b were plotted, wherever possible, against a uniform
2.5-year time scale. Unfortunately, time and funding did not permit rigor-
ous statistical analyses, but even when statistical correlations are not
determined, information about how a given ecosystem component or process
varies over time is valuable.
If we can assume that a time-variant annual curve somewhat represents
the norm for a given variable, we can then hypothesize certain interrela-
tionships that affect the behavior of that curve. To this end, nearly 500
time-series graphs of driving variables, state variables, and ecosystem
processes were drawn in a standard format.
Ecosystem Flows
One of the main reasons for identifying ecosystem components by their
function, rather than by taxonomic classification, is that a functional
classification helps us understand the interrelationships in the system, and
thus how the system works. For example, the movement of carbon is one way
of tracing interrelationships among the abiotic and biotic components of the
system. Carbon, then, is considered a flow currency; i.e., it is a common
element that flows between ecosystem components as a direct result of cer-
tain processes associated with the functioning of those components.
69
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TABLE 2. IMPORTANT STATE VARiABLES IDENTIFIED ON TRACT C-b
The initial letter of each acronym refers to the flow
subsystem in which that variable appears. Subscripts
the text.’
currency of the
are explained in
Heat
(H)
Water
(W)
Acreage
(A)
Source
terrestrial animal numbers
aquatic vertebrate numbers
aquatic invertebrate numbers
sink
source
terrestrial animal weights
aquatic vertebrate weights
aquatic invertebrate weights
sink
Source
live shoots
live roots
standing dead
aboveground litter
belowground litter
aquatic rooted plants
aquatic unrooted plants
aquatic detritus
sink
source
soil top layer head
soil bottom layer heat
aquatic to layer heat
aquatic bottom layer heat
sink
source
surface water (snow,
top soil water
bottom soil water
groundwater layer
aquatic ice layer water
aquatic water
sink
surface area
PSS
PTA
PAV
PA I
PASS
GSS
GTA (I,
GAV (I)
GA l (I)
GSS
CSS
CVS (I,
CVR (I,
CVD (I,
CLA (I)
CLB (I)
CAR (I)
CAN (I)
CAD (I)
CSS
HSS
HST (I)
HSB (I)
HAT (I)
HAB (I)
HSS
wSS
WSI (I)
WTS (I)
WBS (I)
WGR (I)
WAI (I)
WAW (I)
WSS
ACR (I)
Subsystem
Title and Abbreviation Aggregate State Variable Name Acronym I
Limit
J
Animal Population
(P)
Animal Weight
(G)
Carbon Biomass
(C)
(I, J)
(I)
(I)
J)
J)
J)
J)
5 10
2
2
5 10
2
2
75
75
75
5
5
2
2
2
5
5
2
2
5
5
5
5
2
2
12
ice, rain)
70
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The flow rates of currencies through an ecosystem control the rate of
growth or decay of any particular ecosystem component, for it is these
currencies (carbon, phosphorus, nitrogen, etc.) that are stored or dissemi-
nated. The flows themselves, however, are simply the reflection of various
processes that actually control the rates of flow. For example, one of the
processes that controls biomass flow from generation to generation is repro-
duction.
Criteria for selecting currencies of flow were (1) broad commonality
among many system components and (2) ease of measurement. Six currencies
were used in developing the conceptual model: population numbers for var-
ious animals, weights of individual animals, biomass of plant and litter
components, temperature of soil layers, water content of various terrestrial
and aquatic subdivisions, and surface land area.
Ecosystem Processes
After the flow currencies and components were chosen, the processes
that control the flows were identified and similarly aggregated or separ 3t-
ed, according to their importance within the level of resolution specified.
The choice of these was constrained by the baseline information available
and by considerations for future monitoring.
By regulating flow rates, the physical, chemical, and biological pro-
cesses that transfer matter and energy among components of the ecosystem
account for the dynamics of the state variables. Through successive analy-
sis and synthesis, a total of 85 terrestrial and aquatic ecosystem processes
were identified as important on Tract C-b.
These processes were associated with the six flow currencies. The
currencies formed the basis for developing six conceptural subsystems (Table
2) within the model. Each subsystem tracks the flow of a particular cur-
rency among ecosystem components, and subsystems are linked to each other
conceptually by flows of information between subsystems. Figure 1 diagrams
carbon, heat, and water subsystems on the tract. Table 3 lists ecosystem
components and processes associated with each subsystem in Figure 1. Infor-
mation linkages between the heat subsystem and other components and sub-
systems are illustrated in Figure 2. Sympols are explained in Figure 3.
Oil Shale Development Influences
Because the dynamics of system variables are controlled by the process-
es, anything that affects those processes will affect the state variables
indirectly. In addition, state variables can be affected directly by cer-
tain outside influences. For example, bulldozing trees to develop a road is
a direct effect. The process of erosion is affected indirectly as bulldoz-
ing uncovers and loosens the soil. The interaction of bulldozing and
precipitation accelerates the erosion rate and thus the flow (or loss) of
soil.
71
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HEAT
SOURCE
AT
SINK
BOTTOM SOIL
.iLAYER
Figure 1. A simplified conceptual diagram of flows within the carbon,
heat, and water subsystems. 1 Carbon flows are through both
plant and animal subsystems.
:L0wGR0uND
SOIL LAYER
SURFACE
TER
0P SOIL
LAYER
ER
SINK
GROUND
WATER
72
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TABLE 3. ECOSYSTEM PROCESSES CONTROLLING FLOWS IN THE CARBONS
HEAT, AND WATER SUBSYSTEMS ON TRACT C-b’
Flow
Ecosystem
Components
Ecosystem
Processes
Currency
From
To
Process
Carbon Carbon source
Carbon source
Carbon source
Live shoots
Live shoots
Live shoots
Live shoots
Live shoots
Live roots
Live roots
Live roots
Live roots
Standing dead
Standing dead
Aboveground litter
Aboveground litter
Belowground litter
Belowground litter
Live shoots
Aboveground litter
Aboveground litter
Live roots
Standing dead
Aboveground litter
Carbon sink
Carbon sink
Live shoots
Belowground litter
Carbon sink
Carbon sink
Aboveground litter
Carbon sink
Carbon sink
Carbon sink
Carbon sink
Carbon sink
Photosynthesis
Excretion input
Dead animal input
Translocation to roots
Shoot death
Shoot shattering
Shoot respiration
Shoot grazing output
Translocation to shoots
Root death
Root respiration
Root grazing
Dead shattering
Dead grazing
Aboveground decomposition
Aboveground litter grazing
Belowground litter grazing
Belowground litter grazing
Heat source
Heat source
Top soil layer
Top soil layer
lop soil layer
Bottom soil layer
Bottom soil layer
lop soil layer
Top soil layer
Bottom soil layer
Heat sink
Heat sink
lop soil layer
Heat sink
Radiation
Convection in
Conduction down
Reradiation
Convection out
Conduction up
Conduction down
Water Water source
Surface water
Surface water
lop soil water
Top soil water
Top soil water
Bottom soil water
Bottom soil water
Groundwater layer
Surface water
Top soil water
Water sink
Bottom soil water
Water sink
Water sink
Groundwater layer
Water sink
Surface water
Precipitation
Snow melt/infiltration
Surface evaporation
Percolation
Top soil evaporation
Upper soil transpiration
Percolation
Lower soil transpiration
Spri ngf 1 ow
Heat
73
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:TOP SOIL
EVAPORATION
I
SNOW MELT/
AIR — E iiNflLTRATION
TEMP
I
WIND L 4 _JJNTERLAYER
PERCOLATION
II I
II U
LOWER
-4
‘‘E 34 1TRANSPIRATION
• I
• a
L j___4TOP SOIL
WATER
• I
• I
I - j
I- -4_____
_________________ $
: BOTTOM SOIL
• wATgj
a a a... fleas C c i
Figure 2. Partial system control diagram of heat subsystem on oil shale Tract C-b. (Modified from
Van Dyne and Haug’) Symbols are explained in Figure 3 2
HEAT
SOURCE
CONVECTION
IN
RADIATION
CONDUCTION
UP
SINK
-------
Source or sink.
Driving variable .
1 1
LI <
State variable (ecosystem component).
Process . These may be thought of as valves
that control the flows of matter or energy
through the subsystem.
Materials or energy flow . Denoted by a
solid line.
— - —_
——— —_1
Information flows . Dots and dashes repre-
sent input from driving variables. Dashes
trace the inter—subsystem influences of state
variables on processes. Dots are the intra—
subsystem feedback loops through which state
variables influence processes within their
own subsystem.
0
Takeoff points and crossings . The small
circles represent take—off points or branches
in information linkages. The other symbol
simply denotes a crossover.
Figure 3. Symbols and conventions used in system control diagrams. 2
75
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Oil Shale Development Phases
Phase 1 Phase 2 Phase 3 Phase 4 Phase 5
Site Preproduction Ancillary Commercial
Major Activities Preparation Mining Facilities Facilities Operation
Build/Extend Roads X X X
Build Impoundment(s) X X
Sink Shafts X X x
Mine (Development/Retort X X X X
Construction)
Construct Underground X X
Facilities
Construct Surface X X
Facilities
Process Raw Materials! X X
Operate
Build Power Transmission Line(s) X
Build Staging Area (off-tract) X
Build Pipelines (off-tract) X
Build Commercial Facilities X
Figure 4. Development Matrix. Oil Shale Development Phases and their
Associated Major Activities 1
-------
Major Activities
4)
(#
4)
4)
— 0
—
LL. ‘
4) ‘.
4)
4-
(I .)
‘. A
4 - 4)
‘l 1.)
0
o
U 0-
—
4) 4- .
_J ‘-
I
o
4-
u 0
•M 4)
C I-
.
I— DO
C
I-
4) 00
o
0_
- -
I - .
o n
a)
4
9- -
4-
o o
—
Lj
—
—
4)
C 0
I -
— 4)
4)
0.
0
0 U
-u u
co
*Róad building or sinking shafts can be considered either major activities or
subac t iv it i es.
Figure 5. Activity matrix. Subactivities associated with major oil
shale development activities.’
4)
0
U-
0
C
‘I . 0
•u s - . I-
C DO
o 4) 1..
o E 4)
- u - u
-u C C
C
4) 0 4- ’
+ 0. 9-
)c E 0
LU — .C
U i
•u .u 4- .
— — - 4) ( ,
C C C
0
co U
SUB—ACTIVITIES
THAT PRODUCE
ENV I ROt9 1ENTAL
PERTURBATIONS
Remove Vegetation
X
X
X
X
X
X
X
X
Store Equipment/Vehicles
X
X
X
X
X
X
X
X
Operate Equipment
X
X
X
X
X
X
X
X
X
X
X
Drill andBlast
X
X
X
X
X
X
X
X
X
X
X
Grade (Excavate &
Fill)
X
X
X
X
X
X
X
X
Dispose of Waste
X
X
X
X
X
X
X
X
X X
Pave or Surface
X
X
X
X
X
Fence
X
X
X
X
X
X
Increase Traffic
X
X
X
X
Disturb Vegetation
X
X
X
X
X
X
X
X
Build/Extend Roads
*
*
*
*
*
*
*
Remove Underground
Water
X
X
X
X
X
(dewater)
Dispose of Water
X
X
X
X
X
Sink Shafts
*
Build Conveyor
X
Operate Conveyor
X
X
Crush Mined Shale
X
X
Erect Structures
X
X
X
X
X
Exhaust Retorting
Gasses
X
77
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To determine the effects of oil shale development on Tract C-b it was
necessary to systematically identify those effects that might be direct and
those that might be indirect. Three successive matrices were used to do
this, all derived from the operator’s detailed development plan and its
modification.
The first matrix associates major activities of oil shale development
with phases of development (Figure 4). Each major activity is then related
to subactivities within a second matrix (Figure 5). Each of these subactiv—
ities generates certain environmental perturbations or impact-producing
agents. These perturbations or agents are associated with each subactivity
(and in some cases structures resulting from subactivites) in a third matrix
(Figure 6).
The Impact Matrix
Impacts from oil shale development can be traced by linking the pertur-
bations and agents with key ecosystem components and processes in an impact
matrix. For Tract C-b, this matrix arrays 79 rows of processes and compo-
nents against 71 columns of driving variables, oil shale perturbations and
agents, and state variables that interact with the row components and pro-
cesses. Each of the 5,609 cells was evaluated to determine whether there
could be an effect, and if so, whether it was direct, indirect, or both.
Diagrams of the Tract C-b Ecosystem
Three different types of diagrams were used to depict the Tract C-b
ecosystem conceptually: flow diagrams, system diagrams, and process dia-
grams. Flow diagrams were used to illustrate the flows of the various
currencies among ecosystem components (Figure 1).
Two major system diagrams were drawn, one for the terrestr.ial portion
of the ecosystem, and one of the aquatic portion. Each of these diagrammed
the currency flows within each subsystem and linked all subsystems together
with information linkages, as partly illustrated in Figure 2.
Process diagrams focus on the major influences that affect each impor-
tant ecosystem process. These summarize not only the observed time dynamics
of the donor and receiver state variables, but also on the dynamics of all
the variables, state and driving, that affect the rate of transfer between
donor and receiver. Process diagrams were drawn for each of the 85 process-
es included in the Tract C-b model.
In the example, Figure 7, the process of terrestrial animal emigration
from the tract (FTEMI) is controlled by four driving variables (represented
by pentagons at the top of the page) and two state variables (the rectangles
at the bottom). The currency flowing from PTA, the population of terrestri-
al animals, into the sink (conceptually outside the system), is animal
numbers.
78
-------
SUSACT!VITIES
STSuCTU ES
I_____________
—
.po -
: ,
I
I;
,
:‘
I 6
7
T
: j
T
-
i —
J
L
j
i
.
.
11T H
-
- -_-
if-
-
. .
——j-
—__ —
These perturbatons are identified as being associated with the corresponding activities and structures.
These can be considered either subactivities or perturbations.
Figure 6. Perturbation matrix. Environmental perturbations generated
by associated oil shale development activities. 1
79
-------
Emigration, Terrestrial (FTEMI)
DOL(9) 0Th
DGR DOL(8)
Figure 7.
One of the 85 process diagrams used in the Tract C-b con-
ceptual model. The process, emigration, is represented by
the center diamond. Emigration controls the flow of animal
numbers out of the system. Normal emigration rates are
graphed in Box A. These rates are influenced by driving
variables (the pentagons at the top of the page) and state
variables (rectangles at the bottom). Circles represent the
functional responses of emigration to the various state and
driving variables. See text for discussion of nomenclature. 1
FTEMI PTAC
Box A
80
-------
The rectangle with rounded corners (FTEMI) at the bottom center of the
figure represents the normal pattern of animal emigration. This pattern is
influenced by the driving and state variables qualitatively as shown in the
circles.
For example, the air temperature (DTA) changes seasonally, as shown in
the pentagon. The influence of that seasonal temperature change is nega-
tively sigmo-id: i.e., as air temperature increases, the animals slowly
begin to leave more and more rapidly; finally, when most of the animals have
left the area, the emigration rate slows down again.
One projected influence of oil shale development on emigration is shown
in the pentagon labled DOL(9), which represents the secondary hunting and
recreation pressures brought to the area by the oil shale development. The
dotted curve represents only an estimate from sources outside the baseline
study. The effect of increased hunting and recreation pressure is assumed
to be positively linear, as shown in the circle. The effect of snow (WSI),
a state variable, on emigration is likewise projected to affect emigration
in a positive linear fashion.
THE CONCEPTUAL MODEL
The preceding section provides a short explanation of our methodology.
This section summarizes the model in a brief overview, discusses the utility
of the model, and provides a retrospect on the project.
Overview of the Model
The seven ERUs in the model (Table 1) are linked by many flows of
material in and out. Movement of animals, water, and litter across bounda-
ries couple the ERUs. In addition, the relative acreages of ERUs change
according to how man manipulates the ecosystem: deforestation, rehabilita-
tion, and flooding all alter the character of response units, and these
processes are included in the model.
Biotic state variables are represented by five groups of terrestrial
plants, (each with three functional groups of plant parts), ten groups of
terrestrial animals (Table 4), litter, aquatic vertebrates and inverte-
brates, rooted and nonrooted aquatic plants, and detritus.
Abiotic state variables include soil temperature, soil moisture, land
surface area, water temperature, and water surface area. Fifteen driving
variables were also included (Table 5). Flows among all variables are
regulated by 85 processes in the model.
Observed behavior of many state and driving variables is represented by
nearly 500 time-series graphs plotted against a standard 2.5-year X axis.
These graphs were used to represent the behavior of variables in the process
diagrams (Figure 7) in order to infer qualitatively the behavior of a par-
ticular process as influenced by the time-dynamics of the variables.
81
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TABLE 4. SUBDIVISION OF STATE VARIABLES USED IN THE
CONCEPTUAL MODEL, WITH SUBSCRIPTS
The subscript I refers to the ecosystem response unit
Aggregated State Variable Subscript J
Disaggregated Variables
1
Cattle
2
Deer
3
Insectivorous birds
Terrestrial animals numbers-- 4
Omnivorous birds
PTA (I, J) 5
Carnivorous birds
Terrestrial animal weights-- 6
Mammalian predators
GTA (I, J) 7
Rabbits
8
Rodents
9
Reptiles
10
Arthropods
1
Annual plants
Live shoots--CVS (I, J) 2
Perrenial graminoids
Live roots-CVR (I, J) 3
Perennial forbs
Standing dead--CVD (I, J) 4
Shrubs
5
Trees
TABLE 5. LIST OF DRIVING VARIABLES AND
THEIR ACRONYMS
Number Variable Name
Acronym
1 Precipitation
DPI
2 Air Temperature
DTA
3 Wind
DWD
4 Solar Radiation
DRD
5 Forest Cutting Schedule
OCT
6 Cattle Grazing Schedule
DGR
7 Sulfur Compound Emissions
DOL (1)
8 Nitrogen Compound Emissions
DOL (2)
9 Ozone and Oxidant Emissions
DOL (3)
10 Trace Metal Emissions
DOL (4)
11 Carbon Monoxide Emissions
DOL (5)
12 Water Vapor Emissions
DOL (6)
13 Fugitive Dust
DOL (7)
14 Noise and Activity Disturbance
DOL (8)
15 Secondary Hunting arid Recreation
DOL (9)
82
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The model did not attempt to develop functional mathematical forms for
these graphs, nor to develop equations to describe the processes.
Utility of the Model
This conceptual model describes major interrelationships on Tract C-b
from a systems perspective. These interrelationsips consist of innumerable
feedback loops within and between the living and nonliving portions of the
system. The model focuses on major feedback loops as they operate on Tract
C-b.
Because field data collection was not well coordinated in time and
space among disciplines in the baseline studies, there was virtually no
attempt in most of the summary volumes to cross-correlate data on the dif-
ferent ecosystem variables that were measured. The conceptual model draws
qualitative inferences from those data as well as from other ecological
sources and principles outside the baseline study.
A systems approach exploits the organizational power associated with
different types of model-building to array, analyze, and synthesize environ-
mental data and information to improve our understanding of the ecosystem
and our ability to predict what will happen to that system if stressed by
human-controlled or natural influences.
The approach here was largely qualitative. The model comprises box-
and-arrow diagrams, matrices, and verbal description of the important
environmental interrelationships that exist on the tract. Although these
conceptualizations describe the behavior of ecosystem components and pro-
cesses primarily in terms of their qualities, this step is always a neces-
sary preliminary to more rigorous quantitative analysis.
•The conceptual model documented in Van Dyne and Haug 1 serves several
purposes.
o It pulls together a vast amount of information and data about
Tract C—b within a logical conceptual framework.
o It summarizes most baseline information in nearly 500 time-
series graphs that depict behavior of many components and
processes in the ecosystem over a 2.5-year period.
o It serves as a cross-reference to data reported in earlier
volumes of the report, via the time-series graphs.
o It points out deficiencies in the baseline study and identi-
fied gaps in the data.
o It provides a theoretical foundation from which a computer
simulation model could be ct rived in the future.
83
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o It permits users to track potential impacts qualitatively
through the system.
o It assisted users in planning a monitoring program.
o It provides a tool for planning mitigation measures by iden-
tifying ways in which environmental perturbations or agents
from oil shale development could move through the system and
impact components removed in time and space.
Retrospect
The results of this modeling effort were both gratifying and frustrat-
ing. They were gratifying in that the modeling process enabled us to orga-
nize, classify, summarize, integrate, and synthesize the environmental
baseline data into a crude first cut at understanding the ecosystem and the
potential influences of oil shale development.
The exercise was frustrating for several reasons. First, having gone
this far, we wish we could have taken the next step and developed the model
into a full-blown simulation tool that could have been linked directly to
monitoring and mitigation during the life of the project. Such a model very
likely would have permitted the operator ultimately to reduce his monitoring
program through a long range feedback—and-adjustment process.
By this is meant, as the model is fine-tuned using data from a long
term monitoring program, critical ecosystem parameters (i.e., variables or
processes) would have gradually been identified. The monitoring program
could then have relied on measuring only those sensitive parameters instead
of monitoring many parameters in the hopes that the sensitive ones were
included. In other words, as our understanding of the ecosystem increased,
as evidenced by the predictive capabilities of the model, unnecessary moni-
toring could have been phased out.
A second source of frustration was the lack of relevant data from the
2..5-year baseline study. After synthesizing nearly 500 time-series graphs
from the baseline data, and after developing a conceptual ecosystem model,
we found that any relationship between what was actually measured, and what
should have been measured, was almost coincidental.
For example, man’s influence on the ecosystem was virtually ignored.
No information was available on hunting, recreation, forest cutting, and
cattle grazing activities on the tract, although all four activities are
known to occur. Environmental noise above background was measured, but
there are no indications of what the source of that noise was. Although
there are 42 time-series graphs dealing with birds, not one is available for
deer numbers on the tract. Although cattle are known to graze the site, no
figures were reported. A great quantity of water chemistry data was obtain-
ed, but they provide little relevant information.
84
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Most of the items measured were variables. Little information was
provided on process rates. No information was developed for factors influ-
encing processes. Had the conceptual modeling activity been completed
first, many of these information needs would have been identified, and
baseline studies could have been more efficient, more focused, and better
coordi nated.
ACKNOWLEDGEMENTS
This work was performed while Haug was employed at ERT/Ecology Consul-
tants, Inc. , Fort Collins, Colorado, under a subcontract to Quality Develop-
ment Associates, Inc. , Denver, Colorado. The study was financed by
Occidental Oil Shale, Inc. , Grand Junction, Colorado. Figures from Van Dyne
and Haug 1 are reproduced with permission of Occidental Oil Shale, Inc.
REFERENCES
1. Van Dyne, G.M. and P.1. Haug, “Oil Shale Tract C-b Environmental Base-
line Program Final Report, Volume 5: System Interrelationships,” C—b
Shale Oil Venture, Grand Junction, CO. 1977, p. 285 + none foldout
charts.
2. States, J.B., P.1. Haug, T.G. Shoemaker, L.W. Reed, and E.B. Reed, “A
Systems Approach to Ecological Baseline Studies,” FWSIOBS-78121 U.S.
Department of the Interior, 1978, p. 392.
85
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GROUNDWATER QUALITY SAMPLING APPROACHES
FOR MONITORING OIL SHALE DEVELOPMENT
G.C. Slawson, Jr.
General Electric Company-TEMPO
Center for Advanced Studies
P.O. Drawer QQ
Santa Barbara, California 93102
L.G. McMillion
U.S. Environmental Protection Agency
Environmental Monitoring and Support Laboratory
Las Vegas, Nevada 89114
ABSTRACT
The development of cost-effective groundwater quality monitoring pro-
grams for oil shale development requires a structured (or planned) assess-
ment process leading to selection of sampling sites and sampling methods.
General Electric Company-TEMPO is conducting a study to assess the impacts
of oil shale development on groundwater quality and to develop monitoring
design guidelines. One of the roles of a structured design methodology is
to assure the quality of monitoring data. This is accomplished by defining
monitoring goals, evaluating monitoring options, and creating a framework
for assessing cost-effectiveness. The groundwater quality monitoring design
process includes:
o identification and characterization of potential sources of
groundwater quality impact
o characterization of the location of these sources with regard
to hydrogeology and existing groundwater quality
o assessment of mobility and attenuation of potential pollut-
ants in the subsurface
o development of a priority ranking of potential sources of
impact and of potential pollutants.
The purpose of utilizing such a monitoring design process is the collection
of useful data. Design of groundwater quality data collection programs in
the oil shale region poses some interesting problems as a result of the
complexity of the hydrogeology of this area. Some of the key issues in
selection of methods for sampling in the subsurface are:
86
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o well completion
o use of pumping, bailing, or other techniques for sampling wells
o sampling frequency.
This paper outlines these considerations for selection of sampling approach-
es.
INTRODUCTION
The focus of this symposium is on sampling methods, analytical ap-
proaches, and quality assurance, in general, as related to oil shale devel-
opment. Some of the analytical needs for environmental monitoring of oil
shale operations are probably unique to this new industry. Monitoring of
groundwater quality also presents some special requirements with regard to
design of monitoring programs and selection of sampling methods. This is
because of the hydrogeologic character of the oil shale region and because
of the nature of some of the proposed development technologies (e.g., true
and modified in situ retorting). The keynotes of this paper are: (1) the
need for preplanning or structured design of data collection programs (data
need a defined end use, as data have little intrinsic value), and (2) the
role of sampling design in determining what is observed and the uses of
groundwater quality data.
MONITORING PROGRAM DESIGN
General Electric Company-TEMPO is presently conducting a study concern-
ing design of groundwater quality monitoring programs for oil shale develop-
ment. This program, which is being conducted for the U.S. Environmental
Protection Agency, includes consideration of deep mine-surface retorting
operations such as proposed for Federal Lease Tracts U-a and U-b in Utah,
and modified in situ operations such as proposed for Tracts C-a and C-b in
Colorado. The work scope of this effort is based on a general monitoring
methodology developed by TEMPO (Todd et al. 1 ) and includes the following
sequence of steps:
o identify potential sources or causes of impact on groundwater
quality and the potential pollutants associated with these
sources
o carefully examine and interpret background data on the sub-
surface flow regime
o evaluate the mobility of pollutants in the subsurface
o develop a priority ranking of potential pollution sources and
their associated pollutants based on: (1) mass of waste,
concentration, persistence, toxicity, (2) potential mobility,
and (3) known or anticipated harm to water users
o assess gaps in existing groundwater quality monitoring pro-
grams and design a monitoring program based on these gaps and
the priority ranking.
87
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Preliminary results of this study, including a preliminary priority ranking
and monitoring program designs, are contained in a series of project reports
(Slawson; 2 Slawson and Yen 4 ) and will not be presented in detail here.
The planning or structuring of the design of monitoring programs is
essential to assure the quality of the results of that monitoring program.
There are several avenues by which a structured design methodology, such as
outlined above, provides this desired element of quality assurance.
First of all, such a methodology forces one to define that which is
desired of the data collection program. By linking the processes of data
collection and data assessment in the stepwise sequence outlined above,
basic definitions of the use to which the data are to be put are provided
prior to data collection. The data collection program is thus not developed
in a vacuum with no predetermined data analysis-data use program.
Secondly, because the end uses of data are predetermined, the struc-
tured design methodology provides for evaluation of options, such as
alternative sampling sites, sampling methods, sampling frequency, analytical
methods, and other elements of the monitoring design. The basic technical
rationale ,for selection among alternatives follows from consideration of
which options provide the best (or at least adequate) data to address the
defined needs (uses).
Related to these items is the development of cost-effectiveness cri-
teria. The need for good, defensible data is undeniable, but recognition of
economic realities, along with technical-scientific limits, is also neces-
sary. A logical planning sequence can provide this desirable quality of
cost-effectiveness to the monitoring design process as well as to the moni-
toring design itself.
GROUNDWATER SAMPLING METHODS
The methodology described above provides a logical framework for design
of groundwater quality monitoring programs, including selection of sampling
sites, well construction features, sample collection methods, and sampling
frequency. In the oil shale regions, the complexity of the hydrogeologic
systems encountered can present some special problems with regard to these
monitoring components. The influence of the hyctrogeology of the oil shale
region on these considerations is the topic of the following discussions.
Sampling Sites
Groundwater flow in the Piceance Creek Basin occurs in several complex
systems of fractures and faults. The evaluation of a fractured-rock flow
system is generally much more complicated than assessment of a granular,
porous media type of aquifer system. In fractured-rock systems, even def in-
ing the direction of flow may not be straightforward. Generally, the direc-
tion of flow and the flow gradient in groundwater systems are identified by
measuring the head (or water level) in a set of wells and estimating lines
of equal heaG. Flow then is perpendicular to these equipotential lines
88
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(Figure 1). However, flow in fractured rock is along fractures and these
flow paths can provide a flow direction which is nearly perpendicular to
that which may be estimated from simple observation of head levels (Figure
2). Using this illustration (Figure 2), placing a well at point B to moni-
tor the effects of an injection well or other waste source at point A would
clearly not produce data which address the defined information requirements.
The need for detailed hydrogeologic evaluation is thus an integral part of
the monitoring design methodology.
Well Construction
The aquifer systems in the Piceance Basin include a series of horizon-
tal fracture sets very irregularly interconnected by vertical fractures and
faults. The system has commonly been portrayed as including two aquifers
separated by the rich oil shale beds of the Maho gany Zone. In actuality,
the irregular spacing of both vertical and horizontal fractures, the appre-
ciable variability of hydraulic properties among these fracture sets, and
the varying degrees to which halite and nahcolite minerals have been leached
from different zones, create numerous distinct aquifer units. Where wells
are located and where they are perforated (open to water-bearing zones) have
a significant influence on the data collected. This is true for data on
both aquifer characteristics and groundwater quality.
Consider, for example, two wells located close together and which are
perforated over exactly the same interval. The perforated interval contains
two fractured strata of equal hydraulic conductivity (Figure 3). One strata
contains abundant saline minerals and the other little. One well intersects
a fracture in the upper strata, but none in the lower (saline) strata, while
the other intersects a fracture in only the lower strata. These two wells
will provide drastically different water quality data in spite of their
proximity and construction similarity.
This situation may be further complicated by varying permeabilities of
different strata. Some fine-grained, high-organic level strata are resist-
ant to fracturing and may form effective aquitards. This can result in
different head levels between layers and mixing of highly different quality
waters in interconnections, such as well bores. As an example of how well
completion (and recompletion) can affect water quality data, consider the
following data reported for Tract C-b (C-b Shale Oil Venture 6 ).
Original Well TDS before TOS after New Well
Designation Recompletion Recompletion Designation
SG-11, string 1 39,000 16,000 SG-11, string 1R
SG-1O 42,000 2,800 SG-1OR
SG-17, string 1 28,000 4,300 SG-17, string 1R
These wells had initially encountered and been open to a highly saline water
zone which apparently had a higher hydrostatic head than less saline over-
lying aquifer zones. Thus water collected from these overlying zones was
affected by the interconnection. Recompletions were undertaken to isolate
these different water quality zones.
89
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400
395
HEAD ELEVATION
390 385
380 375
0
EQUIPOTENTIAL
——FLOW (ORTHOGONAL)
N
/
,,
Figure 1. Sample of groundwater flow net.
-------
Figure 2. Idealized two-dimensional pattern showing the relation between true direction
of groundwater flow and the direction inferred by drawing orthogonal lines to
the regional water level contours (adapted from Davis and DeWiest 5 ).
INFERRED DIRECTION
B
-------
LOW IDS
WATER WELL
HIGH IDS
WATER WELL
GROUND SURFACE
UPPER FRACTURE STRATA,
LITTLE SALINE MINERALS
LOWER FRACTURE STRATA,
SALINE MINERALS PRESENT
:igurc 3. Fractured-rock aquifer system yielding water of varying quality
depending on location and perforation of wells.
-------
Also, interval of completion and perforation may affect water level
data. For example, on Tract C-b, an apparent mound of water in the center
of the tract may be due to data from a well (SG-6) completed over a small
segment of the aquifer zone. If this interval has a high head, then this
well will show a greater head level than other wells in the area which are
perforated over a wider zone and thus exhibit a more average head.
Sample Collection Methods
Methods currently being used to collect groundwater samples on the oil
shale tracts include bailing, swabbing, and pumping. The choice of sampling
method can greatly influence the results of water quality sampling and thus
the interpretation Of monitoring data.
On Tract C-a, all groundwater quality samples are collected by bailing.
Sufficient water is bailed to fill the required sample bottles. One of the
goals of sampling is to obtain water quality data which are representative
of water within the aquifer zone being sampled. Aside from problems of well
completion, bailing of a small volume from a well bore may not provide the
desired representative sample. For example, construction of deep wells may
include a perforated zone of perhaps 300 feet (91 meters). A 6-inch (15-
centimeter) casing 300 feet long contains about 450 gallons (1,700 liters)
of water. If approximately 4 gallons (15 liters) is bailed for sampling,
for example, on a quarterly basis, water sampled may not be representative
of local groundwater, but water which has been standing in the well bore
(perhaps a very different physiochemical environment) for some time.
The implication here is that care must be taken with the use of bailing
as a sampling technique. For example, tests conducted by Rio Blanco Oil
Shale Project (Tract C-a) indicated that samples bailed from well intervals
perforated in aquifer zones produced results very comparable to pumped water
samples. However, samples bailed from the well interval above the perfo—
rated zone (and where water is stagnant within the well) yielded water
quality data quite different from either pumped samples or samples bailed
from the aquifer zone.
Swabbing, which is used to collect samples from deep aquifers on Tract
C-b, includes the use of oil field equipment to collect water samples.
Several swabbing runs, removing the water column from the well bore, are
made prior to collection of samples for laboratory analysis. This approach
may provide water quality samples more representative of local aquifer
conditions than bailing, as several well volumes are removed prior to actual
sample collection.
Care must also be taken with the swabbing techniques so that contami-
nation of samples (such as from organics from the oil field equipment) does
not occur. In addition, the swabbing action may accelerate the plugging of
well perforations by the action of the rubber swabbing cup on the casing.
The amount of water swabbed from a well must be carefully considered to
obtain consistent and representative samples. Variations in water quality
(conductivity) observed during swabbing are shown in Table 1.
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Table 1. RANGE OF CONDUCTIVITY OBSERVED AND FINAL CONDUCTIVITY
LEVEL OF SWABBED SAMPLES, TRACT C-b, FALL 1976
Well/string
nunber
Gallons
swabbed
Observed
conductivity range
(pmhos/cm)
Final
conductivity
(pmhos/cm)
SG-1, #1
1,260
3,000 - 10,000
8,250
SG-l, #2
2,840
1,200 - 1,500
1,250
SG-9, #1
2,100
1,300 - 3,400
2,000
SG—9, #2
1,150
1,850 - 2,100
1,850
SG -21
3,210
750 - 1,150
1,000
Cb-4
2,300
800 - 900
825
SG-11, #12
1,220
14,000 - 32,000
22,000
SG-ll, #2
530
800 - 4,000
1,200
SG-I1, #3
300
1,600 - 1,800
1,790
SG-18A
--
750 - 1,250
1,000
Cb-2
2,920
1,600 - 1,650
600
SG—6, #1
550
1,800 - 3,100
3.i00
SG-6, #2
630
1,300 - 1,400
1,300
SG—6, #3
160
1,350 - 1,550
1,550
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Many of the difficulties of obtaining representative samples by bailing
or swabbing are overcome by use of a submersible pump to collect samples.
By pumping, a relatively large area of the aquifer is sampled rather than a
zone within or immediately adjacent to the well bore. This “sampled zone
size” is an important consideration for monitoring purposes, as well as for
general collection of representative samples. For example, assume a well is
perforated throughout the water-bearing zone (Figure 4). Bailing will
sample essentially the width of the well bore, perhaps 6 or 8 inches of the
aquifer cross section. Swabbing would sample a wider cross section (perhaps
several tens of feet). Obviously, the opportunity of detecting the mobility
of potential pollutants is enhanced by sampling a greater cross section of
the aquifer.
Care must also be taken in the design of sampling programs which
include pumping. As shown in Table 2, water quality can vary greatly as
pumping continues. A schedule of pumping time before sample collection has
to be established, largely by trial sampling of each well and frequent
sampling of, for example, conductivity and pH, in the field during pumping.
Sampling Frequency
Defining an appropriate sampling frequency is a complex issue influ-
enced by location of sampling sites, monitoring goals, climatological
factors, and characteristics of groundwater flow. As a result, sampling
frequency should be defined on a case-by-case and likely trial-and-error
basis. One of the key factors is groundwater flow rate. If flow from a
potential pollution source to a monitoring well is expected to be on the
order of decades (assuming a release occurs), thenvery frequent sampling
does not seem warranted and perhaps annual sampling for a few indicator
constituents would suffice.
The complexity of the hydrogeology of the oil shale region makes esti-
mation of groundwater flow rate difficult at best and the actual flow rates
highly site specific. Table 3 lists some estimates of travel time in the
upper aquifer zone of the Piceance Creek Basin. The wide variation in
results reinforces the care needed in design of monitoring programs, as our
understanding of the system is incomplete.
CONCLUSION
The goal of monitoring programs is to gather information, such as water
quality data, for some decisionmaking process, such as determining the
effectiveness of environmental control or mitigation measures. The quality
of the data obviously influences the quality of the decision. The assurance
of quality data comes from the planning arid structured design of monitoring
programs as much as from the use of reference samples, spiked samples,
duplicate or repeat samples, standard analytical methods, proper instrument
calibration, chain—of-custody procedures, and the other activities more
normally associated with quality control-quality assurance programs. With-
out such planning, one may collect very “good” data which are inadequate in
some way for the decisionmaking process.
95
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en
MONITOR WELL
BAILING
«
\
\
SWABBING
PUMPING
WATER TABLE
Figure 4. Schematic of size of aquifer cross section sampled by bailing.
by swabbing, and by pumping of monitor well.
\
LAND SURFACE
-------
TABLE 2. FLUORIDE AND BORON FROM LOWER AQUIFER PUMP TEST
(C-b Shale Oil Venture 6 )
Date (1975)
Fluoride (ppm)
Boron (ppm)
January 20
18.0
0.65
February 5
18.1
0.88
February 23
20.0
1.15
February 24
20.1
1.10
February 25
20.4
1.13
February 27
18.4
1.2
February 28
20.4
1.6
March 1
20.2
1.42
March 3
19.0
2.58
March 5
20.0
2.02
March 7
21.2
2.18
March 19
23.2
2.00
97
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TABLE 3. FLOW RATES OF THE UPPER AQUIFER, PICEANCE CREEK
BASIN, ESTIMATED BY THREE STUDIES
Study Reference
Flow Velocity
(feet per day)
Travel Time
(years to
travel 1 mile)
Lawrence Berkeley
Labs, 1978
(data from Weeks
et al., 1974)8
0.05
300
U.S. Atomic Energy
Co mnission, 1972
a
0.36-0.78
20-40
Knutson, 1973’°
11-7
1.2
aRange for representative gradient and maximum gradient cases.
98
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Some of the analytical needs for environmental monitoring of oil shale
operations are probably unique to this new industry. Monitoring of ground-
water quality also presents some special problems with regard to selection
of sampling sites, well construction, sample collection methods, and
sampling frequency. This is because of the complexity and heterogeneity of
fractured rock and/or solution cavity aquifer systems, such as are found in
the oil shale region. Special care must be exercised in sampling in such
hydrogeologic systems to assure that what is being sampled is that which one
desires to sample and also that which one thinks is being sampled.
ACKNOWLEDGEMENTS
The studies referenced in this paper have been sponsored by the U.S.
Environmental Protection Agency, Environmental Monitoring and Support Labo—
ratory, Las Vegas, Nevada. The authors wish to make special acknowledgement
of the project support by Mr. George B. Morgan, Laboratory Director. Key
technical contributors to this study are Dr. L.G. Everett (TEMPO Project
Manager), Mr. F.M. Phillips, Or. O.K. Todd, Dr. L.G. Wilson, and Dr. K.D.
Schmidt.
REFERENCES
1. Todd, D.K., R.M. Tinlin, K.D. Schmidt, and L.G. Everett, “Groundwater
Quality: Monitoring Methodology,” EPA-600/4-76-026, June 1976.
2. Slawson, G.C., and T.F. Yen (eds), “Groundwater Quality Monitoring of
Western Oil Shale Development: Identification and Priority Ranking of
Potential Pollution Sources,” EPA-600/7-79-023, 1979.
3. Slawson, G.C. (ed), “Groundwater Quality Monitoring of Western Oil
Shale Development: Monitoring Program Development,” GE78TEMPO-90,
(Draft in review by EPA), 1979.
4. Slawson, G.C., and T.F. Yen (eds), Compendium Reports on Oil Shale
Technology, EPA-600/7-79-039, 1979.
5. Davis, SN., and R.J.M. DeWiest, Hydrogeology , John Wiley and Sons,
Inc., New York, 1966.
6. C-b Shale Oil Venture, “Oil Shale Tract C-b Environmental Baseline
Program Final Report,” November 1974-October 1976, Submitted to an Area
Oil Shale Supervisor, Grand Junction, Colorado, 1977.
7. Lawrence Berkeley Labs, Chapter 6, “Diffuse Source Effects on In Situ
Oil Shale Development on Water Quality,” (Draft report), 1978.
8. Weeks, J.B., G.H. Leavesley, F.A. Welder, and G.J. Saulnier, Jr. “Simu-
lated Effects of Oil Shale Development on the Hydrology of Piceance
Basin, Colorado,” U.S. Geological Survey Professional Paper 908, 1974.
99
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9. U.S. Atomic Energy Commission, Environmenta1 Statement: Rio Blanco
Gas Stimulation Project, Rio Blanco County, Colorado, April 1972.
10. Knutson, C.F. , “Project Rio Blanco: Evaluation of Possible Radio-
activity Transport in Goundwater,” CER Geonuclear Corpoation, Las
Vegas, Nevada, 1973.
100
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QUALITY ASSURANCE FOR WATER MONITORING PROGRAMS
Douglas M. Skie
U.S. Environmental Protection Agency
Region VIII - Denver
ABSTRACT
The primary objective of quality assurance is to insure that generated
data are complete, accurate, representative and legally defensible. Conse-
quently, the development and implementation of an effective quality assur-
ance program is an integral part of operating a reliable water monitoring
program.
Although a major portion of the quality assurance effort is normally
expended in the laboratory, quality assurance activities associated with
field sampling and data handling must also be addressed to insure that the
integrity of the data is maintained from the time the sampling network is
designed until the data are made available to the data users. An overview
of these major internal and external quality assurance considerations for
field sampling, laboratory analysis and data handling will be discussed.
Review will also be made of neglected aspects of quality assurance such as:
management overview of quality assurance activities; management commitment
of resources to establish and maintain a quality assurance program; estab-
lishment of acceptance/rejection criteria used to maintain system control;
initiation of appropriate corrective action; and, providing indicators of
data quality to data users.
In addition, ‘information will be provided to interested conference
participants for obtaining EPA audit samples, manuals, and guidance docu-
ments associated with quality assurance.
(Paper presented at Symposium but not submitted for publication in the
Proceedings. For more information, contact the author.)
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QUALITY ASSURANCE IN SAMPLING AND ANALYSIS
OF OIL SHALE RETORTING OPERATIONS
R.N. Heistand, L.L. Morriss* and R.A. Atwood
Development Engineering, Inc.
Box A, Anvil Points
Rifle, Colorado 81650
Because of wide variations in oil shales, retorting processes, and
operations of oil shale retorts, there is a need for a sound quality assur-
ance program in sampling and analysis of oil shale retorting operations.
Oil shale, its products, and its processes are unique--oil shale is similar
to, yet differs from limestone; the retorting process is similar to, yet
differs from coal liquefaction; crude shale oil is similar to, yet differs
from conventional crude petroleum. Standard procedures for sampling and
analysis of these more common materials and processes, when applied to oil
shale retorting operations, often lead to erroneous and misleading results.
Any quality assurance program, in order to assure that its data are
accurate and valid, needs the combination of the researcher and his array of
analytical tools and the process operator and his knowledge of the process
being evaluated. Because of the uniqueness of oil shale, its products, and
its processes coupled with the variability of these facets, many samples
which have beer 1 taken and analyzed are representative only of that sample.
RESULTS AND DISCUSSIONS
Raw Shale
Raw oil shale has been described as a solid hydrocarbon polymer cross
linked with numerous sulfur, nitrogen and oxygen atoms embedded in a mixture
of minerals. The richness, or grade, usually expressed in gal. oil/ton
shale (or GPT) varies widely (0 to 80 GPT) in the Green River Formation.
These variations have been shown to exhibit a correspondence between grade
and particle size of crushed material. The higher the grade, the tougher
the material and the larger the particle size.’
Raw shale, like limestone, when heated, produces lime. Yet raw shale,
when subjected to higher heating or longer periods of heating, unlike lime-
stone, loses the lime which had been formed. 2 This loss is caused by ther-
mal reactions forming calcium silicates or aluminates.
* Mr. L.L. Morriss is currently employed as Laboratory Supervisor with
Geokinetics in Vernal, Utah 84078.
102
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Finally, the trace metal constituents, important environmentally and in
downstream refinery processing, vary quite widely with the location of the
shale (see Table 1).
Process
The products of oil shale retorting vary widely with the retorting
process used to produce those products. 3 All products appear to vary widely
in their chemical and physical properties. Crude shale oils, obtained from
various processes, are shown to vary in their physical properties, such as
pour points and their initial to 5 percent boiling range; and chemical
composition, such as trace elements and special organic components such as
paraffins, PAAH, and BaP (see Table 2). Note especially that no valid
relationship appears to exist between weight percent benzene extractables
and selected polyaromatic hydrocarbons such as benz-alpha-pyrene (BaP).
Retorted shales also vary with the process. Data in Table 3 show differ-
ences in organic carbon, total carbon, and trace metals.
Operations
One operation for the Paraho retort is the Direct Mode where combustion
of the carbon on the retorted shale serves as fuel for the process; combus-
tion is done directly in the retort. Another mode of operation is the
Indirect Mode where retorting is carried out by gases heated externally;
combustion is done indirectly outside the retort. Data in Table 4 show some
of the differences obtained for the product gas and retorted shale from the
Paraho Direct and Indirect Modes of operation. 4 Gas from Direct Mode is
diluted with nitrogen and carbon dioxide from internal combustion. That
internal combustion also causes less organic carbon, LOl, and mineraLcarbon
on the Direct Mode retorted shale.
Others
Another product of oil shale retorting operations is process water.
This water is produced primarily by combustion, moisture, mineral decomposi-
tion, and organk kerogen breakdown. It is swept from the retorting zone as
a vapor with the oil, gases, and particulate fines and is condensed with the
oil as a liquid. Water, because of its ubiquitous nature, the availability
of many well-defined standard methods, and general ease of analysis has been
sampled and analyzed frequently by many laboratories. As shown in Table 5,
process water from oil shale retorting operations, in many cases, cannot be
analyzed routinely by standard methods. 5 Chloride, determined titrimetri—
cally with mercuric nitrate, will yield results in the order of 1 to 10
percent, or about tenfold higher than when titrated after interferences are
removed by mild oxidation using HNO 3 boiling and acidification. Boiling is
also required before the colorimetric determination of nitrate is performed.
Nitrate results without pretreatment can be more than 10,000 times too high.
The most common problem is in the det.ermination of total dissolved solids.
Ammonium salts, carbonates, and/or bicarbonates are the principal dissolved
solids. Normal drying to 180°C results in losses due to volatilization of
these ammonium salts. Speciation lends yet another problem. Errors in
103
-------
differentiating carbonate and bicarbonate in these complex waters are
common. Problems with sulfur types--sulfate, sulfite, thiosulfate, and
sulfide--have been identified and are being studied. 6
CONCLUSIONS
In order to guard against erroneous conclusions drawn from the environ-
mental analyses of oil shale retorting operations, a quality assurance (QA)
program must be well planned.
The QA program needs--
o Cooperation between the researcher and the process operator
to assure that raw shale samples are representative of the
deposit and to assure that products are representative of the
process.
o Careful examination of sampling and storage procedures and
comparison of various analytical methods to be sure that the
results are indicative of sample composition rather than the
procedures and methods used.
In the Paraho Laboratory, a six-point QA program is used to evaluate
data produced in the lab or submitted by various researches for review.
This six-point QA program is outlined in Table 6. We have found it helpful
in eliminating most of the eroneous data obtained from the characterization
of oil shale retorting operations.
ACKNOWLEDGEMENT
Data discussed in this paper were obtained from the Paraho operations
being conducted at the Department of Energy’s Anvil Points Oil Shale
Research Facility situated on the Naval Oil Shale Reserves located near
Rifle, Colorado. Further, the authors would like to thank Development
Engineering, Inc. , for permission to publish this paper.
REFERENCES
1. Heistand, R.N., “The Fischer Assay: Standard for the Oil Shale
Industry,” Energy Sources , 2, 1976.
2. Heistand, R.N., L.L. Morriss, and D.B. Jones, “Free Time in Retorted
Shale,” Energy Sources , (in print).
3. American Petroleum Institute, “Comprehensive Analysis of Oil Shale
Products,” (project SPS-5), American Petroleum Institute Medicine and
Biological Science Department, (in print).
4. Jones, J.B., Jr., “The Paraho Ofl Shale Retort,” 81st National Meeting
of American Institute of Chemical Engineering, April 11, 1976 and 9th
Oil Shale Symposium, Golden, Colorado, April 29, 1976.
104
-------
5. Morriss, L.L., “Treatment of Oil Shale Process Water for Analysis by
Standard Methods,” Symposium on Environment Analytical Chemistry, 1978.
6. Stuber, H. A. , J.A. Leenheer, and 0.5. Farrier, “Inorganic Sulfur
Species in Waste Waters from In Situ Oil Shale Processing.”
‘OS
-------
TABLE 1. COMPARISON OF RAW SHALES
Parameter RS-1O1 RS-102
RS-1 03
Oil, gallons per ton 27.1 23.3
23.6
Carbon, wt.% 18.4 15.7
17.3
Mineral Carbon, wt.% 5.11 4.66
4.18
Sulfur, wt.% 0.62 0.69
0.24
Lead, ppm 16.00 20.00
15.00
Chromium, ppm 23.0 28.0
48.0
Zinc, ppm 80.0 102.0
82.0
Vanadium, ppm 73.0 84.0
97.0
TABLE 2. COMPARISON OF CRUDE SHALE OILS
Parameter RO-]. R0-2 R0-3
R0-4
Viscosity (SUS 100°F) 174.2 125.7 131.6
107.2
Viscosity (SUS 210°F) 42.3 39.4 40.2
38.1
Pour Point, °F +85.0 +80.0 +65.0
+70.0
Solvent Fractionation
(Schwager and Yen)
Oil, wt.% 85.1 90.3 87.4
81.3
Asphaltenes, wt.% 2.1 1.8 1.3
6.2
Resins, wt.% 12.8 7.9 11.3
12.6
Sulfur, wt.% 0.68 0.69 0.81
0.80
Nitrogen, wt.% 2.17 2.04 1.82
2.05
Polycyclic Aromatic
Hydrocarbons (PAH)
Benzo (a) pyrene (BaP)
Parent, ppb 1800.0 1800.0 2300.0
4250.0
Polycyclic Aromatic
Aza- hydrocarbons (PAAH)
Acridine, ppb
Run 1 20.0 < 20.0 < 20.0
< 20.0
Run 2 47.0 < 20.0 < 20.0
< 20.0
5% Boiling Range (°F)
Initial 165.0 160.0 70.0
0.0
5% 375.0 375.0 325.0
250.0
Acridine was the only PAAH detected in the oil samples. The
detection
limit was 20 ppb.
106
-------
TABLE 3. COMPARISON OF SPENT SHALES
Parameter
SS-201 SS-202
SS-203 SS-2 04
Loss on Ignition, wt.%
15.1 28.5
26.7 7.2
Carbon, wt.%
5.15 10.50
9.90 0.62
Mineral Carbon, wt.%
3.85 5.75
5.78 0.31
Organic Carbon, wt.%
1.30 4.75
4.12 0.31
Sulfur, wt.%
0.56 0.73
0.56 0.78
Lead, ppm
17.0 17.0
21.0 23.0
Chromium, ppm
37.0 46.0
31.0 37.0
Zinc, ppm
86.0 75.0
95.0 140.0
Vanadium, ppm
100.0 98.0
108.0 137.0
TABLE 4. PRODUCT GAS COMPOSITION
Direct Mode
Indirect Mode
Hydrogen, vol.%
4.6
24.8
Nitrogen
64.3
0.7
Oxygen
0.0
0.0
Carbon Monoxide
2.5
2.6
Methane
2.3
28.7
Carbon Dioxide
22.5
15.1
Ethylene
1.0
9.0
Ethane
0.6
6.9
RETORTED SHALE COMPOSITION
Direct Mode
Indirect Mode
Ignition Loss, wt.%
17.72
23.32
Organic Carbon
1.97
3.06
Total Carbon
6.30
8.37
Mineral CO 2
15.86
19.47
107
-------
TABLE 5. PROCESS WATER ANALYSIS
Parameters Standard Method Comments
Modified Method
Mineral Carbon 16.8 gm/I Particle size
Total Carbon is critical
3.54
gm/i
Chloride No endpoint Organics present
25,329 mg/i tend to precipi-
tate the mercury
as salts
3,400.0
mg/i
Nitrate 30,000 ppm Carbonates and
Other labs volatile organics
1 ppm must be removed
1.0
ppm
Phosphate 1-10 ppm Color of solution
Other labs interferes unless
0.5 ppm removed
0.5
ppm
Phenol 130 mg/i Basic extractions
Other labs and distillations
8 ppm must be done
8.0
mg/i
TOS 43.6 gm/i Drying conditions
(anions must be specified
+ cations) for continuity
119.4
gm/i
TABLE 6. QUALITY ASSURANCE PROCEDURES
1. Material Balance or Electroneutrality
2. Process Balance
3. Intralaboratory Comparisons
4. Standard (Spike) Addition
5. Analysis of a Standard
6. “Reasonabieness”
108
-------
USE OF ZEEMAN ATOMIC ABSORPTION SPECTROSCOPY FOR THE MEASUREMENT
OF MERCURY IN OiL SHALE GASES
D.C. Girvin, T. Hadeishi and J.P. Fox
Lawrence Berkeley Laboratory
Energy and Environment Division
Berkeley, California 94720
Preliminary investigations of pilot-scale oil shale processing
plants, 1 ’ 2 indicate that the level of mercury in offgasses may be signifi-
cant. Extrapolation of these results to field conditions suggests that a
100,000 barrel per day oil shale plant processing 100 2/tonne (24 gal/ton)
oil shale with an average mercury content of 0.86 ppm 3 may release approxi-
nateiy 32,900 kg of mercury per year to the atmospflere. In contrast, the
amount of mercury released from world coal consumption in 1967 is estimated
to be 18,900 kg of mercury. 4 ’ 5 These data suggest that mercury emissions
from oil shale plants may be of future environmental concern and that they
may require control technology to reduce mercury levels. This will require
reliable techniques to measure the mercury in these gases.
Reliable and representative measurements of mercury in gases from in
situ shale plants are difficult to obtain. Fox and others found that the
mercury concentration in these gases may vary over several orders of magni-
tude.’ Since retort runs may last many months, frequent sampling over a
long time period must be employed to obtain representative mercury emission
values. Conventional mercury gas stack sampling techniques such as gold
bead absorption tubes or impinger trains are limited by interferences when
applied to oil shale gases due to the presence of high concentrations of
organic and sulfur compounds.
This paper describes a technique to continuously measure total mercury
in the offgas from an oil shale plant or other similar plant on a real time
basis. This technique utilizes Zeeman atomic absorption spectroscopy (ZAA)
for the online measurement of mercury in the presence of smoke, organics and
oil mist. The theory of Zeeman atomic absorption spectroscopy is presented
along with a description of a new instrument suitable for use in field
settings where wide temperature fluctuations may occur.
THEORY OF ZAA
Zeeman atomic absorption spectroscopy (ZAA) is an analytical technique
similar to conventional atomic absorption spectroscopy (AA). 6 ’ 7 ’ It
differs principally in that the light source is placed in a magnetic field.
This separates the original 2537 A resonance line into its linearly (it) and
circularly (a) polarized Zeeman components. The it component is used to
1.09
-------
OR 1
FILTER
XBL ?93-8742
Figure 1. Electro—optical components of a Zeeman atomic absorption
spectrometer.
detect the presence of mercury and the two a components are used to monitor
smoke and’ vapor in the light beam. A unique electro-optical switching
device distinguishes between the it and a components. These components are
alternatively passed through the sample vapor and the difference in absorp-
tion of these two components is used as a measure of the amount of mercury
present. Since the spatial and temporal variations in the it and a compo-
nents are identical, background correction capabilities are vastly superior
to conventional AA techniques. Mercury can be measured in the presence of
large quantities of smoke, organic molecules and other interfering sub-
stances.
A spectrometer consists cpf three major components (Figure 1): a light
source which provides a 2537 A mercury emission line (it) and reference lines
(a) for background correction; a furnace-absorption tube assembly where
vapors from thermally decomposed samples are swept into the light path of
the emission and reference beams; and a detector which converts changes in
the intensity of the transmitted probe and reference beams into an ac volt-
age for signal processing.
The key to the ZAA technique lies in the mode by which the emission and
reference lines are generated and subsequently distinguished from one
another. Both the emission and reference lines are supplied simultaneously
by a single mercury discharge lamp operated in a 15 LcG magnetic field. The
Zeeman effect is the splitting of the original 2537 A emission line, in the
VARIABLE PHASE-RETARDATION
PLATE ASSEMBLY
FUSED SILICA SLAB
ABSORPTION TUBE
.9OO-I2OO C
_ PLE
INLET
COMPACT LIGHT
SOURCE AND
WAVE LENGTH
MODULATOR
TYPICAL ATOMIC ABSORPTION SPECTROMETER
110
-------
Q_
i- 1.0
CO
CVl
-^
^- jc
.E B-
o£
o
% ^0.5
2 E
o
o°
tn
w *-
E -2
c
D
^ 0
1 1
Emis:
1 i
jion line
o
X=2537A *_. H
^
'\
f
/
*f
1
% 204HgTr /
\
f
204Hgo--(l5KG)\
AL
i|
V
n /
/
1 204Hgcr + (l5KG)
/
/ A
J \
0 -0.114 -0.076 -0.038 0 0.038 0.076
).027A-
H0.027A-
XBL 731-I05B
A\ (A)
Figure 2. Comparison of the emission lines from a Eg discharge lamp in a
15 kG magnetic field with the absorption profile (data points) of
natural mercury at 1 a tin of 1$2-
presence of a magnetic field, into its three Zeeman components: a a- compo-
nent shifted to a longer wavelength, a a+ component shifted to a shorter
wavelength and an unshifted n component. These Zeeman components for a
204Hg lamp are shown in Figure 2.
The mercury present in the absorption tube consists of a naturally
occurring mixture of several stable isotopes at a pressure of 1 atm. Thus
the absorption lines of each isotope are pressure broadened. The resulting
total absorption profile due to naturally occurring mercury (at 1 atm of N2)
is superimposed upon the Zeeman-split. emission spectrum (Figure 2). Note
that the n component coincides with the peak of the absorption profile for
natural mercury, while the a components are both on the outer edges of the
profile. Therefore, the difference in absorption of the n and a components
may be used as a measure of the quantity of mercury present in the absorp-
tion tube. Here the n component becomes the probe beam and the a components
taken together become the reference beam.
The ZAA technique also provides a means of distinguishing between the n
and a components. When the light source is viewed perpendicular to the
applied magnetic field, that is, along the optical axis of the instrument,
both a components are linearly polarized perpendicular to the field, while
the n component is linearly polarized parallel to the field (Figures 1 and
3). Consequently, either component may be viewed independently of the other
with a properly aligned linear polarizer.
Ill
-------
Alternate selection of the it and a components for detection before
tansmission through the absorption region is achieved by using a variable
phase retardation plate (VPRP) and a simple linear polarizer (Figures 1 and
3). The linear polarizer is oriented with its polarization axis parallel to
the light source magnetic field (Figure 3). The VPRP (Figure 4) is a slab
of fused quartz mounted inside a pulse-transformer core which has a driver
coil on one side and a 0.5 mm air gap on the other. Varying the current
through the driver coil applies a stress to the quartz plate. The stress
axis of the quartz is oriented at an angle of 45° to the light source mag-
netic field.
The polarization axis of the incident linearly polarized light is
rotated 900 as the light passes through the stressed quartz. This rotation
is due to the difference in the propagation velocities for those components
of polarized light which are parallel and perpendicular to the quartz stress
axis. The amount of rotation is controlled by appropriate selection of
current to the driver coil and the optical path length of the quartz. As
seen in Figure 3, when the current applied to the driver coil is zero (no
stress), only the it component is transmitted by the linear polarizer and
thus passes through the absorption tube. When the driver coil current is
adjusted so that the quartz is a half-wave plate (maximum stress), both it
and a components are rotated by 900, and the linear polarizer passes only
the a or reference component.
The sample to be analyzed for mercury enters the furnace-absorption
tube assembly where it is heated to 900°C. Mercury and its compounds atom-
ize (thermally decompose) well below 900°C. Individual free atoms of
mercury and decomposition products are then swept by the stream of sample
gas into the light path of the absorption tube. Oxygen is introduced into
the furnace chamber to promote combusiton of organics and thus reduce smoke.
The it component is attenuated due to absorption by mercury atoms and scat-
tering by decomposition products and smoke. The a component is attenuated
by scattering and smoke only.
The detector consists of an interference filter or monochrometer which
passes all Zeeman components of the 2537 A line equally well, but blocks
light of other wavelengths from striking the cathode of the photomultiplier
tube (PMT). The PMT generates an output voltage proportional to the intens-
ity of the it and a components. If no mercury is present in the absorption
tube, the probe and reference beams are absorbed and scattered identically
by nonmercury background. Hence, as they alternatively fall upon the PMT,
the light intensity does not change, and the PMT output voltage remains
constant. In the presence of mercury, however, the probe component will be
more strongly absorbed than the reference component, and the PMT output will
vary at the audio frequency at which the switching from one beam to the
other takes place (Figure 5).
The PMT output, together with an audio reference signal from the oscil-
lator driving the magnetic clamp, is fed into the lock-in-amplifier (Figure
5). The tuned amplifier in the front end of the lock-in-amplifier accepts
only those signals having the same frequency as that used by the VPRP to
112
-------
Light source J Phase retardation ', Linear polarizer & furnace
! plate (squeezer)
Magnetic ff ' No stress
field ^/, (no rotation)
Detector
Light path
Light path
Max stress
(90°rotation)
Phototube
Phototube
XBL 793-"
Figure 3. Schematic Representation of ZAA Depicting TT (probe)
Beam and a (reference) Beam Switching.
X8L748-3»««A
Figure 4. Diagram of the current-controlled variable phase retarda-
tion plate, (a) Plate of fused quartz; (b) laminated pulse
transformer core; (c) 0.5 mm gap; (d) drive coils; (e)
stiffener plates. The long arrow in the center of the
quartz represents the stress axis, while the double arrows
depict the linear polarization axes of the n and a beams.6
113
-------
U
0 ’
0
a)
0 ’
0
0
>
/ Time
Background Background on’y
p’us atomic
absorbtion
Voltage
proportional
to Hg density
[ ecorderj
X L 793-790
Figure 5. Signal processing electronics. To separate the iT and C signals,
the lock—in amplifier requires a reference signal from the
squeezer circuit. The square waves shown are an idealization;
these signals actually vary sinusoidàlly.
switch between it and a beams. This amplifier first recognizes and then
takes the difference between the it and a components in the audio portion of
the PMT output. It supplies a dc voltage which is proportional to this
difference and thus is proportional to the density of mercury in the absorp-
tion tube.
The accuracy of the background correction obtained by ZAA through the
use of spatially and temporally coherent n and a beams and synchronous beam
switching and electronic signal processing techniques results in a signifi-
cant advance beyond conventional AA background correction. As a result, ZAA
is capable of performing accurately with up to 95% attenuation (from smoke
or broad-band IJV adsorption) of the i and a components. Thus, ZAA is cap-
able of direct analysis of most gas, liquid and solid samples for mercury
without prior chemical treatment. This direct analysis capability is the
m’ajor advantage of ZAA over conventional AA for online field measurements.
GAS MONITOR
A ZAA spectrometer has bee designed and built which is capable of
continuously measuring mercury concentration in offgas streams on a real
time basis. Specifically, a new light source, furnace assembly gas handling
system and calibration system have been developed to accommodate gas
Max stress
Time
I
a- Signal
114
-------
Relative intensity change in 253.7nm Hg line
vs. temperature of Hg lamp
o
-------
ZAA signal vs. temperature for
l.22mgHg/m3 (I22ppb) in sample gas
CO
±= 0.9
o
^ 0.8 r
0.7 r
f 0.6 h
0.5'
13
Q.
Q.
E
0.4 h
i
|
0.31-
Corrected for baseline shift
•E 0.2
u
o
0,
0
8 10 12 14 16 18 20 22 24 26 28 30 32
Hg lamp temperature (°C)
XBL 793-870
Figure 7.
Temperature dependence of ZAA response to a constant concentration
of mercury. The decrease in response at 6 C is an experimental
anomaly caused by the defocusing of the light beam by water
droplets condensed out on the outside of the light source window
at this temperature.
constant concentration of mercury remains constant within measurement errors
(Figure 7). This stability is achieved by routing the PMT signal through a
log amplifier before it enters the tuned amplifier section of the lock-in-
astplifier. Thus the electronic processing effectively filters out the
effect of light intensity changes due to temperature fluctuations.
However, there is another temperature effect which is not filtered out
bv the electronics. The relative intensities of the re and a lines are
affected by self reversal or self-absorption of these lines within the
plasma of the mercury discharge lamp. This self-reversal increases with
temperature. This relative change in the n and a intensities manifests
itself as a change in instrumental baseline voltage and thus is indistin-
guishable from the signal produced by mercury in the sample gas. The magni-
of this effect is shown in Figure 8. In the absence of mercury a
12-31°C change in temperature produces a 220 mV ZAA voltage as shown by the
lower curve in Figure 8. The upper curve shows this change in parts pe»~
116
-------
c
o
c
0>
o
c
o
o
cr>
Shift in baseline vs. temperature of Hg lamp
CL
o> O
if O
o
jr
o
o>
CD
cr
For ZAA gain to
detect Hg in 5 to
200 ppb range
4
8 10 12 14 16 18 20 22 24 26 28 30 32
Hg lamp temperature (°C) XBL793-868
>
£
CD
cn
o
QJ
C
a>
(/>
o
CD
Figure 8. Change in ZAA output voltage due to temperature-induced self
reversal in the absence of mercury in the sample gas. The lower
curve shows this effect in terms of baseline voltage. The upper
curve shows this effect in terms of apparent mercury concentration.
Both curves are normalized to 6°C.
billion (ppb) of mercury. If the lamp is operated at 25°C a variation of
±1°C produces a 6 ppb error. This will be significant for measurements
below 60 ppb. However, with the new PPL this problem has been eliminated by
simply enclosing the lamp in the water jacket assembly, described above, and
coupling it to a small constant-temperature bath mounted within the instru-
ment. The temperature of the PLL can be controlled to within ±0.2°C or ±0.5
ppb mercury. This is approximately equal to the mercury detection limit.
This type of temperature control of the light source was not possible with
the old EDL.
Another problem with the EDLs was the rf pickup in adjacent instruments
(e.g., thermocouples and flow transducers) due to the rf excitation of the
argon plasma. This problem has also been eliminated by the use of the PLL.
Overall, the mercury PLL offers a significant improvement in ZAA versatility
and performance.
Furnace—Absorption Tube Assembly
A new furnace for continuous online analysis of mercury in gas streams
has been constructed and successfully operated at 900°C for extended peri-
The furnace (Figure 9) is constructed of 1/2 in. o.d., 0.049 in. wall,
-------
Figure 9. ZAA furnace for on-line analysis of mercury in gas streams.
321 stainless steel (SS) tubing welded into a tee. Incoming gases first
pass through an atomization combustion chamber which is maintained at 900°C
by joule heating. This chamber is filled with ceramic beads to break up the
gas flow and increase the thermal contact area. The gases then pass through
a swall opening into an absorption chamber which is aligned along the opti-
cal path of the spectrometer. The temperature in this chamber is lower
since the current in each leg is one-half of the flowing through the atom-
iztion chamber. Quartz windows at the ends of the absorption chamber pass
the 253? A mercury resonance lines while isolating the hot sample gases from
the a«bient air. Gases exit the furnace through ports located near each end
of the absorption chamber.
Current and the mounting support for the furnace are supplied via
variable-cross-section strips of 304 SS welded to the tubing. When the
furnace is at operating temperature, the outer ends of these strips are
cool, thus preventing the buildup of resistive oxide layers on the power
connector surfaces.
^ presence of hydrogen sulfide in oil shale offgas and consequent
sulfication reactions may create a serious corrosion problem inside the
furnace. In an attempt to inhibit corrosion and maximize furnace lifetime,
aluminum has been diffused into the surface of the tubing and subsequently
oxidized by a process termed alonization. The resulting micro layer of
aluaina has been shown in laboratory and field experiments to reduce the
rate of corrosive attack to stainless steels.
Calibration System
A dynamic calibration system wlr:ch generates known concentrations of
•ercury vapor in a carrier gas will be used to calibrate the gas monitor.
This sytem, which is shown in Figure 10, is
described by Nelson.9 Heated air impinges on
based upon
the surface
the
of a
appartus
pool
118
-------
Valves
Dilution gas
(QD, TD, PD)
Mixing
C hnmber
Flow
meters
(Rotameters)
Carrier gas saturated
Air
Eq ui I i br i u m
vesse Is
XBL793-792
Figure 10. Schematic of mercury calibration system.
mercury warmed to about 60°C. The mercury-laden gas then travels through
two successive equilibration vessels; excess mercury condenses, and the gas
leaves the vessel saturated with mercury at an accurately determined tem-
perature. The saturated gas is then diluted with mercury-free gas and
introduced into the sample line. A range of concentrations is obtained by
varying the ratio of mercury calibration gas to dilution gas. With this
system it is possible to produce mercury concentrations which range from
0.01 mg/m3 (1 ppb) to approximately 20 mg/m3 (2 ppm).
Gas System
The following discussion describes the gas sampling-metering system,
develops the necessary calibration formulae, and summarizes the parameters
to be measured. The gas handling system is shown in Figure 11. Sample gas,
e.g., retort offgas of a given temperature, pressure, and mercury density
(T, P, p), enters the heated sample line at a volumetric flow rate, q. The
heated sample line is to be Jiaintained at approximately 200°C. At point A
f I r\ " r\ HA \ i.j-ill K(~*
-------
= ‘ Q (Retort offgas flow rate)
Calibration gas
(TapPc
(If
XBL 792—480
Figure Ii . Schernaiic of g s handJir. system for ZAA mercury monitor.
TABLE 1. REQUIRED MEASUREMENTS FOR GAS HANDLING SYSTEM
Temperature
Pressure
Flow
Offgas
T
P
.
Oxygen
TO
P 0
q 02
Calibration
gas
T
‘ c
Dilution gas
,
TD
D
q 0
Meter run
TM
M
q
Ambient conditions
Toom
atm
(T,P,p,q)
Oxygen (1 02 ,P 02 ,q 0 ,
valve
Meter
run
(T p
N N ,q
120
-------
The sample and calibration gases then pass into the furnace where they
are heated to a constant temperature (900°C) to atomize the mercury. The
density of mercury atoms in the furnace is then measured and converted into
a voltage response as described above.
It is essential that the furnace be maintained at a constant tempera-
ture between calibration runs since the voltage response of the ZAA to a
given concentration of mercury varies inversely with temperature. In order
to achieve the constant-temperature condition, volumetric flow rate through
the furnace (q ) must be held constant. This is to be accomplished by the
use of a flow controller. The controller system consists of a flow sensor,
which measures the flow downstream of a rotary vane pump, and a servo meter-
ing valve in parallel with pump which maintains the desired flow. Flow
readings q are for standard conditions. The flow controller will be cali-
brated with a wet-test meter located downstream of the controller. Periodic
calibration of the flow controller is necessary since the measurement of gas
flow by the sensing device depends upon the specific heat of the sample gas.
This will change during the course of a retort run. Table 1 summarizes the
gas parameters which must be monitored during the analysis of mercury.
To calibrate the ZAA, the voltage response of the instrument must be
related to a known density of Hg atoms (p 1 ) entering the furnace. To calcu-
late it cannot be assumed that the simple and calibration gases will be
at the same temperature initially. Therefore, the Hg densities in both
sample and claibration gases must be corrected for temperature differences.
In addition, dflution of sample gas by oxygen and calibration gas must be
determined. To calculate we assume that the ideal gas law adequately
describes changes of state in sample and calibration gas. Density and
volumetric flow will be converted to standard conditions (760 mm Hg, 0°C)
using Eqs. (1) .ind (2):
° T 760
p =p .— . — (1)
273 P
0
q —q . — —
T 760
I is temperature in °K; P is pressure in mm Hg p is the density of mercury
in mg Hg/m 3 ; q is the volumetric flow rate in m 3 /min; and the superscript
zero designates standard conditions.
The desnisty F entering the furnace is the sum of the flow-weighted
mercury densities in the sample and the calibration-oxygen lines,
q° q°
(3)
I 0 ‘-‘ 0
where q q° + a + q + q 2 .
121
-------
The measured quantities q , q and are converted to standard conditions
using Eq. (1) and the me sur d temperatures and pressures. The standard
flow rate of sample gas, q°, is not determined directly but is obtained by
difference between q and q° + q + q 2 .
With the calibration system turned on and the sample gas diverted, the
density of mercury entering the furnace becomes, from Eq. (3),
o__ 0 __________
q + q + q 02
The mercury density in the calibration gas at standard conditions is
calculated using Eq. (1):
I _
273
where p = (3.22x10 6 ) P(Hg) rng Hg
C
The measured temperature of the calibration gas is T , and P(Hg) is the
vapor pressure of mercury at obtained from standard Eables.
As noted above, a calibration curve can be obtained by varying the
mercury calibration gas and dilution gas ratio and recording the ZAA voltage
response. When the calibration system is turned off and the sample gas is
reintroduced, tne calibration curve is used to determine the unknown mercury
density p° in tne sample gas.
However, during analysis of the sample gas, the mercury density enter-
ing the furnace (p ) must be corrected for dilution by 02 introduced at
point A. From Eq. (3), we have:
p — p° q° (5)
q° + q 02
An alternate calibration procedure is to inject the calibration gas
directly into the sample gas. The concentration p in this case is given by
Eq. (3). If matrix effects are a problem, this method will be used to
determine the unknown concentration in the sample gas by standard additions.
SUMMARY
This paper describes a technique to continuously measure total mercury
in a gas stream in the presence of high concentrations of organics, smoke,
oil mist and other interfering substances. The technique employees Zeeman
atomic absorption spectroscopy as the mercury detector, which has been
successfully used to measure mercury in oil shale 0 offgases. The instrument
consist of a light source which provides the 2537 A mercury emission line; a
122
-------
furnace-absorption tube assembly where the sample is vaporized and swept
into the light path and a detector which converts the signal into an ac
voltage for processing. Sample gas is heated to 900°C in the furnace-
absorption tube assembly aligned with the optical axis of the ZAA spectrom-
eter. The 2537 A mercury emission line (it) and a reference line (r) are
generated by a single discharge lamp operated in a 15 kG magnetic field.
The difference between the it and u components is taken by a lock-in-
amplifier and converted to a signal which is proportional to the amount of
mercury in the gas.
ACKNOWLEDGEMENTS
This work was supported by the Environmental Protection Agency under
contract No. 68-03-32667 and by the Division of Environmental Control Tech-
nology of the U.S. Department of Energy under contract No. W-7405-ENG-48.
REFERENCES
1. Fox, J.P., J.J. Duvall, K.K. Mason, R.D. McLaughlin, T.C. Bartke, and
R.E. Poulson, “Mercury Emissions from A Simulated In Situ Oil Shale
Retort,” In: Proceedings of 11th Oil Shale Symposium, Golden,
Colorado, April 1978.
2. Fruchter, J.S., J.C. Laul, M.R. Petersen, and P.W. Ryan, “High Preci-
sion Trace Element and Organic Constituent Analysis of Oil Shale and
Solvent-Refined Coal Materials,” In: Symposium on Analytical Chemistry
of Tar Sands and Oil Shale, ACS, New Orleans, March 1977.
3. Poulson, R.E., J.W. Smith, N.B. Young, W.A. Robb, and T.J. Spedding,
“hinor Elements in Oil Shale and Oil-Shale Products,” LERC RI 77-1,
1977.
4. Bertine, K.K., and E.D. Goldberg, “Fossil Fuel Combustion and the Major
Sedimentary Cycle,” Science, 173: 223, 1971.
5. Klein, D.H. , A.W. Andren, J.A. Carter, and others, “Pathways of Thirty-
Seven Trace Elements Through Coal-Fired Power Plant,” Env. Sci. and
Tech. 9: 973, 1975.
6. Hadeishi, T., and R. McLaughlin, “Zeeman Atomic Absorption Spectrosco-
py, LBL-8O31, 1978.
7. Hadeishi, T., and R. McLaughlin, “Isotope Zeeman Atomic Absorption; A
new approach to chemical analysis,” American Laboratory, August 1975.
8. Hadeishi, 1. , “Isotope-shift Zeeman Effect for Trace-Element Detection:
An Application of Atomic Physics to Environmental Problems,” Appl.
Phys. Lett. 21: 438, 1972.
9. Nelson, G.O. , “Simplified Method for Generating Known Concentrations of
Mercury Vapor in Air,” Rev. Sci. Instr. 41: 776, 1970.
:1.23
-------
A SAMPLING AND ANALYSIS PROCEDURE FOR GASEOUS SULFUR COMPOUNDS
FROM FOSSIL FUEL CONVERSION
S.K. Gangwal, D.G. Nichols, R.K.M. Jayanty,
D.E. Wagoner, and P.M. Grohse
Research Triangle Institute
Post Office Box 12194
Research Triangle Park, North Carolina 27709
ABSTRACT
For some three decades, several alternative processes to convert fossil
fuels to liquid and gaseous fuels have been under development. These proc-
esses generate, among other effluents, a gas stream containing hydrogen,
carbon dioxide, carbon monoxide, hydrocarbons, and sulfur compounds. A
sampling and analysis methodology is described for reactive gaseous sulfur
compounds including hydrogen sulfide, carbonyl sulfide, sulfur dioxide,
methyl mercaptan, ethyl mercaptan, carbon disulfide, and thiophene contained
in gas streams as those from developmental processes. An all-glass system
is used to collect and store the gas samples. An all-Teflon gas chromato-
graphic analysis system utilizing a thermal conductivity and a dual-flame
photometric detector is used for the analysis. Quality control procedures
for accurate quantification are described. Typical chromatograms are shown,
and conditions for analysis are listed.
INTRODUCTION
In recent years, considerable research has been devoted to developing
efficient ways to convert fossil fuels--coal and oil shale--to more suitable
forms of energy. In 1926, about 150 manufacturers were producing gas from
coal worldwide, with about 12,000 units operating in the United States. The
availability of clean natural gas and pipeline systems led to the demise of
these gasifiers. With the project.ed energy shortage, interest in coal
gasification has revived. Some 68 different gasification processes have
been identified. Six leading units include the Lurgi, Weliman-Galusha,
Woodall-Duckhamn, Koppers-Totzek, Winkler, and Chapman-Wilputte. Second
generation processes for the production of synthetic fuel gas from coal
include the Hygas, Bigas, and Synthane Processes.
With regard to oil shale, some eight projects are at various stages of
development for the commercial production of synthetic crude oil in Colorado
and Utah. Additionally, at least 14 major research, pilot plant, or demon-
stration projects are currently underway throughout the United States.’ 2
124
-------
The prelia inary outlook is bright for the developmental coal and oil
shale conversion processes because respectable recovery of the heating value
can be made without undue technical problems. However, various environmen-
tal constraints remain to be fully investigated. 3 Of concern to this study
is the environmental impact posed by sulfur-containing compounds in the
product gas.
It is instructive to compare typical coals and oil shales. Table 1
shows the sulfur levels in the raw fuels and H 2 S and COS levels in the gas
streams obtained from the processes mentioned. The data indicate that a
procedure capable of measuring sulfur compounds over a wide range is needed
for use in fossil fuel conversion processes. Additionally, a dearth of
information, especially for shale gas, exists in the literature concerning
the content of sulfur compounds other than 11 2 S and COS. Of particular
concern are mercaptans, carbon disulfide, and thiophene.
Drabkin 4 used an elaborate scrubbing and colorimetric scheme to deter-
mine these compounds in gas from a Russian shale containing 2 percent
sulfur. In recent years, however, gas chromatography-flame photometric
detection (GC-FPD) has become a very popular method for individual sulfur
compound determinations 6 7 8 9 because of its speed, specificity, and sensi-
tivity. However, many difficulties can arise when the gases are present in
widely differing concentration levels. H 2 S is the predominant sulfur gas
from high—sulfur fuels, but it is present in such concentrations that the
TABLE 1. SULFUR IN RAW FUELS AND PRODUCT GASES
Sulfur,
wt
%
Colorado Green
River+ (eocene)
Shale
0.5
Michigan+
Antrim
(Devonian) Shale
3.5
Illinois
No. 6 Sub
Coal
3.0
Wyoming
bituminous
Coal
0.6
H 2 S,
ppm
<100+
4,000
12,000
900
COS,
ppm
<100
1,000
40
30
Process
Ex situ 10 ton
batch retort
Lx situ 10 ton
batch retort
Low Btu semi-
batch gasifi-
cation*
Low Btu semi-
batch gasifi-
cation*
Site
LETC
LETC
RTI
Run 23
Rh
Run 35
LETC--Laramie Energy Technology Center.
Rh--Research Triangel Institute.
*H 2 S and COS levels are integrated averages over the duration of the batch
process.
+As reported in Reference 5.
Not indicated by mass spectrometer.
125
-------
FPD will saturate even with small samples. Microsamples can be used, but
then the detectivity of other gases will be lost. Other problems associated
with the FPD are its nonlinearity, compound dependency, and reduced response
because of an interfering hydrocarbon matrix eluting with the sulfur spe-
cies. 1 ° 11 12 Problems are associated with the GC system as well, includ-
ing strong absorption of the trace sulfur gases on the column packing and
reaction on the walls of the columns and the sampling devices.
The purpose of this paper is to present a reliable and efficient method
that can be readily applied to measurement of H 2 S, COS, CH 3 SH, C 2 H 6 S, CS 2 ,
and thiophene in the off gas from fossil fuel conversion processes. A
dual-column, dual-detector GC system has solved many problems mentioned
above.
EXPERIMENTAL
Sampling System
Sulfur gases can be sampled from the process under study using the
afl—glass Teflon system shown in Figure 1. A pressure letdown device is
required only when the gases are to be sampled from a high-pressure zone.
An all-glass Teflon system is necessary to prevent degradation of the
reactive sulfur compounds. The storage containers (Figure 2) have two
high-vacuum stopcocks so that sample dilution can be prevented if repeat or
additional analyses are required. Samples are stored in a temperature-
controlled box (50°C) until ready for analysis (Figure 3). The box provides
safe storage and transport. An estimated moisture content in the samples
for which this procedure was effective was 0.8 percent or less.
GC Sample Injection and Analysis System
A Varian 3700 gas chromatograph (GC) equipped with a thermal conduc-
tivity detector (IC) and a dual-flame FPD was used in this study. Modifi-
cations were made to the pneumatics to allow for gas sample injection at
subatmospheric and superatmospheric pressure. A 6 ft. by 1/8 in. Teflon
(FEP) column packed with Carbopack B/1.5% XE-6011% H 3 P0 4 obtained from
Supelco, Inc., was used with the FPD. Grade 0.5 helium and grade 0.1 hydro-
gen obtained from Airco were used as carrier and fuel gases. A Bendix clean
air system was used to provide dry hydrocarbon-free air to the FPD. Gas
flows were controlled with both pres ure regulators and flow control needle
valves. A 6 ft. by 1/8 in. Porapak-N column was used with the IC detector
for H 2 S and COS analyses at high levels (>400 ppm). Standard mixtures
containing certified amount of H 2 S, COS CS 2 , CH 3 SH, C 2 H 6 S, and thiophene,
individually, in N 2 were obtained from Scott Environmental Technology, Inc.,
for detector calibrations. Conditions of analysis are summarized in Table
2.
A simplified schematic of the GC injection system is shown in Figure 4.
The sample injection valve was made of inert high-nickel hastalloy-C. The
Heise gauge has a range from -760 to 1500 mm Hg gauge in 2 mm graduations.
The vacuum pump was capable of evacuating the system to 10 torr. An
.1
-------
ALL GLASS
/
1. Source
2. Pressure Letdown Device
3. Glass, Entrained Moisture, Trap
4. 1 in Glass Manifold
5. Glass Sample Storage Container
with Two High-Vacuum Stopcocks
(Figure 3)
Drierite Moisture Traps
Valve
Pump
Dry Gas Meter
Pressure Gauge (0-15 psig)
Corrugated Teflon Tubing
Figure 1. Schematic of sampling system.
2
10
4
/
1
7
3
11
5
J
6
9
8
6.
7.
8.
9.
10.
11.
Figure 2. Glass containers with 0-4 mm ultratorr stopcocks.
127
-------
Figure 3. Constant temperature sample storage box.
128
—
I.
-------
TABLE 2. CONDITIONS FOR ANALYSIS
FPD IC
Column Carbopack B/1.5% x E60/1% H 3 P0 4 Porapak N, 6 ft x 1/8 in Teflon
6 ft x 1/8 in Teflon
Helium 30 mi/mm 30 mi/mm
carrier
Detector 150°C 200°C
temperature
Column 50°C for 2 mm 70°C
temperature 30°C/mm to 130°C Isothermal
Hold 5 mm
additional sample injection valve (not shown) was used for the second column
and detector. For the IC detector, a column backfiush valve (not shown) was
used to prevent previous sample analyses from interfering with the sample
being analyzed. All gas lines were Teflon; a 1-mi Teflon sample loop was
used, and all fittings from the sample bottle to beyond the sample loop were
glass or Teflon. All valves were installed in heated ovens, and sample
transfer lines were kept to minimum practicable length. Dead volumes were
kept to a minimum.
Procedure
A diagram of the FPD explaining the two air supplies is given by
Patterson et al. 13 Gas flow rates for maximum sensitivity in the dual-flame
mode were He: 30 ml/min; H 2 : 140 mi/mm; air 2: 80 ml/min; and air 1:
166 ml/min. The detector could also be used in the single-flame mode by
shutting off air 2. For maximum sensitivity, air 1 then had to be increased
to 205 mi/mm.
Prior to injection, the sample loop and connection were adequately
flushed with the sample. The desired amount of sample, measured in terms of
absolute pressure, was then trapped ir the loop and injected with the valve.
An absolute pressure range of 50 mm Hg to 1,200 mm Hg was used. It was
possible to generate calibration curves from a single standard using differ-
ent pressures. Peak areas were measured using an HP 3352 laboratory comput-
er.
For the thermal conductivity detector, a linear calibration was
observed over the range of H 2 S and COS concentrations studied. For the FPD,
129
-------
a log—log calibration was necessary, both in the single- and dual-f lame
modes. Typically, the following relationship was observed.
(1)
A = peak area
C = constant
S = sulfur mass or concentratior
n = exponent (1.67 to 2 depending on compound) COS; 1.82, CS 2 ;
1.83, thiophene; 1.67, CH 3 SH; 2.00, C 2 H 6 S; 1.9 in the dual-
flame mode for conditions listed in Table 2.
Industrial samples obtained from various fuel conversion facilities
were analyzed using the above procedures. Subatomspheric injection was
termendously advantageous because of the ease with which the sample size
could be varied when required, and calibrations could be performed for each
compound.
RESULTS AND DISCUSSION
Figure 5 demonstrates the repeatability of the analysis system. A
relative standard deviation of less than 3 percent was obtained on a sample
size of about 1 ng sulfur (1.12 ppm SO 2 in 1 ml loop at ambient conditions).
Figure 6 shows a typical FPD calibration plot on log-log paper.
Similar calibrations were performed for other sulfur species. The
single-flame mode on the FPD was found to be about 1.5 times more sensitive
than the dual-flame mode. The ordinate of area//height instead of area is
shown to collapse several sulfur species into one curve. The abscissa could
also be plotted as (ppm x mm Hg) instead of ng S, since both are proportion-
al according to the ideal gas law.
Figures 7 and 8 show the desired separations of actual samples obtained
from the RTI coal gasifier. Similar compounds are expected to be present in
shale gas. This is demonstrated in Table 3 where sulfur compounds in gases
from three processes have been sampled and measured using the procedure
described. The Michigan shale has a high sulfur content resulting in high
amounts of sulfur emissions. The analysis is not representative of low-
sulfur shales like those from Green River, etc. Qualitatively, however,
similar compounds are expected.
QUALITY CONTROL
The sulfur compounds must be stable in the glass containers until
analyzed. This was tested for samples obtained from coal gasification
(Table 4). As seen, the concentration change is generally less than 5
percent over a period of more than 100 hour. Stability of some individual
sulfur species in the glass containers in the presence of ambient air was
also tested (Figure 9) down to less than 1 ppm. A stepwise dilution of
partially evacuated glass bulbs with ambient air was carried out and follow-
ed by a series of measurements over a period of several hours to determihe
130
-------
VALCO HASTALLOY-C NUPRO STAINLESS
VALVE TO GC COLUMN
STEEL TOGGLE VALVES
RESSURE GAUGE VACUUM PUMP
VALVE
SAMPLE
GAS
BOTTLE
Figure 4. Schematic of CS injection system.
U
4 C .1 0 ) (V)
C .1 N C) C ) C)
L )
N N N N
U)
I-
H 2
0
C.)
w
I II
I I
I I!
I>-
I I II
I I
H I I.
I I
u U LI NJ
Figure 5. RepeatabilitY of system for 1 mL-sample of 1.12 ppm SO 2
in the dual-flame mode.
131
-------
oo
5 x 104 r-
2 x 104
104
5 x 103 [~
2 x
c
IS
M
>
103
5 x 102 _
2 x 102
102
I
52
. , P
P
O H2S
D cos
x §o2
0.1 0.2 0.5 1 2
Figure 6. Nanograms sulfur vs.
D
O
NANOGRAMS S
D
rfo
CKO
SLOPE = 0.92
CORRELATION
COEFFICIENT = 0.9975
1 i
5 10 20 50 102 2 x 102 5 x 102
for H2S, COS, and SO2 in the dual-flame mode.
-------
1 23
00
CO
COLUMN: 6 ft x 1/8 in PORAPAK N
TEMPERATURE: 70° C
HELIUM FLOW: 30 mL/min
DETECTOR: TC
SAMPLE: RED ASH ANTHRACITE COAL
GASIFICATION PRODUCT GAS
340 mm HG ABSOLUTE
1. HYDROGEN
2. AIR + CO
3. CH4
4. CO2
5. H2S 7
6. COS
7. BACKFLUSH
6
A
INSTRUMENT: VARIAN 3700 GC
COLUMN: 6 ft x 1/8 in TEFLON
CARBOPACK B/1.5./.XE60/1 Y.H3PO4
TEMPERATURE: 50°C FOR 2 min
30°C/minTO 130°C;
HOLD 4 min
CARRIER GAS: HELIUM 30 mL/min
FPD:
AIR 1:
AIR 2:
H2:
1:
2:
3:
4:
5:
6:
DUAL FLAME
166 mL/min
80 mL/min
1 40 mL/min
H2S
COS
CH3SH
C2H6S
CS2
THIOPHENE
SAMPLE: ILLINOIS NO. 6 COAL
GASIFICATION SAMPLE: 1 mL
AT 160 mm HG ABSOLUTE
8 min
Figure 7. Analysis of H2S and COS.
Figure 8. Separation of gaseous
sulfur compounds.
-------
TABLE 3. ANALYSES OF SULFUR COMPOUNDS IN OFFGASES
FROM FUEL CONVERSION FACILITIES
Fuel
Illinois
No. 6 Coal
Devolatilized
W. Kentucky/#1
Coal
Michigan
Antrim Shale
Sulfur wt %
3.0
2.9
3.5
H 2 S (ppm)
12,000
5,000
7,100
COS (ppm)
50
280
600
CH 3 SH (ppm)
20
<1
40
C 2 H 6 S (ppm)
10
<1
30
CS 2 (ppm)
4
2
270
Thiophene (ppm)
120
‘ .0.4
17
Site
Rh test #6
NCSU
LETC
Process
Semibatch
Continuous
Oil shale batch
gasification
fluid bed gasification
retorting
NCSU--North Carolina State University coal gasification facility.
stability. The solid and dashed lines represent calculated values from
dilution factors whereas the discrete points represent measured values.
Agreement is generally good except for H 2 S at sub-ppm levels. The objective
of these measurements were to test the validity of the described sampling
and analysis procedure for both sources (concentrate), as well as fugitive
emissions (dilute) from fossil fuel ccnversion facilities.
Log-log plots must be used for calibration of the FPD. Use of the
square root signal linearizer built into commercial FPD electrometers is not
recommended since, as given earlier, the exponent of equation (1) is not
always 2. As demonstrated by Farwell and coworkers, 14 the use of the square
root mode could easily result in as much as 70 percent error. Individual
calibration is recommended for each sulfur compound, aith_ough reasonable
determinations can be made by using calibration plot of A,/H vs. ng S or ppm
by volume as shown in Figure 5, since it collapses several sulfur species
into one plot.
A final problem with FPD, as mentioned in the introduction, is the
quenching effect of hydrocarbons on sulfur response. The dual-flame FPD
minimizes this problem, as claimed by Patterson et al.,’ 5 by separating the
regions of sample decomposition and light emissions. This was tested on a
coal gasifier product gas sample containing a large background of hydrocar-
bons (sample was obtained during the pyrolysis period), which was analyzed
in the two modes. Figure 10 shows the analysis. At the same attenuation,
no CS 2 response is seen in the single-flame mode even though a larger thio-
phene response is obtained. FID response under identical conditions shows a
large hydrocarbon background as CS 2 elutes.
134
-------
TABLE 4. SAMPLE STABILITY CHECK
Sample No.*
Time
(hr)
COS
(ppm vol)
CH 3 SH C 2 H 6 S
(ppm vol)(ppm vol)
CS 2
(ppm vol)
Thiophene
(ppm vol)
Si
3
550
39
20
230
250
(7 mm from
28
540
37
19
220
250
coal drop)
49
74
98
123
147
530
530
540
530
510
37
36
38
36
35
19
19
20
19
18
220
220
220
220
210
250
240
250
240
230
S2
3
440
17
9
140
150
(16 mm)
28
49
74
98
123
147
420
420
420
430
410
410
16
15
16
16
15
16
9
8
9
7
7
6
140
130
140
140
130
130
150
150
150
150
140
140
S3
3
150
(86 mm)
28
49
74
74
98
123
147
140
140
130
130
140
130
130
<1
<1
<1
<1
*Samples obtained from low-Btu semibatch gasification of W. Kentucky #9
coal.
135
-------
0
oc
40 60 80 100
TIME (HOURS)
120
Figure 9. Stability of low molecular weight sulfur species in glass containers
in presence of ambient air.
x SO 2
0 H 2 S
VALUES
SO 2
L_
0
o cos
o CH 3 SH
CALCULATED
FROM
DILUTION
FACTORS
H 2 S
---
— — . - CH 3 SH
I I
1_
0
LU
-J
0
>
>.
2
0
-J
-J
LU
U)
I-
100
90
80
70
60
50
40
30
20
10
9
8
7
6
5
4
3
2
1
.9
.8
.7
.6
.5
.4
.3
.2
.1
A
x
0
a
0
0
0
0
0
20
140
136
-------
3
COLUMN: 6 in x 1/8 ft TEFLON
CARBOPACK B/H 3 P0 4 /XE 60
TEMPERATURE: 500 C FOR 2 mm
PROGRAMMED TO
130° C AT 30°C/mm
28 mL/min HELIUM CARRIER GAS
1. COS
2. METHYL MERCAPTAN
3. THIOPHENE
in
-
SINGLE-FLAME
RESPONSE
NO CS 2 RESPONSE
8 mm
DUAL-FLAME
RESPONSE
FID
RESPONSE
Figure 10. Comparison of dual- and single-flame modes for
a coal gasifier product gas.
in
3
in
137
-------
Another way to check the hydrocarbon effect is to analyze the same
sample repeatedly and see if identical responses are obtained. The hydro-
carbons eluting from previous injections could quench the response for the
sample being analyzed. For the dual-flame mode, repeated analysis of the
same sample gave identical response. On the other hand,. it was not possible
to reproduce the response when the same sample was repeatedly analyzed in
the single-flame mode. This was attributed to quenching because of hydro-
carbons eluting from previous analyses. As much as 75 percent relatively
different responses have been observed for CH 3 SH, C 2 H 6 S, and CS 2 present in
samples analyzed repetitively. A backflush following each sample would
reduce the errors involved, but this, of course, adds to the time and cost
of analysis. Even when the backflush is added, the response will still be
nonuniform, depending on the amount cf hydrocarbons present in the sample.
Thus, the dual-flame detector is a suitable choice for samples from fossil
fuel conversion even though its minimum detection limit is not as good as
the single-flame detector. However, for fugitive or ambient samples where
lower detection limits are required, the single-flame detector could be more
suitable.
ACKNOWLEDGMENTS
Financial support from the Industrial Engineering Research Laboratory,
U.S. Environmental Protection Agency, Research Triangle Park, North Caro-
lina, under Grant No. R804979010 and Contract No. 68-02-2156, is gratefully
acknowledged. The authors thank Dr. J. Ferrell of North Carolina State
University and Mr. Richard Martel of LETC for providing samples for this
study and are grateful to Dr. R. Poulsen of LETC for reviewing the manu-
script.
REFERENCES
‘1. Yen, T.F. (ed). Science and Technology of Oil Shale. Ann Arbor, Ann
Arbor Science Publishers, Inc., 1976. p. 47f.
2. Hendrickson, l.A. Synthetic Fuels Data Handbook. Cameron Engineers,
Inc. Denver, Colorado. 1975.
3. Jones, D.C. et al. Monitoring Environmental Impacts of the Coal Oil
Shale Industries--Research and Development Needs. U.S. Environmental
Protection Agency. EPA-600/7-77-015. February 1977.
4. Drabkin, A.E. Chemistry and Technology of Combustible Shales and Their
Products. NSF and U.S. Department of Interior, U.S. Department of
Commerce. Washington, D.C., Publication 6. 1962.
5. Martel, R.A., and A.R. Harak. Preliminary Results from Retorting
Michigan Antrim Shale. LERC/TPR-77/1. July 1977.
6. Brodey, S.S., and Chaney, J.E. Flame Photometric Detector: the Appli-
cation of a Specific Detector to Phosphorus and Sulfur Compounds Sensi-
tive to Subnanogram Quantities. J Gas Chromatogr. 4:42-46. 1966
-------
7. Stevens, R.K., J.K. Mulik, A.E. O’Keefe, and K.J. Krost. Gas Chroma-
tography of Reactive Sulfur Gases in Air at the Parts-Per-Billion
Level. Anal. Chem. 43:827-32. 1971.
8. Desouza, T.L.C., D.C. Lane, and S.P. Bhatia. Analysis of Sulfur-
Containing Gases by Gas-Solid Chromatography on a Specially Treated
Porapak QS Column. Anal Chem. 47(3):543-45. 1975.
9. Pearson, C.D., and W.J. Hines. Determination of Hydrogen Sulfide,
Carbonyl Sulfide, Carbon Disulfide, and Sulfur Dioxide in Gases and
Hydrocarbon Streams by Gas Chromatography/Flame Photometric Detection.
Anal. Chem. 49(1):123-26. 1977.
10. Farwell, S.0., and R.A. Rasmussen. Limitations of the FPD and ECD in
Atmospheric Analysis: A Review. J Chromatogr. Sci. 14:224-34.
11. Burnett, C.H. , D.F. Adams, and S.0. Farwell. Relative FPD Responses
for a Systematic Group of Sulfur Compounds. J CHromatogr Sci.
16:68-73. 1978.
12. Mizany, A.E. Some Characteristics of the Melpar Flame Photometric
Detector n the Sulfur Mode. J Chromatogr Sd. 8:151-54. 1970.
13. Patterson, P.L., R.L. Howe, and A. Abu-Shumays. Dual-Flame Photometric
Detector for Sulfur and Phosphorous Compounds in Gas Chromatographic
Effluents. Anal Chem. 50(2):339-44. 1978. -
14. Burnett, C.H., D.F. Adams, and S.0. Farwell. Potential Error in
Linearized FPD Responses for Sulfur. J Cromatogr Sci. 15:230-32.
1977.
15. Patterson, P.L. et al. Comparison of Quenching Effects in Single and
Dual Flame Photometric Detectors. Anal Chem. 50:345-48. 1978.
139
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FUGITIVE DUST AND OFFGAS ANALYSIS METHODS
APPLIED AT THE PARAHO FACILITY
J.E. Cotter
TRW Inc.
Environmental Engineering Division
R.N. Heistand
Development Engineering, Inc.
Anvil Point, Colorado
INTRODUCTION
BACKGROUND
An environmental assessment of oil shale processes was recently
completed for the USEPA by TRW. As part of the assessment effort, TRW
conducted a sampling and analysis program at the Paraho oil shale demonstra-
tion plant in Anvil Points, Colorado, in 1976.’ This work was done in close
cooperation with the Paraho operating company, Development Engineering, Inc.
(DEl).
A strong recommendation resulting from this prior work was that a
comprehensive fugitive dust survey should be conducted at the Anvil Points
site, in anticipation of future studies for dust control related to mining,
crushing, and material handling operations. In addition, retort offgas
analyses were also recommended as a first step in characterizing retort gas
as a potential fuel, and its combustion products.
AIMS OF THE TEST PROGRAM
The fugitive dust program objectives included: 1) determining the
sources of fugitive dust; 2) noting related meteorological characteristics;
3) quantitatively evaluating the total suspended particulates (TSP) over and
above natural background TSP values at various distances from the dust
sources; and 4) determining particulate size distribution at the TSP
measurement locations.
In addition to the mass measurements, chemical composition of 1 iartic-
ulate matter was also defined as a program measurement objectivt. Both
inorganic elemental analysis and organic classifications were sought. These
constituent analyses helped to further characterize particulate iatter, and
they also provided useful clues concerning the particulate-generating
sources.
-------
The offga measurement objectives incorporated the quantitative deter-
mination of organic constituents (C 1 -C 12 ), combusion products, nitrogen-
based constitutents, sulfur-based constituents, and volatile trace elements.
THE PARAHO PROCESS
The demonstration plant operations, indicated schematically in Figure
1, consisted of mining, raw shale hauling, crushing and screening, retort-
ing, and retorted shale disposal. Crude shale oil was stored in tanks for
subsequent shipment to an offsite refinery. The heart of the demonstration
plant is the Paraho retort (Figure 2), which can process about 400 metric
tons per day.
Provision has been made for operating the retort in either the direct
mode or indirect mode. In the direct mode the carbon on the retorted shale
is burned in the combusion zone to provide the principal fuel for the
process. Low calorie retort gases are recycled to both the combustion zone
and the gas preheating zone. In the indirect mode heat for retorting is
supplied by recycling offgases through an external furnace, thus eliminating
combust.ion in the retort and producing a high heating value, 8000 kcal/std
cu meter offgas.
In either mode of operation, raw shale is fed into the top of a Paraho
retort and passed downward by gravity successively through a mist formation
and preheating zone, a retorting zone, either a combustion zone (direct
mode) or heating zone (indirect mode), and finally, a residue cooling and
gas preheating zone. It is discharged through a hydraulically-operated
grate, which controls the throughput rate and maintains even flow across the
retort. The retorted shale is discharged from the retort at about 150°C
(300°F), and sent to the shale disposal area.
The shale vapors produced in the retorting zone are cooled to a stable
mist by the incoming raw shale (which is thereby preheated), and leave the
retort. This mist is sent to a condenser, and finally a wet electrostatic
precipitator, for oil separation. The resulting shale oil is transported to
storage.
The demonstration plant differs considerably from a commercial facility
design, so that it cannot be considered a scale model of a full-size opera-
tion. The product gas at the demonstration plant was combusted in a thermal
oxidizer prior to atmospheric discharge; in a commercial facility this gas
would be cleaned and used as a fuel in process heaters and boilers. Materi-
al handling in a commercial plant would most likely rely on conveyors,
rather than trucks, and the disposal of retorted shale would be a major
portion of the operation.
141
-------
Figure 1. Schematic of Anvil Points Mining and Material Handling Operations.
• .
:
r’
-------
PARAHO RETORTING, DIRECT MODE
Rotating spreader
Collecting tubes
Mist formation
and preheating
Distributors
Retorting zone
Distributors
Combustion zone
Residue cooling and
gas preheating
Moving grates
Retorted shale to disposal beds
Feed shale
Gas and oil mist
L
: ‘
(A)
Product
gas
Oil-gas separator
Shale oil
Gas-air
mixture
I
Recycle gas
blower
Air blower
Figure 2. Schematic of Paraho Retort.
-------
SAMPLING AND ANALYSIS PROGRAM
Fugitive Dust Program Execution
The principal dust collection devices were high-volume samplers
(General Metal Works Models 2000 and 2310). These were supplemented, as
required by the test plan, by cascade impaction samplers (Sierra Instru-
ments, Model 235) determining particle size distribution. The sampling
locations and area designations are given in the local contour map
(Figure 3) and the test matrix (Table 1) respectively. As indicated in
Table 1, dust collection took place in the vicinity of mining, hauling,
crushing and discharging operations. The Anvil Points mine ventilation
system consists of fresh air forced through one adit, circulation to the
back of the mine, and exhaust from two remaining adits. High-volume
samplers were used at the mine mouth for the fugitive dusts carried out in
the exhaust air through the two adits. Except for the mine, meteorological
instrumentation was also provided at each collection location to contin-
uously record wind direction and velocity.
The .sampling schedule was arranged for a continuous 4-week effort, in
an attempt to include some statistical variation of sample characteristics
during the course of the program. As indicted by the test matrix (Table 1),
measurements in the vicinity of the crusher were not emphasized. The crush-
ing equipment furnished by the U.S. Mavy, was used as an expedient during
the limited duration research and development program conducted by DEl.
TABLE 1. TEST MATRIX
No. sampling Total No. Total No. Total No.
Sources locations samples for size inorganic and
TSP distribution organic analyses
(Each category)
Mine adits 2 40 8 4
Haul road 3 90 12 6
Crushing area 3 15 6 3
Spent shale transfer 3 30 12 6
The required complement of high-volume samplers, portable generators,
and meteorological instruments were deployed at the Anvil Points site
according to the final test plan. High-volume samplers are shown positioned
at the retorted shale transfer area (Figure 4) and adjacent to the haul road
(Figure 5). The collectors located near each source consisted of a one
upwind-two downwind configuration, with the exception of the mine mouth. As
in most mountain valley terrains, there was a strong upslope wind during
midday, and patterns were variable, so that close surveillance was required
144
-------
Figure 3. General Locations of Fugitive Dust Sampling.
145
S ha
-------
*
S p
Figure 4.
High-Vol Samples at Retorted Shale Disposal Area.
146
4
.. , : •
-------
0
Figure 5.
High-Vol Samplers and Meteorological Station Near Haul Road.
147
-------
in order to det ermine when a collector was in an upwind or downwind posi-
tion, and manual switchovers were done as required.
Data Reduction and Quality Assurance for Dust Sampling
The period of sampling varied, depending on the amount of sample
desired and proximity to a source. Although a nominal 1-hour sampling
period was usually sufficient, rough filter weighings in the field were used
to provide assurance that sample catches were sufficiently large to provide
accurate weighings and analyses. The three-sampler sites had one unit
upwind (approximately 20 meters) and two units downwind (approximately 10
and 50 meters, depending on the site and sample catch). The two downwind
samplers were approximately on the downwind axis.
Records of mine and plant activity were kept by the field crew for each
sampling site. In particular, mining activities, blasting, haul truck
operations, and crushing operations were logged, since all of these activi-
ties were intermittent or variable. This information was recorded on the
same data sheets as the high-volume unit records.
Filters were removed from the high-volume samplers and cascade impac-
tors after each test and sealed in polyethylene bags. The bags were placed
in an envelope, with the location anc field data recorded on the envelope.
The basic record number for each sample was the filter sequence number,
which was printed on each filter. Therefore, the results associated with
each sample were directly traceable back to the filters, which were put into
storage unless they were consumed in a subsequent analysis step.
As already noted, the key to assuring continued operation of both
high-volume samplers and meteorological instrumentation was close surveil-
lance by the field crew. The air flow rate through the high-volume units
was recorded from rotometers at the beginning and end of each sample collec-
tion. Local temperatures and pressures were recorded to correct the average
actual flow rates to standard conditions. The rotometers were calibrated
against a calibrated orifice meter at the start of the field testing
program.
Total suspended particulate (TSP) values were determined for each
sample collected from stabilized filter weights, sample time, and corrected
sampler air flow rate. Before-and-after weights were taken under controlled
temperature and humidity conditions. The cascade impactor filters were
weighed under controlled conditions as well, and particulate size breakdown
determined from impactor calibration curves, down to about the O.Sp (and
less) cutoff. A five-stage impactor as used. Fiberglas filters were used
for TSP determinations and (in some cases) subsequent organic analysis.
Figergias filters have excellent weight stability, since water absorption is
negligible. Since Fiberglas filters cannot be decontaminated well enough
for good elemental analysis, Whatman paper filters were used for inorganic
determinations. There is a trade-off involved in this choice, since paper
filters are difficult to stabilize and the resulting net sample weights are
probably less accurate than samples collected on Fiberglas.
148
-------
Organic ar alyses require large (1 to 10 g) samples to be quantitativ ,
especially for spent shale (which has the lowest organic content). There-
fore, the sampling time for organic analysis samples was considerably longer
than other samples. In some cases with a 24-hour source operation, the
high-volume units were run overnight to collect a sufficient sample mass.
Locating a sampler within a few meters of the dust source was another tech-
nique used to increase sample size.
Meteorological data were recorded on strip charts, and average condi-
tions were manually interpreted for each hour of operation. Wind direction
and air temperature were directly recorded, while average wind speed was
calculated from the “wind run” trace (which is an integrated wind speed).
The final quality assurance step was data validation by comparison with
similar samples. The size of the data base obtained from the test program
was large enough to allow these comparisons to be useful. In some cases,
for example, the appearance of insufficient sample size was confirmed by
gross variances in analytical results. Accuracy estimates for analysis
methods were used to determine the statistical validity of analytical
results.
Offgas Sampling Execution
Gas samples for field analysis were taken using an integrated gas
sampling train. Samples were drawn through a stainless steel probe to an
ice bath condenser by means of a small diaphragm pump, and then metered into
a Tedlar bag. At the conclusion of the sampling, the bag was sealed and
transported to a mobile lab for analysis of inorganics (C0 2 , °2, N 2 , CO,
502, NON) and light hydrocarbons (C 1 -C 5 ).
Standard EPA absorption train methods (Figure 6) were used for some
sulfur-based constituents (H 2 S, SO 2 , SO 3 ). Other constituents for which
selective absorption was attempted included NH 3 , arsenic, and mercury.
Total sample volumes for the various tests ranged from 0.02 to 0.2 cubic
meters at 20°C.
The particulate matter in the gas discharge after passing through a
thermal oxidizer was captured with a high volume (0.1 std cu meters/mm)
Source Assessment Sampling System (SASS), shown schematically in Figure 7.
About 12,000 standard liters of sample gas were processed in each test,
allowing higher accuracy than found in traditional low volume sampling
equipment. Traverses and gas velocity measurements across the stack were
done in conformance with standard stacK sampling procedures. 2
The probe assembly used for recycle gas collection (for subsequent
analysis in the C 6 -C 12 range) is shown in Figure 8. The sample collection
procedures used multiple methods, including evacuated gas bottles, cold trap
tubes, and polymeric adsorbents.
149
-------
Figure 6. Absorption Train at Recycle Gas Sample Location.
I
‘ 1
11”?
150
-------
STACK T.C.
COOLER
L_
GAS
XAD-2
IMP/COOLER
TRACE ELEMENT
COLLECTOR
DRY GAS METER ORIFICE METER
CENTRALIZED TEMPERATURE
AND PRESSURE READOUT
CONTROL
10 CFM VACUUM PUMPS
Figure 7. SASS Train Schematic.
-------
/
CONDENSER
U,
r.,)
RECYCLE
ICE BATH
DEWAR
XAD.2 CANISTER.
ABSORPTION
TUBE
GAS BOTTLE
Figure 8. Schematic Diagram of Recycle Gas Sample Probe Assembly.
-------
Impinger solutions used to collect H 2 S, SO 2 , and NH 3 were analyzed by
standardized wet chemistry methods, while Hg was determined by atomic
absorption spectroscopy (AA). Arsenic (as arsine) was sought by AA methods
as well, but the collected quantities were apparently below the level of AA
detection. Another analysis effort was undertaken, using a reducing agent
(NaBH 4 ), separation and identification. This latter technique was success-
ful, with a quantitation limit of 10 no for arsine.
Laboratory analysis for C 6 through C 12 hydrocarbons consisted entirely
of separation with a gas chromatograph, coupled to a mass spectrometer for
constituent identification. Quantification of 53 compounds was then accom-
plished by gas chromatography. The CC/MS identification procedure required
a concentrated sample, provided by the cold-trap samples. Duplicate gas
bottle samples were injected in triplicate for GC quantification. Eight
standard gases were used for CC column calibration, as well as verification
of the mass spectrometer identificaticn.
RESULTS
kesults and Discussion of Fugitive Dust Measurements
Total suspended particulate (TSP) measurements of fugitive dust can be
effectively carried out with high-volume samplers in the vicinity of oil
shale mining nd handling operations. Measurements conducted at the Anvil
Points mine adits appeared to be the most definable, since fugitive dusts
from mining, blasting, and vehicular exhausts were confined. Measurements
in open areas (haul road, retorted shale transfer, crushing) will vary
considerably from one high-volume sampler to the next, implying that a
single sample source cannot supply data which is typical for the area. A
number of samplers must be used, to allow for random variations in dust
concentrations under varying wind tnd terrain conditions. The sampler
configuration used in this study (two-downwind, one-upwind) is probably a
minimum choice to provide useable TSP statistics.
TSP data included in this work appeared to be credible with sample
volumes as low as 30 cubic meters, while sample volumes of 15 cubic meters
tended to give results that were out of line with larger sample volumes.
Although the data are useful measures of ambient dust concentrations at
ground level, they are too scattered or biased to be used for very accurate
dispersion or source emission estimates. TSP trends over the course of a
number of days are indicated in Figure 9 for the retorted shale handling
area, with sampler positions indicated by distance upwind (N) or downwind
(5). Consistently higher concentrations were measured at 35 meters downwind
than at 15 and 20 meters downwind of the source. The only other apparent
contributor of dust in the vicinity was the crushing and screening opera-
tions which were always downwind of the samplers during the testing periods.
A possible explanation of this result is that the samplers at 15-20 meters,
having been pl3ced in a lower elevation that the source, were missing a
portion of the centerline dust concentrations. The samplers at 35 meters
were at about the same elevation as the source, since the terrain was rising
1 3
-------
at this point. Although there is c’nsiderable scatter in these measure-
ments, it is clear that increased TSP values will be observed in the immedi-
ate vicinity of handling operations. When the Figure 9 TSP values at each
downwind distance (x) are averaged, a best-fit regression curve of the form
TSP = 11.34 x -0.27 appears to be a good prediction for observed dust con-
centration. 3 Increased dust levels were observed in Figure 10 as a result
of mining activities, especially blasting on 9/27.
The use of slotted-plate cascade impactors for particle sizing has
become reasonably common with high-volume sampler usage. The four-stage-
plus-backup filter version used in this study had an expected particle size
cutoff pattern (at 50% collection efficiency) as follows:
Stage 1 2 3 4 5
50% cutoff (microns) 7.2 1.5 0.95 0.49
An examination of the data averages for particle size distributions
showed an apparent bimodal distribution, with 30 to 50% of the total sample
catch appearing on the backup filter. If the mass of particulate on the
backup filter is treated as though it really is less than 0.49 microns in
diameter, calculated mass median diameters will be less than typical urban
suspended particulates. This conculsion is clearly invalid, and an optical
scanning analysis of randomly selected backup filters was conducted to
provide a limited QA audit of the ext.ent of particle sizing inefficiencies.
These results are given in Table 2, together with an estimate of the cor-
rections in observed mass distributions when the particles counted on the
filters are assumed to be spherical and unity density. The number of
particles in each size range are reported as No. %, and on three of the four
filters, particles in the 0-1 micron range were numerically in the minority.
TABLE 2. OPTICAL SIZING OF SELECTED FILTERS FROM
PARTICULATE SEPARATION TESTS
Sample location
O-1.Op
1.O-2.Op
2 .0- 3 .Op
3.O-8.Op
> 8.Op
Retorted shale
transfer
No.
Wt.
%
%
21.78
0.02
18.05
0.35
14.61
1.32
36.10
34.83
9.46
63.47
Haul road
:
No.
Wt.
%
X
16.21
0.01
19.88
0.29
13.46
0.89
35.47
25.08
14.98
73.73
Crusher area
No.
Wt.
%
%
19.25
0.01
17.08
0.27
10.56
0.76
40.37
30.98
12.73
67.98
Mine adit No.
1
No.
Wt.
X
%
72.85
0.12
8.61
0.38
2.65
0.54
10.93
23.77
4.97
75.19
(Polarized light microscopy performed by Walter C. McCrone Associates)
154
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tri
01
30
iw° n 10°
3-15
MPH
WIND DATA (AVI HAGi AM/PM)
10
4-7
2 U
36 MN
3bMN
; 8 9
DATE (SEPT)
1') M
Figure 9. TSP Values for Retorted Shale Transfer Area.
-------
tj'l
en
15
DATE (S6PTI
Figure 10. TSP Values for Mine Adits 1 and 2.
-------
We believe that the trend of particle size distributions on the select-
ed high-volume filters summarized in Table 2 provides convincing evidence
that the cascade impactor separation was subject to a great deal of particle
bounce error. Particles larger than 3 microns dominate the weight percent-
age on these filters, and the range that the filters were intended to catch
(0-1 microns) represent a negligible weight percentage. Even 5% of the
number of particles being in the over 8 micron category (as in the mine adit
sample) will seriously bias the mass distributions.
Organic and inorganic analysis results from these dust samples are
reported elsewhere. 4 As part of this analysis effort, it was noted that
infrared scans of organic extracts were partially masked by the presence of
silicon oils, and that the Fiberglas filters were the source of the contam-
ination. Solvent extraction of the filters prior to use was recommended as
a future quality assurance requirement for samples intended for organic
analysis.
Results and Discussion of Offgas Measurements
Instrumental analysis of recycle and thermal oxidizer gases has been
demonstrated to be a reliable method, using grab-bag sampling methods, for
both light hydrocarbons and combusion gases. Where concentrated gas stream
constituents were required (e.g., volatile trace elements), capture in
sample train impinger solutions was the method of choice. The proper selec-
tion and sequence of impinger solutions depends on a semiquantitative knowl-
edge of the constituents in the gas stream. Capture of heavier organics
(C 6 -C 12 ) was feasible with evacuated gas bottles and cold-trap tubes, but
adsorption on polymeric materials still appears to be a technique that
should be investigated for applications to shale oil processing streams.
High-volume particulate sampling trains (SASS trains) were needed to get
repeatable results from the thermal oxidizer discharge.
The standard analysis methods used for typical waste gas stream con-
stituents (H 2 S, SO 2 , NH 3 ) appear to be reliable procedures that can be done
within reasonable variance ranges, but instrumental analysis for SO 2 and NO
is easier and faster. Results for these constituents were consistent with
measurements made at Paraho during 1976. Ammonia was found in the recycle
gas stream at about 1 volume percent levels, along with hydrogen sulfide (in
the 0.1 volume percent range). These constituents would be removed in a
gas-cleaning urtit under full—scale operations. Emissions from burning the
treated gas would then be similar to natural gas combusion.
The volatile trace elements mercury and arscenic were detected. The
detection of arsenic provides some extremely useful information for Paraho
gas streams, because the very small amounts found (> ppb) suggest that
removal will not be required.
CONCLUSIONS
The quality assurance planning involved in the fugitive dust and offgas
survey has allowed subsequent analysis and data interpretation to be carried
157
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cut with a maximum utilization of the effort involved in a month-long field
sampling program. Pretest determinations of minimum sample sizes, reagent
requirements, contamination preventior, and sample identification procedures
are all essential. The audit role of quality assurance cannot be under-
emphasized either. Two highlights of the QA audit effort in this program
were the detection of probable errors in fugitive dust particle size deter-
mination by cascade impactors, anc the idenfication of trace organic
contamination of Fiberglas filters from silicon oils. Both of these eval-
uations were made with the aid of alternative measurement methods that would
not have been used for a large number of samples.
A definite conclusion reached from this work is that fugitive dust
particle sizing with cascade impactors should be calibrated with optical
sizing techniques, and that the first stage of an impactor should be
preceded by a cyclone collector with about a 5-micron cutoff. These proce-
dures should compensate for the particle bounce problem by preventing most
of the 5-micron and heavier particles from reaching stages with a lower
cutoff, and by assessing the bias introduced by particle bounce in the size
ranges under 5-microns.
Offgas measurements should be made with continuous monitors whenever
possible. GC/MS analysis of gas samples for trace organics is a very effec-
tive technique. Although trace element analysis of gas samples was success-
fully done in this program, further research will be needed to develop
continuous monitors for this purpose.
ACKNOWLEDGEMENTS
This work was done in part at the DOE Anvil Points Oil Shale Research
Facility located on the Naval Oil Shale Reserves near Rifle, Colorado. The
work was sponsored by the Resource Extraction Technology and Environmental
Control Divisons of IERL.
REFERENCES
‘ Sampling and Analysis Research Program at the Paraho Oil Shale Demon-
stration Plant, EPA 600/7-78-065, April 1978.
2 Administrative and Technical Aspects of Source Sampling for Partic-
ulates, EPA 450/3-74-047, August 1974.
R.C. Thurnau, IJSEPA, personal communication.
Cotter, J.E., D.J. Powell, and C. Habenicht. Fugitive Dust at the
Paraho Oil Shale Demonstration Retort and Mine. TRW report to USEPA,
Contract No. 68-03-2560, March 1979.
158
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INTERCOMPARISON STUDY OF ELEMENTAL ABUNDANCES
IN RAW AND SPENT OIL SHALES
J.P. Fox
Lawrence Berkeley Laboratory
Berkeley, California 94720
J.C. Evans
Battelle Pacific Northwest Laboratory
Richland, Washington 99352
T.R. Wildeman
Colorado School of Mines
Golden, Colorado 80401
J.S. Fruchter
Battelle Pacific Northwest Laboratory
Richiand, Washington 99352
INTRODUCTION
Techniques for accurate and sensitive elemental analysis of oil shale
materials are important for determining the fate and effects of various
constituents during oil shale conversion. Many routine analytical tech-
niques are not suitable for oil shale materials due to numerous chemical
interferences. 1 - 3 The need for oil shale reference standards was first
recognized by Poulson et al. 2
The purpose of this work was to develop raw and spent oil shale refer-
ence samples, to characterize them using an interlaboratory, interinstru-
mental approach, and to assess the performance of various analytical
methods. This study was jointly carried out using the analytical facilities
of various Colorado universities (COLO), Battelle Pacific Northwest Labora-
tory (PNL), the Lawrence Berkeley Laboratory (LBL), and the Lawrence
Livermore Laboratory (LLL). Analytical procedures routinely used at those
laboratories for similar measurements on other geochemical materials were
used. Thus, some laboratories analyzed a single sample rather than several
replicates.
Aliquots of the reference standards described in this work may be
obtained by writing the authors.
159
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EXPERIMENTAL
Instrumental neutron activation analysis (INAA), X-ray fluorescence
spectrometry (XRF), atomic abosrption spectroscopy (AA), emission spectros-
copy (ES), gamma-ray spectrometry (GS), and colorimetric and fluorimetric
methods were used to measure 52 elements in four oil shale reference
samples. Preparation of the reference samples and analytical procedures
used by the participating laboratories are described. Measurements by the
Colorado universities were made under the auspices of the Colorado Environ-
mental Trace Substances Research Program which consists of a number of
research groups at various Colorado universities. There is no central
laboratory--reported measurements were made at the Colorado School of Mines,
the University of Colorado, or Colorado State University. The U.S.
Geological Survey neutron activation and delayed neutron analyses were
performed on a service basis for the Colorado universities group for this
study.
Reference Samples
Four reference samples were prepared for tflis study. Samples OS-i and
FASS were prepared at the Colorado School of Mines using procedures pre-
viously described. 3 ’ 4 Sample OS-i is a raw oil shale from the Dow Mine,
Colorado. Twenty-seven kilograms of material were prepared by crushing and
grinding to -65 mesh and blending and splitting into 75-g samples. FASS is
Fischer Assay spent shale produced by 46 repetitive runs of Fischer Assay
retorts charged with OS-i. 3
Samples RAW-lB and SOS-1TB were prepared at the Lawrence Berkeley
Laboratory using procedures described here. RAW-lB is a raw oil shale from
the Anvil Points Mine, Colorado; SOS-11B is a spent shale produced during a
high-temperature combusiob run of the Lawrence Livermore Laboratory’s 125-kg
simulated in situ retort. 5
RAW-lB was prepared from master batch material received from Lawrence
Livermore Laboratory (LLL). The master batch material was prepared by LLL
by separating 136,000 kg of Anvil Points oil shale into greater-than and
less-than-1O2-n n fractions with a grizzly; passing the less-than 102-mm
material through a roll crusher; and screening the material to the size
range of 13 mm to 25 mm. A 25-kg sample of the 13-mm to 25-mm material was
split from the master batch and mixed and split into 500-g lots, using the
technique described by Wildeman. 4 Random number tables were used to select
two lots. Selected lots were ground to less than 3 mm in an alumina-faced
jaw crusher, and to less than 0.15 mm, with most passing 0.074 mm in an
alumina-jaw pulverizer; they were then split into 15-g samples for use in
this work. The 15-g samples were stored in acid-washed glass vials and
maintained at 4°C.
SOS-11B was prepared by grinding the charge from the 125-kg retort in a
Sturtevant rotary grinder with a built-in splitter. A 25-kg sample was
split from the rotary-ground material and prepared for analysis as described
for RAW-lB.
160
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A minimum of three separate splits of each of the four standards was
tested for homogeneity by measuring elemental abundances by neutron activa-
tion analysis and X-ray fluorescence spectrometry. 4 All samples were found
to be homogeneous within the analytical precision of the method.
Neutron Activation Analysis
Battelle Pacific Northwest Laboratory -
Two procedures were used by PNL. The first procedure was used to
analyze all four samples, and the second was used only for RAW-lB. In both
procedures, 0.1 to 0.5 g of sample were weighed into 0.4-dram polyethylene
vials; the vials were then heat sealed. The 0.4-dram vial was placed in a
2-dram polyethylene vial which was also heat sealed. After irradiation, the
samples were transferred to fresh vials. Standards used for elemental
analysis were Fischer atomic absorption standards for As, Ni, Zn, and Se;
National Bureau of Standard’s orchard leaves (SRM 1571); U.S. Geological
Survey standard rocks BCR-1, W-1, AGV-1 , and PCC-1; and IAEA standard
Soil-5. Samples were irradiated at the Oregon State University reactor at a
power of 1 MW.
Triplicates were analyzed in the first procedure and the reported
errors are the larger of 1 standard deviation for the replicates, 1 standard
deviation from the counting statistics, or 2% of the reported value. The
analysis procedure used one irradiation period and two decay/counting inter-
vals, as shown in Table 1. Following the irradiation, the samples were
transferred into clean polyethylene vials and counted on an 80-cc Ge(Li)
detector with a resolution of 1.96 keV at 12% relative efficiency after the
7-day cooling period. Counting following the 6-week cooling period was done
on an anticoincidence-shielded Ge(Li) detector to reduce Compton background
for low- and medium-energy gamma rays, and, in some cases, to remove peak
interferences from correlated gammas.
A single aliquot of RAW-lB was analyzed by the second procedure; the
reported errors are the larger of I standard deviation for the counting
statistics of 2% of the reported v&ue. The analysis procedure used one
irradiation period and three decay/counting intervals. The samples were
counted on a 130-cc Ge(Li) detector with a rasolution of 1.8 keV at 25%
relative efficiency following the 7-day and 30-day cooling periods. Count-
ing following the 70-day cooling period was done on an anticoincidence-
shielded Ge(Li) detector.
Lawrence Berkeley Laboratory -
Two replicates were analyzed. The reported error is an estimate of
1 standard deviation in the accuracy calculated from the counting statistics
of both the samples and the standards and the uncertainties in the elemental
abundances in the standards. The samples were analyzed using procedures
similar to those described elsewhere. 8 Approximately 100 mg of sample were
mixed with 50 mg of cellulose and compacted into a 1 cm x 1.2 mm pill using
a hand-operated hydraulic press. The samples were wrapped in thin poly-
161
-------
ethylene and p 1 aced in radial array with four samples and five standards
(standard pottery, KC1, CaCO 3 , and Al foil) in a heavy-duty polyethylene
irradiation capsule. The sealed capsule was suspended by a wire in the
central thimble of the Berkeley Triga Reactor and rotated during irradia-
tion. The technique used to analyze the resulting pills consists of two
irradiation periods and five decay/counting intervals, as summarized in
Table 1. Three of these were made with a 7-cc intrinsic Ge detector with a
resolution of 1.6 keV at 1 MeV and two were made with a 1-cc Ge(Li) detector
with a resolution of 0.54 keV at 103 keV. For the second irradiation, the
samples were rewrapped in high-purity Al foil and placed in radial array in
an Al irradiation capsule.
Lawrence Livermore Laboratory -
A single sample was analyzed using an absolute INAA procedure described
elsewhere. 7 Approximately 200 mg of sample were mixed with 200 mg of
Avicel, pressed into a 1.59-cm-diameter disc, and stacked in an Al irradia-
tion capsule between polyethylene spacers. Flux monitors consisting of U
and Sc were placed at opposite ends of the tube. Following irradiation of
the anipies, the disc was removed from the outer vial and placed in a second
container for counting. The analytical technique uses two irradiation
periods’ and five decay/counting intervals, as summarized in Table 1. The
samples were irradiated in the Livermore pool-type reactor which is moder-
ated and cooled by light water and consists of plate-type fuel elements and
boron—containing control rods. The samples were counted on 50- to 70-cc
Ge(Li) detectors.
Colorado Universities -
Samples OS-i and FASS were analyzed by the U.S. Geological Survey in
Denver under contract to the Colorado School of Mines. The procedure used
was similar to that described elsewhere. 8 Three replicates were analyzed.
The reported errors are the larger of 1 standard deviation for the repli-
cates or 2% of the reported value. Approximately 0.8 g of a powdered sample
were weighed into 2-dram polyethylene vials and irradiated in the General
Atomic TRIGA Mark I reactor. Standards used for the analysis were U.S.
Geological Survey G-2 and two specially prepared combined quartz standards
containing the elements of interest. The analysis procedure used two irra-
diation periods and four decay/counting intervals. Following the irradia-
tion, the samples were transferred into clean polyethylene vials and counted
on a 30-cc Ge(Li) detector with a resolution of 2.0 keV at 12% relative
efficiency. A 10-mm cooling period was used for the first irradiation. In
addition to the high efficiency Ge(Li) detector, a low-energy intrinsic Ge
detector with a 1-cc active volume and 0.48 keV resolution at 122 keV was
used for the 7-, 14- and 60-day cooling periods.
X-ray Fluorescence Spectrometry
‘I. .—
-------
TABLE 1. NEUTRON IRRADIATION AND COUNTING SCHEDULES USED BY LBL, PNL, LLL, AND THE USGS
Irradiation
time
Neutron flux
nlcm 2 -sec
Cooling
time
Counting
time, win
Elements
detected
LBL
18 win
2 x
1011
8 mm
1.25 hr
1
6
Al,
Mn,
Ca,
Na,
V,
K,
Cl,
Eu,
Mg, Ti
Ba, Sr, Cu, In, Ga
8 hr
2 x
1013
6 days
30 days
20
60,90
U,
Fe,
Ag,
Sm,
Sc,
Hf,
Lu,
Ta,
Th
Ti,
Eu,
La, As, Br, Cd, Mo, W, Ba, Au
Zn, Co, Cs, Sb, Ce, Ir, Se,
PNL
2 hr
6 x
1012
7 days
6 weeks
300
300
Na,
Cr,
La,
Th,
Sm,
Eu,
As,
Sr,
Sb, Co, Fe, Rb, Sc, Ba, Hf
Ni, Rb, Zn, Se
8 hr
6 x
1012
7 days
30 days
70 days
100
10
1000
Sm,
Fe,
Eu,
Lu,
Cr,
Sc,
Yb,
Th,
Zn,
Ba,
Sb,
Ni,
La, Na, Br, As, K
Ce, Go, Hr, Hg, Rb, Sc, Ta, Yb
Sr, Se, Tb
LLL
2 win
2.1 x
1O’
10 win
10,20,40
Al,
In,
V,
Dy,
Cu,
As,
Ti,
Ga,
Ca, Na, Mg, Cl, Mn, Br, I, Ba,
Sm, V, Mo
72 ruin
2.6 x
io’
3 days
15 days
133
333
Na,
Sb,
Fe,
Sc,
As,
Mo,
Cr,
Th,
W,
Zn
Co,
Ni,
Ga,
Zn,
Ta,
K, Cd, Mo, V, Sm, Au, Hg, La,
Hg, Se, Ag, Sb, Ce, Cs, Eu,
Hf, Ba, Rb
USGS
20 win
2.5 x
1010
10 win
20
Na,
K,
Ca,
Mn,
Sr, Ba, La, Dy
8 hr
2.5 x
1012
7 days
14 days
60 days
20
33
167
Ca,
Sc,
Tm,
Sc,
Ce,
Cr,
Cr,
Lu,
Cr,
Eu,
Ba,
Fe,
Hf,
Fe,
Gd,
La,
Co,
Ta,
Co,
Tm,
Ce, Nd, Sm, Eu, Yb, Ta, U
Zn, Rb, Sb, Ba, Ce, Nd, Eu,
Th, U
Zn, Sa, Rb, Sr, Nb, Sb, Cs,
Yb, Hf, Ta, Th
-------
Battelle Pacific Northwest Laboratory -
Three replicates were analyzed. The reported errors are 1 standard
deviation for the three analyses. The samples were analyzed using pro-
cedures similar to those described elsewhere. 9 Samples were prepared by
pressing 0.250 g of powder and an equal weight of cellulose into a 3.2-cm-
diameter disc. The samples were analyzed on a Kevex Model 810 energy-
dispersive X-ray machine. System resolution was 200 eV at 6.4 key (Fe Ka
X-ray). Excitation was provided by a Zr or Ag secondary source. The X-ray
tube was operated at 50 kV with a current of 35 mA. The resulting radiation
was measured with a 80-mm 3 detector and a 1000-channel pulse height
analyzer. Counting time was 100 minutes. The elements analyzed were Si,
Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Hg, Se, Pb, As, Br, Rb, U,
Sr, Y, Zr, Nb, and Mo. Individual calibrations were calculated for each
sample matrix from backscatter intensities and multi -element, thin-sample
calibration curve.
Lawrence Berkeley Laboratory-
Two energy-dispersive X-ray fluorescence systems were used. The “low-
energy ’ system was used to analyze for Al, Si, Ti, Fe, Na, K, Ca, and Mg.
Two replicates were analyzed. The reported errors are the larger of 10% of
the reported values, or 1 standard deviation for the counting statistics.
The samples were prepared using a L1BO 2 fusion technique O Approximately
200 mg of powdered sample were fused with 1.80 g of LiBO 2 in a Pt crucible
over a Fischer burner. The temperature of the mixture was slowly raised to
900°C, the mixture was poured into an Al ring, which was resting on a
vitreous carbon disc at 250°C, and pressed into a ring with a hydraulic
press. Weight loss on fusion was measured and used to compute elemental
abundances. The samples were analyzed using a prototype energy-dispersive
system designed and built at LBL. 1 ° The samples were placed in a vacuum
chamber maintained at 10 torr or better and irradiated with a multiple
anode soft X-ray generator consisting of six anodes and an electron gun.
Emitted radiation was measured using a lithium-drifted silicon detector and
a multichannel analyzer.
The Thigh-energy” system was used to measure the elements Ti and heav-
ier which have X-ray energies >4.5 keV. Two replicates were analyzed. The
reported errors are the larger of 1 standard deviation for the two analyses,
2 standard deviations from the counting statistics, or 4% of the reported
value. The samples were analyzed using procedures similar to those describ-
ed elsewhere.’ 1 Approximately 2 g c’f powder were pressed into a Lucite
cylinder and analyzed on a prototype system designed and built at LBL. The
total system resolution FWHM was 190 eV at 6.4 keV (Fe Ka X-ray) at 5,000
counts/sec using 1.8 psec pulse peaking time. Excitation was provided by a
Mo X-ray tube with external Mo filters. The X-ray tube was operated at
45 kV and with regulated currents that varied from 100 to 245 .iA. The
resulting X-rays were simultaneously measured by a guard-ring detector with
pulsed-light feedback electronics and 512-channel pulse height analyzer.
Counting time was 20 minutes.
164
-------
University of Colorado -
Six to eight replicates were analyzed. The reported errors are 1
standard deviation for the replicates. Samples were prepared and analyzed
(by a procedure described elsewhere) 12 ’ 13 by glueing 250 pg of powdered
sample into the center of a Forvar foil. The samples were analyzed on a
prototype system built at the University of Colorado cyclotron facility.
Excitation was provided by a 2-kW tungsten anode X-ray tube operated at
55 kV and with regulated currents varying from 2 to 20 mA. The total system
resolution FWHM was 163 ev at 6.4 keV (Fe Ka X-ray). The resulting X-rays
were measured on an 80-mm 2 , lithium-drifted, silicon X-ray detector,
arranged in a compact geometry, and a 512 channel pulse height analyzer.
The counting time was not fixed but was normally about 2 hour. The con-
figuration of the system was adjusted so that the background was lowest for
the Mo Ka X-ray. This made the background under K, Ca, Ti, Cr, and Mn
somewhat high and the uncertainties in the analysis of these elements is
correspondingly larger. The data reduction method used peak areas, compared
to thin-film, pure-element standards.’ 2 ’’ 3
Atomic Absorption Spectroscopy
Battelle Pacific Northwest Laboratory -
Atomic absorption spectroscopy wils used to measure Al, Ca, Fe, Hg, K,
Mg, Na, Si, Sr. and Ti. Conventional flame atomic absorption was used for
all elements except Hg which was determined by flameless atomic absorption.
Measurements were made with a Perkin Elmer 403. Powdered samples were fused
with lithium metaborate using a 6:1 LiBO 2 sample ratio. The fused sample
was dissolved in concentrated HNO 3 and diluted to 200 ml. Commercially
prepared aqueous standards were used. USGS standard rock BCR-1 was used as
a control.
Mercury was determined using a flarneless technique. A 50 to 150-g
sample was combusted, the vapors swept into a separation train, and the
mercury trapped on gold beads and analyzed by flameless AA. In the separa-
tion train, mercury-free air was introduced into a 950°C quartz tube
containing the sample overlying a layer of 3-4 cm of gold-coated quartz
beads. The high temperature and oxidizing conditions volatilized the
mercury from the sample matrix and the gold beads converted it to the ele-
mental form. Elemental mercury was separated from interfering organic
vapors by passing the sample gases through an alumina column which selec-
tively retains the organic vapors but passes Hg°. The Hg° was collected by
amalgamation in a column of gold-coated glass beads maintained at room
temperature. This column was detached from the separation train and
attached to a flameless AA system. Amalgamated Hg° was released by heating
the column to 500°C in a N 2 gas stream. The Hg° vapor was swept into a
long-path-length gas absorption cell where it was measured by atomic absorp-
tion at 254 nm.
165
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Lawrence Berkeley Laboratory -
Zeeman atomic absorption spectroscopy was used to measure Cd, Hg, Cu,
Pb, and Zn. The reported error is the larger of 10% of the reported value
or I standard deviation for three replicates. The instrumental technique
has been described elsewhere.’ 4 -’ 8 Electrodeless discharge lamps were used
for all elements. Mercury was atomized in a T-shaped combustion tube main-
tained at 900°C and Cd, Pb, and Zn, were atomized in a Massman-type furnace
equipped with a dual chamber graphite rod.’ 6 Powdered samples for Pb, Zn,
and Cd analysis were diluted with graphite powder (Ultra Carbon U.C.P.-2-
325) and mixed with a Wig-I-Bug. For the Hg analyses, the sample was
directly weighed into a tared Pt boat and inserted into the furnace. For
the other elements, the sample was weighed in a tared plastic tip for use
with an adjustable micropipette and transferred to a Massman-type furnace by
tapping the sides of the tip. The empty tip was weighed to determine trans-
fer efficiency.
Colorado Universities -
Abomic absorption spectroscopy was used to measure Al, As, Ca, Mg, Si,
and Na. Sample preparation consisted of standard HF, HNO 3 , and HC1O 4 diges-
tions for Na, Mg, Al, Si, and Ca’ 9 and a sodium peroxide fusion for As. 3
The solids were spiked prior to digestion and standard additions used to
check the analyses. Four replicates were analyzed for Al, Ca, Na, and Mg
using standard flame conditions, and three replicates were analyzed for As
using the hydride generation method. 3
Emi ssion Spectroscopy
Battelle Pacific Northwest Laboratory used a dc plasma technique to
measure B. Three replicates were analyzed. The reported errors are 1
standard deviation for the three replicates. Samples were prepared by
fusing 3 g of sodium carbonate with a 0.5-g sample, dissolving the residue
in 8 M HNO 3 , and diluting to 100 ml. The samples were analyzed on a
Spectrametrics Spectraspan 111 employing a dc argon plasma excitation system
and an Eschelle grating spectrometer.
Molecular Absorption
The Colorado universities used molecular absorption of A1F in an air—
acetylene flame to determine F in a fused sample. 4 The analytical method
was developed by Tsunada, Fujiwara, and Fuwa, 2 ° and was modified for oil
shales by Meglen and Krikos. 2 ’
Gamma- Ray Spectrometry
The Lawrence Berkeley Laboratory used gamma-ray spectrometry to measure
ii, Th, and K. 22 Approximately 50 g of powdered sample were packaged in a
3.8—cm—diameter plastic container and counted for 1,490 to 3,829 minutes in
a lead-shielded compartment with a 20-cm-diameter by 10-cm-thick Nai(T1)
crystal. The spectra were taken by a 1600-channel pulse-height analyzer
166
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covering the interval 0.1 to 4.0 MeV, and reduced by a computer program that
fits, channel-by-channel, standard and sample spectra over selected energy
intervals.
Fluorimetric and Colorimetric Methods
The Colorado universities used these techniques to measure Si, Se, Mo.
and B. Silicon was determined colorimetrically by the stanthrd molybdenum
blue procedure. 23 Selenium was determined by a fluorimetric method which
uses 2, 3-diaminonaphthalene. 24 Molybdenum was determined colorimetrically
using potassium thiocyanate 25 and B was determined using an Azomethine-H
method. 26 The fusions and digestions used with these procedures have been
previously described.
Delayed Neutron
The U.S. Geological Survey determined U under contract to Colorado
School of Mines by this technique. Approximately 10 g of powdered sample
were analyzed by procedures given by Stuckless et al. 27
RESULTS
Analytical results for the four samples are summarized in Tables 2
through 5. The data are grouped by analytical technique so that the per-
formance of each method may be readily assessed. The data in Tables 2 to S
were reduced using statistical techniques; the results are plotted in
Figures 1 through 4. A minimum handling error of 2% was assigned to all
values with unrealistically small errors. This is frequently a problem in
neutron activation work when the analyst reports counting errors.
The error-weighted average ( ) was computed and the value
x. -
1
Ui
was determined for each point, where X. is the ith measurement of an element
and a. is the associated standard de 1 viation. Chauvenet’s criterion 28 was
applie 1 d to the largest Z. for each element set for N 2 where N is the total
number of measurements for a given element. A value was rejected if
zi P
where P was a 1/3 N probability function based on the normal curve of error.
If a value was rejected, N was reduced by 1 and a new error-weighted average
was computed. This procedure was applied only once per element for each
sample. Finally, the percent deviation from the mean was computed for each
value in an element set as
167
-------
x. -
%DEV = 100
x
and the percent root-mean-square deviation for the element set determined as
1/2
%RMS =
The results of applying this procedure to the data developed in this
study are shown in the last column in Tables 2 to 5 and by Figures 1 to 4.
The last column of each table summarizes the error-weighted average obtained
for each sample after applying Chauvenet’s criterion 28 and the number of
separate determinations included in the average. Application of this cri-
terion resulted in the rejection of 12 values for SOS-11B, 16 values for
05—1, 21 values for FASS, and 19 values for RAW—lB. The errors reported in
the last column are the larger of the error-weighted standard deviation of
the laboratory values included in the average, or the smallest reported
error qf the individual laboratory values included in the average.
Figures 1 through 4 summarize the %RMS deviation, %DEV, and the coeff i-
cient of variation for each technique in an element set. The %RMS deviation
is recorded along the top of each graph and is a measure of the uncertainty
in the determination of the reported means for an element set. The %RMS
deviation ranged from 0.0 to 22.2 in this study, with 85% of the values
falling below 10%. The %DEV is plotted for each technique and is indicated
by a geometrical symbol that designates the technique. The corresponding
error bars are the percent standard deviation for each technique.
One interesting feature of this type of plot is that since an error-
weighted average was used, the points will not necessarily distribute sym-
metrically about zero. This is particularly the case if a single point with
a very small error dominates the average. Thus, a careful error analysis is
critical. This is particularly serious for a small number of points. These
problems have been partially alleviated by assigning a minimum analytical
error of 2% to all points, and by evaluating the %RMS deviation. An analy-
sis of this type assumes a normal distribution of errors. In some cases,
such as in the reported results for arsenic, there may be a bimodal distrib-
ution caused by a systematic difference between methods. If the cause of
the discrepancy was known, or at least suspected, the offending datum was
rejected. Otherwise, the values were retained. One such example, involved
a large fission product interference in the INAA analysis of Zr.
DISCUSSION
The results of this study show that most elements studied here can be
reliably and accurately determined i i raw and spent oil shales if careful
168
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Table 2. ELEMENTAL COMPOSITION OF ANVIL POINTS RAW OIL SHALE PREPARED BY LBL, RAW lB (ppm)
A1(%) -
As 54<1
3 -
Ba 540050
Br 0.52±0.16
Ca (8) 8.25±0.6
Cd
Ce 46±2
Cl —
Co 9.3±0.3
Cr 45±1
Cs -
Cu —
Dy —
Eu —
F
Fe (8) 2.41±0.05
Ga —
Ce —
Hf 1.7±0.1
Hg 0.06±0.01
Is —
Ir
K (7.) 1.83±0.19
La 21.2±0.5
Lu 0.26±0.03
Mg( 8) —
Mo =
Mo —
Na (1) 1.540.03
Nb -
Nd —
Ni 25±2
Pb —
Rb 76±5
Sb 2.0±0.1
Sr 6.8±0.1
Se 2.6±0.3
Si(%) —
Sm 3.6±0.1
Sr 840±50
Ta 3.55±0.02
m 0.37±0.04
Tb 7.0±0.1
Ti (Z) —
±
V
w -
V —
Yb 1.6±0.1
Zn 70±6
Zr —
3.83±0.12 4.04±0.19 —
45<4 39±1 38±3
498±26 479±19 -
10.3±0.5 9.41±0.19 10.5±0.8
44±2 41.2±0.8 —
<830 —. —
9.18±0.29 8.56±0. 17 —
37±2 33.1±0.8 —
4.46±0.33 4.40±0.10 —
— <98 43±4
2.48±0.13 2.13±0.14 —
0.63±0.02 0.59±0.01 —
2.21±0.06 2.16<0.04 2.2±0.2
— — 10.1±1.2
1.72±0.14 1.68±0.04 —
<0.18 — —
<0.01 — —
1.79±0.14 1.77±0.06 1.47±0.12
20.8±0.7 23.4±0.3 —
0.19±0.01 0.214±0.008 —
3,5±1.6 3.6±0.09 —
343±12 334±7 350±30
20±2 19±1 21±2
1.56±0.04 1.53±0.03 —
19±0 14<3 —
21±4 <36 23±3
— — 24.5±0.5
85±8. 74±3 79±6
2.1±0.1 1.90±0.07 —
6.47±0.20 5.93±0.05 -.
— 2.38±0.30 2.2±0.5
— — 15.0±1.0
3.08±0.12 3.19±0.07 —
— 6±3±29 —
0.46±0.02 0.47±0.04 —
0.40±0.07 0.36±0.02 —
6.70±0.26 6.18±0.07
3.93±0.12 (4)
42±1 (7)
108±11 (1)
495±19 (4)
0.55±0.01 (2)
9.6±0.2 (6)
0.72±0.07 (1)
42±1 (2)
30 (1)
8,8±0.2 (4)
46±1 (3)
4.41±0.10 (2)
39±3 (3)
2.32±0.13 (2)
0.60±0.01 (3)
990±20 (1)
2.18±0.04 (8)
10±1 (3)
±0.4 (1)
1.68±0.04 (3)
0,083±0,004 (2)
<0.18 (1)
<0.01 (1)
1.73±0.04 (6)
20.6±0.4 (3)
0.21±0,01 (2)
3.6±0.1 (4)
337±6 (5)
20±1 (5)
1.56±0.03 (5)
5,7±0.1 (1)
16±3 (2)
24±2 (5)
24±0.5 (4)
75±2 (6)
1.98±0.07 (4)
6.8±0.1 (3)
2.5±0.3 (5)
15.0±0.8 (3)
3.25±0.07 (3)
712±19 (5)
0.46±0.02 (2)
0.36±0.02 (3)
6.95±0.10 (4)
0.16±0.01 (6)
3.92±0.16 (2)
93±5 (2)
12±1 (3)
1.33±0.03 (2)
65±2 (5)
50±3 (3)
Al
As
B
Ba
Br
Ca
Cd
Ce
Cl
Co
Cr
Ca
Cu
Dy
Eu
F
Fe
Ca
Ce
Hf
Mg
In
Or
K
La
Lu
Mg
Mn
Mo
Na
Nb
Nd
Ni
Pb
Rb
Sb
Sc
Se
Si
Sm
Sr
Ta
Tb
Th
Ti
U
V
w
Y
Yb
Zn
Zr
Neutron Activation
Analysis High
energy
(B) (C) (A)
X—ray Fluorescence Spectrometry
other Average
High High Low
energy energy energy
(B) CD) (B) (A) (B) (5) Cone
No. of
Values
(A—i) (A—2)
62±1
8.8±0.2
49±1
0.59±0.03
2.29±0.05
20. 2±0.4
1,58±0.03
26±1
74±1
1.90±0.10
7 .3±0.1
2.6±0.3
3. 5±0. 1
740±40
7. 0±0. 2
05±5
37±1
<1.5
< 31
40±3
2.18±0.09
9.8±1.8
<2.4
<4
341±21
20±4
24±2
74±2
2.1±0.7
698±19
6.8±1.7
0.16±0.05 0.17±0.03 0.17±0.03 0.18±0.02
4.10±0.16 3.63±0,20 —
107±24 92<5 — —
- - 12±2 13±1
1.38±0,05 1,31±0.03 — —
— 75±3 69±7 67±3
— — 56±8 —
4009
520± 36
0.55±0.01
11±1
34±3
2.10±0.04
6.5±0.7
300 ±8
20±2
5, 7±0. 1
29±1
23±1
82±1
7 98 ±4
12±1
63±2
6Q• 3
4.0±0.2 388 , 034 a — —
— —
i 8±ii — —
9.2±0.5 100 , 05 a
— — 0 , 7200 , 07 b —
— — 40 ,46 —
— : — 990*200
2.1<0.1 224 < 03 a - —
: 08605 a 0077 , 0008 b :
1.7 0.l 161*0110
3.5 0.2 352+0100
— 395 < 70 a — —
- — - 19±2°
1.7±0.1 — — —
— — 23±36 —
15.0±0.8 149+120
= 720±600 - —
0.16±0.01 017 , 003 a : =
A = Rattelle Parifir Northwest Laboratory, I I Lawrence Berkeley Laboratory, C Lawrence Livermore Laboratory. 0 University of Colorado
E — U. S. Geological Survey, a — atomic absorption spectroscopy, b Zeeman atomic absorption spectroacopy, c — colorimetric,
d — fluorimetric, e delayed neutron, P gaussa—ray upectronetry, g emission spectroscopy
-------
Table 3. ELEMENTAL COMPOSITION OF DOW MINE RAW OIL SHALE, OS-i (ppm)
Neutron
Activation Analysia
X—Ray Vluoresc
High High
enargy energy
(A) (8)
ence Spectrosaeery
High
energy
(0)
0th Average
Low
energy No. of
(g) (A) (B) (0) Conc Values
(A)
(B)
(B)
Al — 3.41±0.12 — — — — 3.40.2 3.4±0.? — 3 . 5 ± 0 . 1 a 3.43±0.10 (4) Al.
As 77±2 77±6 — 65±5 65±3 64±6 — — — 7546 a 75±2 (5) As
B — — — — — — — 110±25 ‘ 80±8 (2) B
Ba — 1225±47 1410±60 — — — — — — — 1295±47 (2) Ba
Br — — — — “1.5 — — — — — ..5 (1) Br
Ca (5) — 10.1±0.5 — 10.5±0.7 — 9.9 0.5 — 73107 a 9.6±0.4 (5) Ca
Cd — — — — — — — — 1.05±0.11 1 . — 1.05±0.11 (1) Cd
Ce — 36±2 34±2 — — — — — — — 35±2 (2) Ce
C l — <700 — — — — — — — 00 (1) Cl
Co 9.2 0.2 10.8±0.3 18±5 — <30 — — — — — 9.7±0.2 (2) Co
C c 28±1 35±2 46±7 — — — — — — 29±1 (2) Cr
Ce — 4.49±0.34 4.4±0.2 — — — — — — — 4.4±0.2 (2) Ca
Cu — — — 49±5 52±4 44±2 — — — — 46±2 (3) Cu
Dy — 1.87±0.11 2.4±0.2 — — — — — — — 2.0±0.1 (2> Dy
Eu 0.49±0.04 0.54±0.02 0.54±0.01 — -. — — — — — 0.54±0.03 (3) Eu
F — — — — — — — — — 10201100 a 1020±100 (1) F
Fe (5) 1.870.04 1.89 0.06 1.87±0.04 1.95±0.51 1.88±0.07 1.5±0.2 1.8±0.1 194 , 031 a — — 1,87±0.04 (8) Fe
Ca — — — 8.7±1.1 8.1±0.2 3.7±0.4 — — — — 8.1±0.2 (2) Ca
Ce — — — — 2.8±1.6 — — — — — 2.8±1.6 (1) Ce
Hf — 1.44±0.12 1.45±0.03 — — — — — a — b — 1.45±0.03 (2) Hf
Hg — — — — — — — 0.14±0.007 0.16±0.02 — 0.14±0.01 (2> Hg
In — <0.20 — — — — — — — ‘0.20 (1) In
ir — <0.01 — — — — — — — — <0.01 (1> ir
K (Z) — 1.36 O.l3 1.33±0.06 1.25±0.01 — — 1.2±0.1 1.17±0.11 1.20±0.02 — 1.23±0.02 (6) K
La 18.4±0.4 18.8±0.8 19±1 — — — — — 18.5±0.4 (3) La
0 Lu — 0.16±0.02 0.20±0.02 — — — — — — — 0.18±0.02 (2) Lu
Hg (5) — 2. 3±1.1 — — — — 2.6±0.1 2 , 60 0 1 a 2 760 2 a 2.6±0.1 (4) Mg
— 275±9 272±14 262±22 258±26 196±16 — 290±70 — — 272±9 (5) Mn
Mo — 32±4 — 27±2 — 29±2 — — — 26±2° 28±2 (4) Mo
Na (5) 1.31±0.03 1.40±0.04 1.47±0.06 — — — 1.4±0.1 1.46±0. ? — i. 54 ±ü. 04 a 1.36±0.03 (5) Na
— — — — — 4.5±0.2 — — — — 4.5±0.2 (1) Nb
Nd — 16±4 15±1 — — — — — — — 15±1 (2) Nd
N i 323 30±4 — 33±4 26±5 25±1 — — — — 31±3 (4) Ni
Pb — — — 32±3 30±3 28±1 — — 29±2 — 29±1 (4) Pb
Rb 72±4 80±8 68±2 72±5 65±5 68±3 — — — — 68±2 (6) Rb
Sb 2.6 0.1 3.2±0.2 3.2±0.1 — — — — — — — 3.2±0.1 (2) Sb
Sc 4,5±0.1 5.16±0.16 5.0±0.1 — — — — — — — a 5.0±0.1 (2) Sc
Se 4.2±0.5 — — 4.6±0.6 4.1±1.0 — — 3.5±0.2 4.3±0.3 (3) Se
S i (5) — — — 13. 5±1.0 — — 13.0±0.6 13. 5±1.0 a — 13.0±0. 3 a 13. 1±0.3 (4) Si
Sm 3.0 Ii.4 2.49±0.11 2.7±0.1 — — — — — 2.6±0.1 (3) Sm
Sr 653±40 — 650±40 — 595±23 620±12 — 660 ± 6 0 a — — 620±12 (5) Sr
Ta — 0.39±0.02 — — — — — — — — 0.39±0.02 (1) Ta
Th — 0.28±0.05 0.33±0.03 — — — — — — — 0.32±0.03 (2) Th
Th 4.6±1.0 5.17±0.20 4.8±0.1 — 6.0±2.0 — — — 5 . 35 ± o . 21 — 4.9±0.1 (5) Tb
Ti (5) — 0.13±0.05 — 0.18±0.01 0.14±0.02 — 0.11±0.01 Q.l 4 ±O. 03 — — 0.12±0.01 (4) Ti
U — 4.54±0.18 6.1±0.5 — — — — — 4.24±0.09 s. 4 ± o. 3 e 4.4±0.1 (4) U
V — 127±30 — — — — — — — 127±30 (1) V
W — 2.8±0.4 — — — — — — - — 2.8±0.4 (1) H
V — — — 8.2±1.0 7.8±1.6 8.3±0.4 — — — — 8.3±0.4 (3) Y
Yb — 1.04 0.04 1.0<0.1 — — — — — — — 1.03±0.04 (2) Yb
Zn 91 5 — — 72±6 74±4 70±5 — — 9 1 ±gb — 74±4 (4) Zn
Zr — — 63±2 54±7 — 49±1 — — — — 49±1 (2) Zr
A — Battell ,e Pacific Northwest Laboratory, B — Lawrence Berkeley Laboratory, C Lawrence Livermore Laboratory, 0 — University of Colorado,
K — U. S. Geological Survey, a atomic absorption apectroacopy, b — Zeeman atomic absorption apectroscopy, c — co lor imetric,
d — fluorlmetrlc, a delayed neutron, f gamma—ray apectrometry, g emission spectroscopy
-------
Table 4. ELEMENTAL COMPOSITION OF SPENT OIL SHALE FROM RUN S-li OF LLL’S 125-kg RETORT
PREPARED BY LBL, SOS-11B (ppm)
X—Ray Fluorescence Spectrometry
Average
Neutron Activation Analysis High High High Low Other
energy energy energy energy No. of
(A) (B) (C) — (A) (B) (0) (8) (A) (B) (0) Conc Values
Al — 5.81±0.18 5.86±0.12 5.68±0.3 — — 5.6 <0.3 — — — 5.81±0.12 (4) Al
As 59±1 65 <6 54±2 58±5 56±3 60±2 — — 5 i± 4 a 58±1 (7) As
B — — — — — — — j4O±l5 — — 140±15 (1) B
Ba — 725±50 680±23 — — 740±31 — — — — 704±23 (3) Ha
Br — — — — <1.8 <1.6 — — — — <1.6 (1) Br
Ca (¾) — 14.0±0.7 12.3+0.2 14.5±0.5 — 16±2 12.3±0.6 139 , 05 a — — 12.7±0.2 (5) Ca
Cd — — — — — — — — o, 7 ?±o, ofb — 0,77±0,08 (1) Cd
Ce — 63±4 58.1±1.2 — — — — — — — 58.5±1.1 (2) Ce
Cl — <1180 — — — — — — — — <1180 (1) Cl
Co 11.8<0.2 12.6±0.4 11.9±0.2 11.8±0.2 <38 — — — — — 11.9±0.2 (4) Cc
Cr 50±2 60±3 50.4±1.0 — — — — — — — 50±1 (2) Cr
Cs — 6.96±0.52 6.89±0.14 — — — — — — — 6.89±0.14 (2) Cs
Cu — — <98 63±5 55±5 48±2 — — SO ±5 ’ — 49±2 (3) Cu
Dy — 3.65±0.52 3.22±0.22 — — — — — — — 3.46±0.20 (2) Dy
Eu 0.86.0.03 0.93±0.04 0.86±0.02 — — — — — — — 0.87±0.02 (3) Eu
F — — — — — — — — — 98060 a 980±60 (1) F
Fe (¾) 3.28±0.02 3.09±0.09 3.03 0.06 3.19±0.22 3.22±0.12 3.2±0.2 2.9±0.2 3.l5±O.3] .a — — 3.08±0.06 (7) Fe
Ga — — — 14.6±1.6 13±2 10.4±0.4 — — — — 14.0±1.2 (2) Ga
Ge — — — — <2.1 — — — — — <2.1 (1) Ge
Hf — 2.84±0.23 2.58±0.05 — — — — — — — 2.59±0.05 (2) Iii
Hg — — — — <4.5 — — <0.005 ’ < o 01 b — o,oi (1) Hg
In — <0.31 — — — — — — — — <0.31 (1) In
Or — <0.01 — — — — — — — — h01 (1) It
K (¾) — 2.66<0.24 2.55±0.10 2.35±0.17 — — 2.5±0.1 2 . 39 ±o. 19 a — — 2.50±0.10 (5) K
La 30.0±0.4 31.8±1.1 29.8±1.5 — — — — — — — 30.5±0.6 (3) La
Lu — 0.32±0.03 0.33±0.01 — — — — — — — 0.33±0.01 (2) Lu
Mg (¾) — 4.9±1.2 5.2±0.1 4.97±0.10 — — 4.7±0.2 — — — 5.04±0.10 (4) Mg
Mn — 482±16 478±10 507±40 481±37 459±48 — 49 5 ± 40 a — — 480±10 (6) Mn
Mo — 27±4 25±2 28±2 — 27±1 — — — 27±1 (4) Mo
Na (7.) 2.41±0.05 2.45±0.07 2.41±0.05 — — — 2.4±0.1 — — — 2.42±0.05 (4) Na
— — — — 8.9 <0.6 — — — — 8.9±0.6 (1) Nb
Nd — 27±5 26±3 — — — — — — — 26±3 (2) Nd
Ni 36 +5 32+7 <41 40±5 31±7 40± 3 — — — — 38±3 (5) Ni
Pb — — — 38+4 37±3 40±2 — — 30±8 1 — 39±2 (4) Pb
Rb 110±11 110±11 102±3 103±7 105±4 123±1 — — — — 104±3 (7) Rb
Sb 2.9±0.1 3.1±0,2 2.95±0.08 - — — - — - - 2.9±0.1 (3) Sb
Sc 10.1±0.03 9.28±0.29 8.26±0.16 - — - — - — — 8.51±0.16 (2) Sc
Se 1.7±0.3 - 1.5±0.4 1.4±0.4 1.4±1.2 - — - - 1.6±0.3 (4) Se
Si (¾) - - — 21.5±1.8 — — 21.7±1.1 22 . 1 ±i. 2 a - — 21.8±1.1 (3) 51
Sm 5.2±0.1 4.48±0.20 4.51±0.11 - — - — — - - 4,50±0.11 (2) Sm
Sr 1040±50 — 965±59 995±60 944±37 1071±21 — 9 & o± 6 0 a — — 1043±21 (5) Sr
Ta — 0.69±0.03 0.69±0.04 - — — - — - — 0.69±0.03 (2) Ta
Th — 0.60±0.14 0.51±0.02 — — — - — — — 0.51±0.02 (2) Tb
Th 9.9±0.2 9.85±0.39 8.93±0.08 - — - — - - — 9,89±0.20 (2) Th
Ti (¾) - 0.23±0.10 0.22±0.05 0.22±0.02 0.24±0.02 - 0.21±0.02 0 , 2000 , 03 a — - 0.22±0.02 (6) Ti
U - 6.33±0.26 6.25±0.30 — - - - — - - 6,30±0.26 (2) U
V - 146<34 127±6 — - - — - — — 128±6 (2) V
W — 1.8’0.4 <2.5 — — — — — — — 1.8±0.4 (1) w
Y — - - 21+3 21±2 18±1 - - — — 19±1 (3) Y
Yb — 2.06±0.07 1.98±0.04 — - — - — - — 2.00±0,04 (2) Yb
Zn 130±5 - 124±3 130±10 116±4 105±4 — — 1 09 ±iib - 123±3 (5) Zn
Zr - — — 86±12 - 107±47 - - — - 12 ( ) Zr
A = Battelle Pacific Northwest Laboratory, B Lawrence Berkeley Laboratory, C = Lawrence Livermore Laboratory, D University of Colorado
F — U. S. Geological Survey, a — atomic absorption apectroscopy, b — Zeeman atomic absorption spectroscopy, c colorimetric,
fluorimetric, e — delayed neutron, f gaimna—ray spectrometry, g — emission spectroscopy
-------
Table 5. ELEMENTAL COMPOSITION OF FISCHER ASSAY SPENT SHALE (FASS)
Neutron Activation Anglysie X—rsy Fluorescence Spectroaetry Other
-______________________________ _________________________________________ ____________________________________— Average
High High High Low
(A) (8± ( ) Energy EoerEr Energy Enargy (A) (B) (D) No. of
(A) (B) (0) (B) Conc Values
Al (2) — 4.05±0.13 — — — — 4.0±0.2 4.04±0.25” — 4.2±0.1 < 4.12±010 (4) Al
As 91±2 89±8 — 8616 703 79±3 — — S7±4 82±3 (5) As
B — — — — — — — 9 l ±9 — 11 .5±12° 110±9 (2) B
Ba — 1761f 194050 — — — — — — j959±5Q (2) Ba
Br — — — ±1.8 — — — — — <1.6 (1) Br
Ca (2) — 12.2 0.6 — 13.3±1,0 — 9.7±1.2 10.8±0.5 12.7±0.5” — 93002 a 98±02 (3) Ca
Cd — — — 12 8 j 0 1 )b — 1.28±0.13 (1) Cd
Ce — 44±2 4j±j — — — — — — — 42±1 (2) Ce
C l — <1I — — — — — — — — <1355 (1) Cl
Co 11.0±0.2 1.3.1±0.4 15.8±0.2 — <34 — — — — — 11.5<0.2 (2) Co
Cr 34±1 43±2 52±8 — — — — — — — 36±1 (2) Cr
Ca — 5.65±0.42 5.6±0.1 — — — — — — — 5.60±0.11 (2) Cs
Cu — — — 68±7 66±5 47±1 — — — — 67±5 (2) Cu
Dy 2.28±0.12 2.7±0.1 — — — — — — — 2.5±0.1 (2) Dy
Eu 0.580.02 0.65±0.03 0.68±0.01 — — — — — 0.68±0.01 (2) Eu
— — — — — — a — 1420±200 ” 1420±200 (1) F
P. (2) 2.23±0.04 2.34±0.07 2.36±0.05 2.5±0.3 2.42±0.09 1.8±0.2 2.2±0.1 2.68±0.31 — — 2.31±0.04 (7) Fe
Ga — — 10.7±1,3 8.4±2.0 4.6±0.1 — — — — 10.0±1.3 (2) Ga
Ge — — — — 2.8±1,8 — — — — — 2.8±1.8 (1) Ge
Hf — 1.78±0.14 1.8220.04 — — — — — — — 1.82±0.04 (2) Hf
Hg — — — <3.9 — — 0.o4l±0.001 003520003 b — 0.040±0.001 (2) Hg
In — <0.21 — — — — — — — ±0.21 (1) In
Ir — <0,01 — — — — — — — <0.01 (1) Ir
K (2) — 1.51±0.14 1.54±0.03 1.6±0.1 — — 1.5<0.1 1.41±0.11” — 1,52<0.03” 1.53±0.03 (6) K
La 21.0±0.4 22.0±0.9 23±1 — — — — — — — 21.2±0.4 (2) La
Lu — 0.15±0.02 0,16±0.01 — — — — — — — 0.16±0.01 (2) Lu
Mg (2) — 3.0±1.3 — — — — 3.1±0.2 3.2±0.1” — 2.94±0.08” 3.0±0.1 (4) Mg
339±11 340±10 370±30 351±32 224±25 — 375±7Q ” — 342±10 (5) Mn
Mo — 38±5 — 39±3 — 36±2 — — — 28±2° 37±2 (3) Mo
Na (2) 1.62±0.03 1.74±0.05 1,76±0.04 — 1.8±0,1 l.67±O.1O — 1.90±0.07” 1.77±0,04 (5) Na
Nb — — — — — 5.6±0.2 — — — — 5.6±0.2 (1) Nb
Nd — 19±4 18±1 — — — — — — — 18±1 (2) Nd
Ni 38±1 33±5 — 41±5 33±6 29±3 — — — — 38±1 (4) Ni
Pb — — — 43±4 38±3 37±3 — — 32±80 — 38±3 (4) Pb
Rb 91±10 97±10 87±2 81±6 82±3 86±1 — — — 86±2 (6) Rb
Sb 3.6±0.1 3.8±0.2 4.1±0,2 — — — — — — — 3.6±0.1 (2) Sb
Sc 5.3±0.1 6.35±0.20 6.2±0.1 — — — — — — 6.24±0.12 (2) Sc
Se 5.2±0.3 — . — 4.9±0.7 4.9±1.0 — — — — 43 ± 02 d 5,J±Q.3 (3) Se
Si (2) — — — 16.1±1.0 — 15.6±0.8 163 <1,2 — 15.1±0.3” 15.7±0.3 (4)
Sm 3.6±0.2 3.01±0.13 3.3±0.1 — — — — — — — 3.4±0.1 (2) Sm
5± 790±50 — 860±50 810±60 770±30 771±40 ‘ . — — — 790±30 (5) Sr
Ta — 0.47±0.02 — — — — — — — 0.41±0.02 (1) Ta
Tb — 0.35±0.06 0.44±0.02 — — — — — — — 0.45±0.02 (2) Tb
Th 5.4±1.0 6.35±0.25 5.9±0.1 — 7.2±2.2 — — — — — 6.0±0.1 (4) Th
Ti (2) 0.23±0.04 0.15±0.06 — — 0.16±0.02 — 0.13±0.02 — — .. 0.14±0.02 (3) 11.
U — 5.38±0.22 1±1 — — — — — 6.4±o.2 6,4±0.2 (2) u
V — 161±36 — — — — — — — 161±36 (1) V
Id — 3.1±0.4 — — — — — — — — 3.1±0.4 (1) W
y — — — 12.6±1.9 12±2 10.2±0.7 — — — — 10.6±0.6 (3) V
Yb — 1.25±0.05 1.3±0.1 — — — — — — 1.3±0.1 (2) Yb
Zn 109±5 — 96±9 92±4 72±3 — — 103±100 99±4 (4)
Zr — — 110±20 72±10 — 61*2 — - 61t2 (2) Zr
A • Battelle Pacific No±thwe t Laboratory, 8 — Lawrence Berkeley Laboratory, C — U. S. Geological Survey, I — University of Colorado,
B • U. S. Geological Survey, a — atomic absorption epectroscopy. b • Ze.man atosic absorption spectroecopy, c — colorimetric,
d • fluoriaetric, a deliy .d neutron, f — ga a —cay epsctrometry. g — smiaeion apectroacopy
-------
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173
-------
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-------
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_[
K La Lu Mg Mn Mo Na Nd Ni Pb
RMS DEVIATION i%)
i
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!r T^ 5
1
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o
50
40
30
20
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40
1.2 0.4 54 00 12.2 0.3 5.9
'.
(f ¥ \t{ f
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j '{ '
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i
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0.6 10.2 84 2.2 6.7 186
T
j
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t Ji
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t
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-
1 1 1
U V Y Yb Zn Zr
a
UJ
S
E
o
INAA A FLAME AA o COLORIMETRY x GAMMA RAY SPECTOMETRY
XRF * ZAA • COLD VAPOR AA
Figure 3. Relative performance of analytical techniques
on spent oil shale SOS-11B
175
-------
FISCHER ASSAY SPENT SHALE - FASS
50
2 40
X
a 30
UJ
I 20
S 10
1 o
U.
§-10
< -20
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-40
50
z 40
UI
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ul
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1 o
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Ul
3-30
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RMS DEVIATION!0',
5~9 4~820l 4l104 147O6T6 fT4
As Ba Ca Ce Co CF Cs Cu Dy Eu Fe
RMS DEVIATION 1%)
O f8 37 51 5l O 4l 37 100 10.4 6.6
Tl - 2l
Mg
157 TT7
Mo Na Nd Ni Pb Rb
RMS DEVIATION (%l
12.2
I.
Se Si Siii Sr fb fh fi U Y Yb Zn Zr
o INAA A FLAME AA a COLORIMETRY x GAMMA RAY SPECTROMETRY
« XRF » ZAA • COLD VAPOR AA o DELAYED NEUTRON
Figure 4. Relative performance of analytical techniques
on Fischer assay spent shale FASS
176
-------
ir easurements are made. Of the 52 elements surveyed, 20 were determined by
more than one technique and a minimum of two measurements was obtained on
40 elements. Typically, only a single measurement, or an upper limit, was
obtained for B, Cd, F, Hg, In, Ir, and Nb. Excellent agreement between
laboratories and techniques was obtained for most elements on all samples.
There was no significant difference in the results obtained for the raw and
spent oil shale samples. The %RMS deviation was less than or equal to 10%
for all elements on all samples except Ca, Cr, Ga, Pb, Ni, Tb, Zr, Y, Dy,
Th, U, Zn, Ti, V, As, and Nd. The elements As, Ca, Fe, Rb, Se, Ti, and Zn
were the most frequently measured elements and the major elements Al, Mg,
Na, Si, and Fe were precisely measured by at least three laboratories and by
using three separate techniques.
This is the first major interlaboratory comparison of energy-dispersive
X-ray fluorescence spectrometry with other techniques. Intercomparison
studies heretofore focused on neutron activation analysis and atomic absorp-
tion spectroscopy. 29 ’ 3 ° This work affords the first opportunity to assess
the performance of recently developed X-ray techniques on geochemical
samples for a range of elements.
More than one technique was used for the analysis of 38% of the ele-
ments (Al, As, Ca, Fe, Ga, K, Mn, Mo, Na, Ni, Pb, Rb, Se, Si, Sr, Th, Ti, U,
Zn, Mg). The agreement between techniques for these elements was excellent
with a few exceptions as discussed below.
Neutron activation analysis determined the most elements (38) and
typically produced the most accurate and precise results. This is the only
technique that was used to measure Ba, Ce, Cr, Cs, Eu, La, Sb, Sc, Sm, Yb,
Dy, Cu, Nd, and Tb in all samples. Good interlaboratory agreement was
obtained on all elements by INAA except Co, Cr, Dy, and Sm. Good agreement
was obtained between the absolute INAA method of analysis used by LLL and
the calibration method used by PNL, USGS, and LBL.
X-ray fluorescence spectrometry was used to measure 27 elements in this
study. It was the only technique used for Cu, Nb, Ga, and Y. These tech-
niques are not as precise as INAA; precisions of 10% were typical. Inter-
laboratory agreement was excellent and generally better than for INAA,
presumably due to the larger analytical errors Two types of XRF systems
and various sample preparation and data reduction procedures were used in
this study. High-energy XRF, in which the elements SI and heavier were
measured, was used to analyze thin-film and thick samples using pure-element
and standard rock calibration standards, respectively. Low-energy XRF, in
which elements 11 through 20 were determined, was used to analyze LiBO 2
discs using standard rocks. These techniques agreed well with other methods
of analysis except the high-energy system that used thin-film samples. That
technique yielded low results for Mn and Zn and erratic results for other
elements. Approximately 25% of the n easurements made by the thin-specimen
technique were discarded when Chauvenet’s criterion was applied to the data
set. This is believed to be due to X-ray absorption and matrix correction
procedures. 12 ’13
177
-------
An analytical problem was noted for As in the raw oil shales in this
study. The results obtained by INAA were typically about 15% higher than
the results obtained by XRF. This same trend was evident in more than
100 samples not reported here. This disagreement was investigated by both
PNL and LBL during the course of this study but its source was not identi-
fied. There are no obvious interferences in the measurement of As by either
INAA or XRF. Additional work completed by some of the authors, in which
material balances were calculated for various retorting processes, suggests
that the problems lie with the XRF technique.
Atomic absorption spectroscopy was used to determine Hg, Al, Ca, Fe, K,
Mg, Mn, Na, Sr, As, and Ti, and Zeeman atomic absorption spectroscopy was
used to determine Cd, Hg, Pb, and Zn. Both of these AS techniques produced
accurate and precise results. The interlaboratory and interinstrumental
agreement was cood except for Ca by AA in two samples and Na in one sample.
The color’imetric procedures used for Si and Mo agreed well with instru-
mental techniques but the fluorimetric procedure for Se yielded low results
on the two samples reported here. However, good agreement between the Se
fluorimetric procedure and instrumental techniques was obtained on other
samples not reported here.
Additional work is required to develop reliable analytical techniques
for B and F, which are important constituents in oil shales due to their
leaching potential. Neither of these elements can be readily measured by
the instrumental methods INAA, SRF, and AA, and chemical methods have not
been adequately developed for oil shale matrices. In this study, B was
measured by dc emission and colorimetrically. The results obtained by these
two techniques disagree by more than 2 standard deviations of the reported
errors. Similarly, F was measured by only a single technique. Additional
work is required to develop and validate reliable techniques for the
measurement of both B and F.
SUMMARY
Two samples each of raw oil shale and spent oil shale were prepared as
reference samples and analyzed by four laboratories using neutron activation
analysis, X-ray fluorescence spectrometry, atomic absorption spectroscopy,
and other techniques. Excellent agreement was obtained between techniques
and laboratories except for the thin-film XRF technique. The %RMS devia-
tions were less than or equal to 10% for 85% of the values. In general, the
INAA analysis procedures yielded the most accurate and precise rsults. The
XRF and coloriii etric methods compared well with INAA but they were not as
precise. Poor interlaboratory agreement was obtained for Cr, Co, Dy, and Sm
by INAA, and an analytical problem was noted for As and Zr. Additional work
is required to develop and validate reliable methods for B, F, Cd, and As.
ACKNOWLEDGMENTS
The authors thank Connie Wilkerson, Kirk Nielsen, and Ron Sanders of
Battelle Pacific Northwest Laboratory; Bob Meglen and Cincy Crouch of the
178
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‘Jniversity of Colorado; Hugh Millard of the U.S. Geological Survey; Bob
Giauque, Frank Asaro, and Al Smith of the Lawrence Berkeley Laboratory and
Robert Heft of Lawrence Livermore Laboratory for the analyses reported in
this paper. This work was supported by the Office of Health and Environ-
mental Research and the Division of Fossil Fuel Extraction of the U.S.
Department of Energy under contract Nos. EY-76-C-06-1830 (PNL); E(11-1)-4017
(COLO); and W-7405-ENG-48 (LBL).
REFERENCES
1. Fox, J.P., D.S. Farrier, and R.E. Poulson, Chemical Characterization
and Analytical Considerations for an In Situ Oil Shale Process Water.
LETC/RI-78/7, 1978. — ____
2. Paulson, R.E., J.W. Smith, N.B. Young, W.A. Robb, and T.J. Spedding.
Minor Elements in Oil Shale and Oil Shale Products. LERC RI-77/1,
1977.
3. Wildeman, T.R. and R.H. Meglen. The Analysis of Oil Shale Materials
tar Element Balance Studies. In: Analytical Chemistry of Oil Shale
and Tar Sands. Advan. in Chemistry Series, No. 170, 195-212, 1978.
4. Wildeman, T.R. Preparation and Fischer Assay of Standard Oil Shale
Sample. Preprints, Div. of PetrDl. Chem., ACS, 22(2):760-764, 1977.
5. Sandholtz, W.A. , F.J. Ackerman, A. Bierman, M. Kaehler, and J. Raley.
Run Summary for Small Retort Run S-il. UCID-l7855, 1978.
6. Penman, I. and F. Asaro. Potti ry Analysis by Neutron Activation.
Archaeometry. 11:21-52, 1971.
7. Heft, R.E. Absolute Instrument.31 Neutron Activation Analysis at
Lawrence Livermore Laboratory for the Environmental Research Program.
UCRL-80476, 1977.
8. Gordon, G.E. et al. Instrumental Activation Analysis of Standard Rocks
with High Resolution X-ray Detectors. Geoch. Cosm. Acta. 32:369, 1969.
9. Nielson, K.K. Matrix Corrections for Energy-Dispersive X-ray Fluores-
cence Analysis of Environmental Samples with Coherent/Incoherent
X-rays. Anal. Chem. 49:641, 1977.
10. Hebert, A.J. and K. Street, Jr. A Nondispersive Soft X-ray Fluores-
cence Spectrometer for Quantitative Analysis of the Major Elements in
Rocks and Minerals. LBL-1616, 1973.
11. Giaque, R.D., R.B. Garrett, and L.Y. Goda. Energy Dispersive X-ray
Fluorescence Spectrometry for Determination of Twenty-six Trace and Two
Major Elements in Geochemical Specimens. Anal. Chem. 49:62, 1977.
179
-------
12. Aifrey, A.C., L.L. Nennelley, H. Rudolph, and W.R. Smythe. Medical
Applications of a Small Sample X-ray Fluorescence System. In:
Advances in X—ray Analysis, Vol. 19, Gould, R.W., C.S. Barrett, J.B.
Newkirk, and C.O. Rudd (eds.). Proceedings of the Twenty-Fourth Annual
Conference on Applications of X-ray Analysis, University of Denver,
497-406, 1976.
13. Kubo, H., R. Bernthal, and T.R Wildeman. Energy Dispersive X-ray
Fluorescence Analysis of Trace Elements in Oil Samples. Anal. Chem.
50:899-903, 1978.
14. Hadeishi, 1. Isotope-Shift Zeeman Effect for Trace-Element Detection:
An Application of Atomic Physics to Environmental Problems. Appi.
Phys. Lett. 21:438, 1972.
15. Hadeishi, T. and R.D. McLaughlin. Isotope Zeeman Atomic Absorption, A
New Approach to Chemical Analysis. Am. Lab., August 1975.
16. Gadeishi, 1. and R.D. McLaughlin. Zeeman Atomic Absorption Determina-
tion of Lead with a Dua’ Chamber Furnace. Anal. Chem. 48:1009, 1976.
17. Hadeishi, 1., D.A. Church, R.D. McLaughlin, 8.0. Zak, M. Nakamura, and
B. Chang. Mercury Monitor for Ambient Air. Sci. 187:348, 1975.
18. Hadeishi, 1. and R.D. Mclaughlin. Zeeman Atomic Absorption Spectro-
metry. 181-8031, 1978.
19. Huffman, C., Jr. Copper, StronUum, and Zinc Content of U.S. Geologi-
cal Survey Silicate Rock Standards. U.S. Geological Survey Prof. Paper
600-B, BilO-Bill, 1968.
20. Tsunada, K., K. Fujiwara, and K. Fuwa. Subnanogram Fluorine Determina-
tion by Aluminum Monofluoride Molecular Absorption Spectrometry. Anal.
Chem. 49:2035, 1977.
21. Meglen, R.R. and A. Krikos. The Determination of Fluorine in Oil-Shale
Related Matrices Using Graphite Furnace Molecular Absorption. Pro-
ceedings of the EPA Oil Shale Sampling, Analysis, and Quality Assurance
Symposium, Denver, CO, March 26-28, 1979.
22. Wollenberg, H.A. and A.R. Smith. Geologic Factors Controling
Terrestria’ Gamma-Ray Dose Rates. In: The Natural Radiation Environ-
ment II, Adams, J.A.S., W.M. Lowder, and T.F. Gessell (eds.).
CONF-720805-P2, 457, 1972.
23. Jeffrey, P.G. Chemical Methods of Rock Analysis. Second ed., New
York, Pergamon Press, 1975.
24. Chan, C.C.Y. Improvement in the Fluorimetric Determination of Selenium
in Plant Materials with 2,3-diarninonaphthalene. Anal. Chim. Acta.
82:213, 1976.
180
-------
25. Ward, F.N. Determination of Molybdenum in Soils and Rocks, A Geo-
chemical Semimicro Field Method. Anal. Chem. 23:788, 1951.
26. John, M.K., H.H. Chauah, and H.H. Neufeld. Anal. Letters 8:559, 1975.
27. Stuckless, J.S. et al. A Comparison of Some Analytical Techniques for
Determining Uranium, Thorium, and Potassium in Granite Rocks. Jour,
Research U.S. Geological Survey 5:83, 1977.
28. Meyer, S.L. Data Analysis for Scientists and Engineers. New York,
John Wiley & Sons., p. 17.
29. Ondov, J.M. et al. Elemental Concentrations in the National Bureau of
Standards Environmental Coal and Fly Ash Standard Reference Materials.
Anal. Chem. 47:1102, 1975.
30. Wesch, H. and A. Bindl. Analysis of 11 Elements in Biological
Material. Comparison of Neutron Activation Analysis and Atomic Absorp-
tion Analysis. In: Accuracy in Trace Analysis: Sampling, Sample
Handling, Analysis, Vol. 1 Proceedings of the 7th Materials Research
Symposium, October 7-11, 1974.
181
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INTERLABORATORY. MULTIMETHOD STUDY OF AN IN SITU
PRODUCED OIL SHALE PROCESS WATER
D.S. Farrier and R.E. Poulson
Department of Energy
Laramie Energy Technology Center
Laramie, Wyoming 82071
J.P. Fox
Lawrence Berkeley Laboratory
Berkeley, California 94702
INTRODUCTION
Accurate measurement of chemical constitutents in waters from alterna-
tive fossil energy sources, such as oil shale, is essential to the orderly
and timely development of those energy resources. The technology necessary
to handle, contain, treat, utilize, and dispose of those waters and the
information needed to predict their environmental effects and to determine
regulatory compliance, require careful chemical characterization. This is
particularly important for in situ oil shale technologies because about 1
barrel of water may be coproduced with each barrel of oil.. 1
Reliable c iemical characterizations of synfuel process waters have been
difficult to obtain. This is due to the lack of adequate standards and
limitation of many available analytical methods. Concentrations of many
constituents fall outside the recommended ranges for published methods, or
chemical interferences produce inaccurate results. These problems have been
identified by many researchers faced with making chemical measurements. 2 5
They were first nationally acknowledged when the ASTM Committee on Water,
D-19, formed Subcommittee D-19.33 on “Water Associated with Synthetic Fuel
Production” to address analytical problems specific to alternative fossil
energy process waters.
The purpose of the •present work was to obtain a careful chemical
characterization of an oil shale process water designated for wide use in
environmental research and to determine the suitability of existing analyt-
ical methods for this characterization. The study was carried out using an
interlaboratory, multimethod approach. Samples from a larger volume, homo-
geneous reserve of an in situ oil shale process water were prepared and
submitted to 13 laboratories for the neasurement of major, minor, and trace
elements and standard water quality øarameters; a variety of instrumental
and chemical methods was used. This paper presents the characterization of
that water and discusses analytical problems specific to in situ oil shale
process waters.
182
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In Situ Oil shale Process Water
Water coproduced with shale oil and decanted from it is referred to as
oil shale process water. This water originates primarily from three
sources: combustion, dehydration of minerals, and groundwater’ The ratio
of water to oil ranges from 0.15 to 22, depending on the retorting atmos-
phere (air or inert gas) and the geographical location of the oil shale
reserve.’ This paper considers an air atmosphere process (combustion) and
the oil shale reserves near Rock Springs, Wyoming.
Simulated in situ oil shale process waters produced in laboratory scale
and pilot scale retorts have been characterized by several investigators. 2 ’ 7
Large variations in many measured parameters have been noted. 3 These
waters are brown to yellow in color, have a pH that ranges from 8.1 to 9.4,
and contain high levels of inorganic. nd organic constituer t 1 s. T ie p imary
in rganic constitutents are HCO 3 , SO 4 , S 2 0, SCN , F , Mg , Na , K , and
NH 4 . 7 The organic constitutents are primarily polar and the carboxylic
acids are a major organic group.
OMEGA-9
The oil shale process water used in this work is from the 1976 Rock
Springs Site 9 true in situ oil shale combustion experiment conducted by the
Laramie Eneray Technology Center (LETC). 8 This water has been designated
“Omega-9” (Ref. 9) and that descriptor will be used in this paper. The
chemical composition of this sample is specific only to itself and is not
necessarily representative of in situ oil shale waters in general. Never-
theless, the analytical problems encountered in the analysis of this sample
are typical of these waters due to a common matrix that includes high levels
of inorganic and organic N, 5, and C compounds.
Preparation
The acquisition, processing, and storage of Omega-9 are discussed in
detail by Farrier et al. 9 Briefly, 12,450 gal. of process water were col-
lected from a storage pond after 1 to 3 days residence; mixed, to ensure
homogeneity, by pumped recirculation through a storage vessel; and pressure-
filtered in the field through to in-line cartridge-type membrane filters
with a nominal 0.4-pm exclusion. The materials in direct contact with the
sample were either an inert epoxy coating, inert plastic, or stainless
steel. The filter catridges were constructed of polypropylene. The up-
stream filter material was a compressed matrix of borosilicate microfiber-
glass with an acrylic resin binder, and the downstream filter was cellulose
esters cast onto a cellulose web. The filter sample was partitioned into
415 polyethylene-lined, 30 gal. drums and stored at 4°C. Each laboratory
participating in the study received a 500 ml sample from one of four of
these drums.
183
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Homogeneity
The homogeneity of the resulting sample with respect to some of the
parameters evaluated in this study was investigated by randomly selecting
three 30 gal. drums for detailed analysis. Aliquots from each drum were
analyzed for representative major, minor, and trace elements and water
quality parameters by two participating laboratories using techniques of
known high precision. The results of those analyses are summarized in Table
1. The entries. in Table 1 are average concentrations plus or minus 1 stan-
dard deviation. The number of analyses included in the average is shown in
the second column. All parameters for each barrel agree to within 2 stan-
dard deviations. These data suggest that Omega-9 is homogeneous.
Stability
Stability of oil shale process waters is a significant concern. Most
researchers have noted that samples stored at >4°C to 40°C develop consider-
able turbidity after several days. This turbidity is composed primarily of
stressed rod-shaped bacterial cells. 9 These cells have a large adsorptive
capacity and, within 10 days, removes significant amounts of the elements
Br, Se, As, Fe, Ni and Hg from filtered samples stored at room temperature. 5
The stability of Omega-9 water with respect to these visual changes,
microbial growth, and organic content was investigated by Farrier et al. 9
and Felix et al.’° The work of Refs. 9 and 10 indicated that storage at 4°C
stabilized the water’s organic content by inhibiting microbial growth.
Therefore, the loss of chemical constituents due to adsorption on bacterial
cells would also be significantly lessened.
An additional concern with aqueous samples is the loss of constituents
by adsorption onto container walls or precipitation reactions. These
effects are usually minimized by acidifying the sample to pH <2 with con-
centrated HN0 3 .’ 12 13 Such acidification was not possible in this case.
The sample is...highly buffered by the CO 3 and NH 3 systems and contains high
levels of S 2 O . Acidification results in the precipitation of elemental S
and organic acids. The precipitates act as adsorbents for some elements,
interfere with most analytical measurements, and result in an inhomogeneous
sample. Because the sample is well buffered, relatively large volumes of
acid are required; as a result, the acid further dilutes many low level
constitutents, and may contaminate the sample.
Stability of Omega-9 water for select major, minor, and trace elements
was investigated by several participating laboratories. No change was noted
in elemental content on storage in polyethylene-lined containers for up to 1
year at 4°C.
EXPERIMENTAL
A 500 ml aliquot of Omega-9 water, contained in an opaque plastic
container, was sent to each of the 13 participating laboratories. Labora-
tories were seected to provide a mix of research-grade analyses, such as
184
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Table 1. HOMOGENEITY TEST OF OMEGA-9 (mg/u except as noted)a
Parameter
Number
of
Measurements
Barrel 1
Barrel 2
Barrel 3
ELEMENTAL ANALYSESb
Antimony (NAA)
Calcium (AAS)
Copper (AAS)
Iron (AAS)
Lithium (AAS)
Magnesium (AAS)
Silicon (AAS)
Silver (AAS),pg/Q
Sodium (AAS)
Zinc (AAS)
1
1
1
1
1
1
1
5
1
1
2.02 ± 0.05
20.3 ± 0.3
0.09 ± 0.03
1.70 ± 0.20
0.19± 0.01
22.2 ± 0.2
5.2 ± 0.7
2.74 ± 0.59
4400± 100
0.30 ± 0.01
2.03 ± 0.05
19.2 ± 0.3
0.07 ± 0.03
1.49± 0.20
0.18 ± 0.01
21.9 ± 0.2
5.2 ± 0.7
3.42 ± 0.38
4200± 100
0.35 ± 0.01
2.03 ± 0.05
16.9 ± 0.3
0.04 ± 0.03
1.49± 0.20
0.18 ± 0.01
22.2 ± 0.2
5.2 ± 0.7
2.79 ± 0.35
4300 ± 100
0.30 ± 0.01
WATER QUALITY PARAMETERS
1
3
3
1
1
1
16,900
3650 ± 365
1050± 210
4935
25,200
8.80
16,900
3630 ± 365
1310 ± 260
5120
25,600
8.80
16,000
3790 ± 380
1032 ± 210
5105
23,500
8.86
aNote: Indicated errors are one sigma for replicate
statistics (NAA) or signal background (AAS).
analyses. If a single measurement is reported,
the error is counting
(X
U i
Alkalinity, total (as CaCO 3 )
Carbon, inorganic
Carbon, organic
Chemical oxygen demand
Electrical conductivity (pmhos/cm)
pH
bNAA = neutron activation analysis; AAS = atomic absorption spectroscopy.
-------
those performeu at Department of Energy national laboratories, and routine
analyses, such as are available at many commercial establishments. Most
laboratories selected had prior in-depth experience analyzing a wide variety
of environmental samples, including oil shale materials. The participating
laboratories were coded to maintain anonymity.
Six instrumental methods were selected for detailed elemental analyses:
neutron activation analysis (NAA); X-ray fluoresence spectrometry (XRF);
spark source mass spectrometry (SSMS); optical emission spectroscopy (OES);
plasma emission spectroscopy (PES); and atomic absorption spectroscopy
(AAS). Sample preparation techniques and the suite of elements measured
were left to the discretion of each laboratory. Reported results include
uncertainties due to both the analysis itself and the method of sample
preparation.
The measured Water_quality parameters include alkalinity, biochemical
oxygen demand (BOb), Q0 3 , HCO 3 , organic and inorganic C, conductivity, CN
hardness, NH 3 , NH 4 , NO 3 , organic N, Kjeldahl N, oil and grease, pH, phenols,
PO , solids, chemical oxygen demand (COD), and S species. The best analyti-
cal method and sample pretreatment were left to the discretion of each
laborat ory. In most cases, Standard Methods” or EPA Methods 12 were used.
The instrumental and chemical methods used to measure major, minor, and
trace elements and water quality parameters in Omega-9 water are summarized
in Tables 2 and 3. Additional information is available in Ref. 7.
RESULTS
The detailed analyses of major, minor, and trace elements are presented
in Table 4, and of water quality parameters in Table 5. Inspection of these
data indicates that there is a wide spread in values for many elements and
water quality parameters. Therefore, a statistical technique 32 was used to
provide a basis for discarding outlying values. The result of applying this
technique to the individual values in Tables 4 and 5 is summarized in Table
6. This table presents the best value, in the judgement of the authors, for
72 elements and 28 water quality parameters.
The procedure used to analyze the data was as follows. Measurements
made using a technique with known interferences were discarded. These are
documented in the footnotes to Table 6. Dixo&s technique was then applied
to the remaining data to reject outliers. 32 This method expresses the gap
between an outlier and the nearest value as a fraction of the range from the
smallest to the largest value. The value of this fraction provides the
basis for rejection. A range was reported when the coefficient of varia-
tions was 100%. If only upper limits were reported, the smallest upper
limit was chosen except when SSMS was the analytical method. In that case,
the reported upper limit was multiplied by 3 to account for a maximum factor
of 3 variability noted for that technique in this study. When there were
only two measurements, and when they diverged, the choice between them was
based on conversations with the individual analysts. Those cases are docu-
mented in the footnotes to Table 6. Best values based on single measure-
186
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Table 2. SUMMARY OF INSTRUMENTAL METHODS USED FOR THE ANALYSES OF OMEGA .9
2 Direct analysis of
liquid
3 Direct analysis of
liquid
Special
Features
2 irradiations and 5
decay/counting
measurements
2 irradiations and 5
decay/counting
measurements
2 irradiations and 3
decay/counting
measurements
1 irradiation and 3
decay/counting
sequences
energy-dispersive system
with Mo x-ray tube;
counted for 20 mm
energy-dispersive system
with Ag secondary
source; counted for
100 mm
Elements
Detected
Sb, As, Ba, Cs, Cl, Co.
Hf, Fe, Mo, Ni, Rb,
Sc, Se, Ag, Na, Th,
U, Zn
Al, Sb, As, Br, Cl, Sc,
Se, Na
Sb, As, Br, Cl, Co, Cu,
Mn, Mo, Na, U
Sb, As, Br, Cl, Co, Fe,
Sc, Se, Na, Sr, U, Zn
As, Br, Ca, Cu, Fe,
Mn, Ni, Rb, Se, Sr,
Ti, U, V, Zn, Zr
As, Br, Ca, Cu, Mo,
Rb, Se, Zn, Zr
AAS
D 3 Ref. 15
flame atomization;
correction for Na matrix
Na, Mg, Si, Fe, Li,
Ca, Cu, Zn
AAS E
AAS F
AAS G
AAS
AAS
2 Ref. 12, 16
1 Ref. 12
1 Ref. 12, 1]
H 3-10 Ref. 15, 18
2 Ref. 19
flame atomization except
K, Na by flame emission
flame atomization
flame atomization
flame atomization
flame atomization;
correction for Na matrix
Ca, K, Mg, Na, Hg
Na, K, As, Se, Hg, Zn,
Ca, Mg, Al
Na, K, Ca, Mg, Se, Pb, Cd
I
Mn, Ni, Zn, K, Fe, Ca, Sn
Ca, Mg
Instrumental
Technique
No. of Sample
Laboratory Replicates Preparation
NAA
A
1
Evaporation at 80°C
NAA
B
NAA
C
1
Direct analysis of
liquid
NAA
B
XRF
A
3
Freeze dried
XRF
B
3
Air dried
XRF
N
1
Direct analysis of
liquid
SSMS
E
2
Carbon slurry dried
with infrared lamp
PES
D
3
Direct analysis of
liquid
OES
F
1
Evaporation, ignition
at 450°C and
grinding
AAS
A
3
Direct analysis on
liquid except Hg
which was evaporated
at 80°C
AAS
C
2
Digestion; Ref. 14
wavelength -di spersi ye
system with Pt x-ray
tube; counted for 100 sec
me fractions analyzed
by ion-sensitive photo.
plates and the disappear-
ing line technique
System used Ar plasma
jet and Echelle grating
spectrometer
D.C. arc source coupled
to grating spectrographs
Zeeman AAS; graphite
rod atomization
flame atomization;
corrections for matrix
effects
Cl
Al, Sb, As, Ba, Br, Cd,
Cs, Cr, Co. Cu, Ga,
Ge, Hf, I , Fe, La, Pb,
Mn, Mo, Ni, Nb, P, Pr,
Rb, Se, Si, Ag, Sr. Ta,
Te,Sn,Ti,W, U, V.
Y, Zn, Zr
As, Ba, B, Ca, Cu, Mg,
Mo, P, Si, V. Zn
Sb, Ba, B, Cr, Co, Fe,
Pb, Li, Mn, Mo, Rb,
Sr, Ti, V. Zr
As, Se, Cd, Ag, Hg
Ca, Mg, Na, K, Fe, Si,
As, Se, Sb
187
-------
Table 3. SUMMARY OF CHEMICAL METHODS USED FOR THE ANALYSIS OF OMEGA .9
Chemical
Parameter Laboratory Method Interferences Reference
Alkalinity A, F, J, N Titrimetric Soaps, oils 11, 12
Arsenic N Ag diethyldithiocarbonate Co, Hg, N , Ag, Cu, Cr, Mo, Sb 11
BOD 5 F 5day incubation Various toxicants 12
Boron C Dianthrimide method Unknown —
I Unknown 14
E Unknown 11
Calcium J EDTA titrimetric PO , Ba, Sr, alkalinity 11
Carbon (HCO . C0) C, F, H, I, K Computed from alkalinity NH 3 , B, Si, organic bases 11, 12
Carbon, inorganic A, C Unknown 11
H Unknown 20
Carbon, organic K, C Sealed ampoule Unknown —
H, M, N Direct Volatile organics 11
A Indirect Unknown 11
COD A, F,J,t Chemical oxidation S 2 O,S 4 O 11,12
N Chemical oxidation S20, S4O 21, 22
Chloride F, H, J Hg(N0 3 ) 2 titration Organics, 1. B( 11, 12
E — 23
C, I Technicon Autoanalyzer B(, r, SCN
Conductivity A, 0, I Instrumental Soaps, oil, grease 11, 12
Cyanide F Colorimetric Color 12
C. N Distillation/specific Fatty acids 11
ion electrode
Fluoride D, E, F, G, N Specific ion electrode Unknown 11, 12
C Technicon Autoanalyzer/ Unknown —
specific ion electrode (C) or
Technicon Autoanalyzer (N)
SPADNS Unknown 12
Hardness H EDTA titration Unknown 11
Computed — —
Magnesium J Computed See Ca, hardness 11
Nitrogen, ammonia A, H, J Distillation/titrimetric Amines 11
C Distillation/idophenol Unknown 12
J, N Specific ion electrode Amines 12, 24
Nitrogen, Kjeldahl C Technicon Autoanalyzer Unknown —
F, H, I Distillation Amines plus others excluded 11, 12, 25
Nitrogen, organic J Oisti llation/titrimetric Amines plus others excluded 11
H Computed Amines plus others excluded —
Nitrogen, nitrate F Colorimetrically SCW 12
Oil and grease C Freon extraction Organics 12
pH A, C, F, G, Electrometrically Soaps, oils, grease 11, 12
H, I, J, N
Phenols A, C, F, .1, N Cotorimetrically Para substituted phenols 11, 12
Phosphorus, J Stannous chloride Si0 2 , As, F: S 2 O , SCW 11
orthophosphate F Colorimetrically Unknown 12
Phosphorus, total C, F Technicon Autoanalyzer Unknown 12
Potassium I Technicon Autoanalyzer Unknown
Silicon I — Unknown 14
Sodium I Technicon Autoanalyzer None known —
J Specific ion electrode Unknown - —
Solids A,F,G,H, Gravimetric - NH 3 ,NH ,HCO,CO 11,12
I, J. N
Sulfur, sulfate A,C, F,J Turbidimetric None known 11,12
0, N Gravimetric None known 12
Chloranilate None known 12
Sulfur, sulfide A, N Titrimetric S compounds, volatile organics 12, 26
C Qualitative None known 11
Sulfur, sulfite F Titrimetric Organics 12
Sulfur, thiocyanate A, C Colorimetric None known 11
Sulfur, tetrathionate C Colorimetric Unknown 27, 28
Sulfur, thiosulfate C — Unknown 27, 28
J Titrimetric Unknown —
Sulfur, total C Digestion Unknown 29
E, H Gravimetric None known 30
Uranium G None known 31
188
-------
ments are enclosed in parentheses; these values are uncertain and require
additional analysis for validation.
The use of this procedure with the elemental data (Table 4) resulted in
the rejection of seven measurements as outliers and of six others due to
chemical interferences. For the water quality parameters (Table 5), one
measurement was rejected as an outlier and ten were rejected due to chemical
interferences. Ranges were reported for six elements and three water quali-
ty parameters.
Elemental Characterization
The best values in Table 6 indicate that of the 72 elements measured in
Omega-9 water (1) 32 were detected by two or more laboratories or techniques
and fair agreement was obtained; (2) a range was reported for six elements;
(3) 22 were below the detection limit of all techniques used; and (4) only a
single measurement was used for an additional 12 elements.
The coefficient of variation reported in the last column of Table 6
demonstrates the agreement obtained among different laboratories and tech-
niques. The average coefficient of variation for the 32 elements measured
was 30%. Of those 32, the coefficient of variation was 10% for 3 elements
(Ci, Cr, Na); 10% - < 20% for 7 elements (Br, F, Mo, K, U, Zn, K); 20% -
< 30% for 7 elements (Sb, As, B, Hf, Fe, Rb, Sc); and 30% for 15 elements
(Ba, Cd, Ca, Co, Cu, Mg, Mn, Ni, P, Se, Si, Ag, Sr, S, Zr).
Although the 30% average coefficient of variation obtained in this
study is large compared with that obatined in some intercomparison studies
using other sample types, 34 the results are encouraging. The present sample
is highly contaminated, chemically complex, and the concentration of many
measured constituents is close to the detection limit of applied techniques.
The average concentration for 29 elements measured by two or more techniques
is 6.3 mg/i. Additionally, other intercomparisons have focused on a single
instrumental method. 34 This study employed six separate analytical tech-
niques for whicr a wide range of sample preparation methods was used. Thus,
the sources of variability include not only instrumental error and sample
handling, but uncertainties due to different sample preparation methods.
A range was reported for Al, Li, Pb, Hg, Sn, and Ti. The large varia-
tions for these elements are probably due to interferences or to sample
handling and preparation methods. Since all of these elements are environ-
mentally important, work should be directed at discovering the source of the
variability and correcting it.
Water Quality Parameters
The best values in Table 6 indicate that of the 28 water quality
parameters measured in Omega-9 water, 16 were detected by two or more labor-
atories and fair agreement obtained; a range was reported for 3; 1 was below
the detection limit; and 8 were measured by only a single laboratory.
189
-------
Table 4. ELEMENTAL ANALYSIS OF OMEGA9 (mg/u)
Aluminum — 19.1±4,1 — — <420
Antimony — — 1.81±0.36 1.81 2.03±0.03 166±0,16
Arsen ic 0.92±0.02 1.09±0.02 0.84±0,18 0,88 1.11±0.03 1.3±0,3
Barium 0.83±0.17b — < 4.4 — — 0.41±0.24
Bevy Ilium — — — — —
Bismuth — — — — —
B0r ’n — — — -‘ —
bromine 2.10±0.08 2,44±0,1 2,01±0,42 3,0 2.65±0.09
Cadmium - — < 3.3 — --
Calcium 12.4±0.8 7.5±1,4 <410 — —
0.30±0.06 <0.03
-------
Table 4. CONTINUED
Instrumental Mathodsa
Spark Source
Element
X-ray Fluorescence
Spectrometry
Instrumental Neutron Activation Analysis
Mass
Spectrometry
Emission Spectroscopy
Atomic Chemical
Absorption and other
Element
E
Optical DC Plasma
F D
Spectroscopy Methodsa
A D
B C D A
Scandium
Selenium
— —
018±0.01 0.18±0.03
0.00145±0.00036 — 0.0011±2% 0.0010±0.0003
0.38±0.08 — 0.17±0.04 0.25±0.03
<0.01
0.094±0.000
<0.01 —
— —
— —
—
Scandium
Selenium
Silicon
Silver
— — .
— —
<3900 — — —
< 0.28 — — 0.0044±0.0014
18±4
0.0025±0.0001
92±04
<0.01 —
98 C 51 ± 07 0 40 F 2.0
o. 0 029 ±o.Q O O 5 A ’(o.iG
Silicon
Silver
Sodium
— —
4210±840 4550 4503±24 4530±130
— —
j 4500’,3685±212J
Sodium
Strontium
1.12±0.05 —
<24 — 1,03±0.07 <16
1,6±0.1
0.72±0.07 —
— —
Strontium
Sulfur
— —
<27000 ‘
“-
— ._
— 234 oC, 989 ± 14 E, 2700 H
Sulfur
Tantalum
— —
K 0.013 — — <0.0003
0,045±0.025
— —
— —
Tantalum
Tellurium
— —
< 1.5 — — —
0.001
— —
— —
Tellurium
Terbium
— —
K 0.0065 — — <0.0009
—
— —
— —
Terbium
Thallium
—
<450 — — —
<0.002
<0.01
— —
Thallium
Thorium
<0.063 -.
< 0,024 — — 0.0037±0.0003
<0.006
— —
.-
Thorium
Thulium
—
<0.013 — — .,‘
—
-., —
—
Thulium
Tin
—
K 5.3 — — —
0.001
<0.01 —
10 N —
Tin
Titanium
0.14±0.10
<71 — — <43
1.3±0,0
0.03±0,003 <0.02
2 H
Titanium
Tungsten
Uranium
—
0.59±0.03
- .
<0.62
< 0.91
—
0.51
—
0.52±0.07
<0.15
0,41±0.02
0,010±0.000
1.08±0.18
—
—
—
—
—
—
0.650
Tungsten
Uranium
Vanadium
0.11±0.08
—
< 0.89
‘-
<5
0.068±0.000
0.04±0.004
0.13±0,01
—
Vanadium
Ytterbium
—
—
< 0.025
. .
<0.002
<0.005
<0.01
Ytterbium
Yttrium
<0.05
—
<2800
—
—
—
0.001±0,000
<0,01
—
—
—
Yttrium
Zinc
0.33±0.04
0.30±0.11
K 3
—
0.33±0.01
0,26±0.06
0.7±0,4
—
0.34±0.02
k
—
Zinc
Zirconium
0.49±0.27
0.88±0.03
<2000
.
1.0±0,0
0.51±0.05
,,.
Zirconium
aSuperscript letters A through N are coded descriptors for the laboratories making measurements.
bBa measurement made on a different n.ray system by measuring the Ba KO strays induced in the sample prepared by laboratory A for NAA analysis with the 60 KeV gamma ray of Am.
c 8 oC 17 ± 2 D, 6 . 45 ±o. 07 E, 19 F, 14 G , 121, 121 N
d 3900 C 2530 ±i 27 E, 950 F, , 685 ± 505 H , 4100±285 , 3677 ± 55 J, 22 N
e 560 53 0 68 ± 0 E 77 F 680 56 t 68 N
12 c, 22.2±0.30, 26.5±0.78. 200. 104 H , 19k, 1 93N
0.021 ±o.oo3A. < 00002 E
-------
Table 5. ANALYSIS OF OMEGA-9 FOR WATER QUALITY PARAMETERS (mgIQ)
Lboratory 8
Parameter
( C) ( F) ( G )
— 15,600
740 —
(H)
(I)
( N ) Other
16,600 —
Alkalinity
Biochemical Oxygen Demand,
5-day
Carbon, Bicarbonate (as HcO;)
• Carbonate (as C0)
• Inorganic (as C)
• Organic (as C)
Chemical Oxygen Demand
Conductivity (pmhos/cm)
Cyanide (as CNfl
Hardness, Total (as CaCO 3 )
Nitrogen, Ammonia (as NH 3 )
• Ammonium (as NH )
Kjeldahl (as N)
,Nitrate (as NO )
Organic (as N)
Oil and Grease
pH
Phenols
Phosphorus, Orthophosphate
(as POT)
Solids, Fixed
Solids, Total
Solids, Total Dissolved
Sulfur. Sulfate (as SO )
Sulfide (as S)
Sulfite (as SI
Tetrathionate (as S 4 O )
Thiocyanate (as SCN1
Thiosutfate (as S 2 O )
(A)
16,600 ± 520
3690 ± 86
1130± 160
5052 ± 83
24,800± 1100
4070 ± 90
3290 ± 75
8.82 ± 0.03
56 ± 2
13,721 ± 10
2020± 160
116± 12
110±2
15,100
15,300
—
13,255± 920
16,000
2100
660
—
3020± 780
0
3400
—
—
2917±231
—
920
—
—
780
—
—
7700
—
—
18,000
—
—
18,200
—
18,100± 850
0.90
0.42
—
—
—
—
—
—
62±6
110
—
—
—
3218±0
—
4890
—
—
2321
3400± 140
4000
3400
.-
3280± 164
3000
- .
0.11
—
—
—
—
—
—
630
—
580
—
8.5
8.6
59
110
(J)
16,100 ± 344
5679 ± 481
3846 ± 95
3300 ± 80
148 ± 28
1300
4154
2.9
3643
1035 ± 104 K, 851 ± 18 M
4198 ± 68 O
3600 ±
8.5
9.0 ± 0.1
6.7
0.08
—
—
—
24.6
—
—
—
—
—
13,135±50
—
—
14,200
14,340±40
—
14,100±494
—
13,900
14,200
14,340± 40
14,400
—
2040
1200
1890
2500
1900
1710±80
8.2
8.7
8.9
—
—
29.4
45
—
0.0 —
— 925
280
136
2225
14,200
1875
176
3260
aLetters A-N are coded descriptors for laboratories making the meesurernents,
-------
Table 6. CHARACTERIZATION OF OMEGA-9 TRUE IN-SITU SHALE PROCESS WATER (mg/i)
Totala Included in Best Value
Number Numbera Number Number Coefficient
of of of of Best Valueb of
Element Measurements Measurements Labs Techniques (mg/Q) Variation
ELEMENTAL ANALYSES
Aluminum 6 6 6 6 <0.03 . 19.1
Antimony 7 7 6 4 1.9 ± 0.5 28%
Arsenic 12 12 7 6 1.0 ± 0.2 22%
Barium 7 5 4 3 0.71 ± 0.33 47%
Beryllium 2 1 1 1 <0.006
Bismuth 2 1 1 1 <0.01
Boron 6 6 6 4 27 ± 7 26%
Bromine 6 6 4 3 2.4± .4 18%
Cadmium 6 2 2 2 0.0016 ± 0.0008 53%
Calcium 14 12 10 4 12±4 35%
Cerium 2 1 1 1 <0.026
Cesium 4 1 1 1 (0.0021 ± 0.OOO3)
Chlorine 11 5 5 2 824 ± 61 d
Chromium 5 2 2 2 0.02 ± 4% 3.6%
Cobalt 8 5 5 3 0.030± 0.012 40%
Copper 10 7 4 3 0.10± 0.04 44%
Dysprosium 3 1 1 1 <0.006
Europium 3 1 1 1 <0.0013
Fluorine 8 7 7 3 60±9 16%
Gallium 5 1 1 1 (0.004 ± 0.000)
Germanium 4 1 1 1 (0.013 ± 0.004)
Gold 3 1 1 1 (0.005
Hafnium 3 2 2 2 0.015 ± 0.003 23%
Holmium 1 1 1 1 <0.063
Indium 3 1 1 1 <0.01
Iodine 2 1 1 1 (0.59 ± 0.30)
Iridium 3 1 1 1 <0.00006
Iron 10 9 5 5 1.2 ± 0.3 25%
Lanthanum 4 1 1 1 (0.006 ± 0.001)
Lead 5 2 2 2 0.0045 0.02
Lithium 2 2 2 2 0.18 . 0.8
Lutecium 3 1 1 1 <0.005
Magnesium 10 9 8 3 20 ± 6 30%
Manganese 8 4 4 4 0.09 ± 0.04 44%
Mercury 8 4 4 1 0.0003 - 0.021
Molybedenum 7 5 5 4 0.60± 0.07 11%
Neodymium 3 1 1 1 <0.009
Nickel 8 4 3 4 0.06 ± 0.02 38%
Niobium 2 1 1 1 (0.002 ± 0.000)
Osmium 2 1 1 1 <0.06
Palladium 2 1 1 1 <0.05
Phosphorus 5 4 4 3 3.2 ± 2.6 83%
Platinum 2 1 1 1 <0.08
Potassium 9 7 7 2 47 ± 9 19%
Praseodymium 2 1 1 1 (0.0020 ± 0.0014)
Rhenium 1 1 1 1 <0.024
Rhodium 2 1 1 4 <0.015
Rubidium 6 4 2 3 0.16 ± 0.04 25%
Ruthenium 2 1 1 1 <0.042
Samarium 3 1 1 1 <0.0013
Scandium 5 3 3 1 0.0012 ± 0.0002 20%
Selenium 10 10 8 3 0.21 ± 0.11 53%
Silicon 7 6 5 4 8± 6 72%
Silver 6 3 2 3 0.003 ± 0.001 31%
Sodium 13 12 11 3 4333± 244 5.6%
Strontium 6 4 4 4 1.12 ± 0.36 33%
Sulfur 4 3 3 2 2010± 900 45%
Tantalum 3 1 1 1 (0.045 ± 0.025)
Tellurium 2 1 1 1 (0.001)
Terbium 2 1 1 1 <0.0009
Thallium 3 1 1 1 <0.006
Thulium 1 1 1 1 <0.013
Thorium 4 1 1 1 (0.0037 ± 0.0003)
Tin 4 4 4 4 0.001-10
Titanium 7 7 6 6 <0.02.2
193
-------
Table 6. CONTINUED
To Ia Included in Best Value
Number Number a Number Number CoefficIent
of of of of Best Valueb of
Element Measurements Measurements Labs Techniques ( mg/Q ) Variation
Tungsten 3 1 1 1 (0.010± 0.000)
Uranium 7 5 4 3 0.55± 0.07 13%
Vanadium 6 2 2 2 0.12± 0.01 12%
Ytterbium 4 1 1 1 <0.002
Yttrium 4 1 1 1 (0.001 ± 0.000)
Zinc 11 9 5 5 0.31 ±0.04 13%
Zirconium 5 4 3 4 0.73 ± 0.25 35%
WATER QUALITY PARAMETERS
Alkalinity (asCaCO 3 ) 4 4 4 1 16.200± 480 3.0%
Biochemical Oxygen
Demand, S-day 1 1 1 1 (740)
Carbon, Bicarbonate (as HCO 3 ) 4 0 0 0 (l5,940 )e
Carbon. Carbonate (as C0 3 ) 4 0 0 0 (500)
Carbon, Inorganic (as C) 3 3 3 2 3340 ± 390 12%
Carbon, Organic (as C) 6 6 4 3 1003 ± 192
Chemical Oxygen Demand 5 4 4 1 8100 ± 5700 70%
Conductivity ’(J.Lmhos/cm ) 3 3 3 1 20,400 ± 3840 19%
Cyanide (as CN ) 2 2 2 2 0.42 2.9
Hardness, Total (as CaCO 3 ) 2 1 1 1 (1 10)
Nitrogen, Ammonia 9 (as NH 3 ) 5 5 5 3 3795± 390 10%
Nitrogen, Ammonium (as NH ) 6 6 5 3 3470 ± 830 24%
Nitrogen, Kjeldahl (as N) 4 4 4 2 3420 ± 420 12%
Nitrogen. Nitrate (as NO;) 1 1 1 1 (0.17)
Nitrogen,Organic (as N) 2 2 2 2 148 - 630
OilandGrease 1 1 1 1 (580)
pH 8 8 8 1 8.65 ± 0.26 3.0%
Phenols 5 5 5 1 60±30 51%
Phosphorus, Orthophosphate
(as P0) 3 3 3 3 0.08 - 24.6
Solids. Fixed 2 2 2 1 13,430 ± 415 3.1%
Solids, Total 3 3 3 1 14,210 ± 120 0.85%
Solids, Total Dissolved 5 5 5 1 14,210± 193 1.4%
Sulfur, Sulfate (as SO ) 8 7 5 3 1990 ± 250 13%
Sulfur, Sulfide (as SI 3 1 1 1
Sulfur. Sulfite (asS) - 1 0 1) 0 <20’
Sulfur, Tetrathionate (as S 4 O ) 1 1 1 1 (280)
Sulfur,Thiosulfate(asS 2 O ) 2 2 2 2 2740± 730 27%
Sulfur, Thiocyanate las SCN) 2 2 2 1 123 ± 18 15%
a The first column is the total number of measurements including upper and lower limits. The second column is the number of measure-
ments used to compute the best value.
b The following rules were used to determine best values: (1) The smallest upper limit is reported unless that upper limit is for SSMS. In
that case, the SSMS upper limit is multiplied by 3. (2) A range is reported if the coefficient of variation is greater than 100%. (3) Best
values based on a single measurement are enclosed in parentheses. (4) Best values based on 2 or more measurements are determined using
Dixon’s procedure (32) following exclusion of values resulting from analytical errors. The reported error is 1 standard deviation if the
number of measurements is greater than 1. Otherwise, it is the error reported by the laboratory making the measurements.
C The NAA value was selected based on conversations with the individual analysts.
d The measurements made using the Technicon Autoanalyzer and the mercuric nitrate methods were excluded due to interferences.
e Calculated using methodology shown in Table 8 and for CT 3336 mg/Q. pH = 8.6.
Total hardness is the sum of polyvalent cations reported as CaCO 3 - The reported value is consistent with value computed from Ca and Mg
analyses reported in Table 6.
This is the sum of NH 3 and NH .
The presence of a very low sulfide level was verified by laboratories A and C using the qualitative AgS precipitation test 39
The method used to measure sulfite has strong interferences. Based on qualitative analyses made by laboratory C. the sulfite level is
<2OmgIQ (Ref. 39).
194
-------
Quantitative data based on two or more measurements iere obtained for
alkalinity, organic nd inQrganic_ C, conductivity, NH 3 , NH 4 , Kjeldahl N, pH,
phenols, solids, SO 4 , S 2 0 3 , SCN , and COD. The coefficient of variation
reported in the last column of Table 6 demonstrates the agreement obtained
among different laboratories and techniques. The average coefficient of
variation for the 16 parameters is 18%, significantly better than the 30%
coefficient obtained for the elemental analyses. However, in general, the
accuracy obtained for the water quality parameters is poorer than that for
the elements. (This will be discussed in the section on “Analytical Con-
siderations.” Of these 16 parameters, the coefficient of variation was < 5%
for S parameters (alkalinity, pH, solids); 5% - < 20% for=7 parameters
(inorganic and organic C, conduc ivity, HN 3 , Kjejdahl N, SO 4 , SCN ); and
20% for 4 parameters (COD, NH 4 , phenols, S 2 0 3 ). However, the average
concentration of 13 water_quality parameters measured by two-or more labor-
atories (pH, phenols, SCN excluded) is 8,200 mg/i, which is 1,300 times
higher than that of the average concentration for 29 elements (6.3 mg/l).
The results obtained for CN, organic N, and PO varied widely and only
a range is reported in Table 6. Coefficients of variation greater than 50%
were obtained for phenols and COD. The variability in these parameters is
probably due to significant interferences and/or stability problems.
Relative Instrumental Performance
An approximate criterion of performance for each laboratory and instru-
mental technique is summarized in Table 7. Table 7 presents the mean,
standard deviation, coefficient of variation, and uncertainty in the coef-
ficient of variation for normalized measurements. Normalized measurements
were computed by dividing each value in Tables 4 and 5 by the best value
from Table 6. Only elements or waters quality parameters detected by two or
more laboratories or techniques for which a coefficient of variation is
reported in Table 7 are included in the normalized measurements. The coef-
ficient of variation is a measure of accuracy for the elemental analyses;
the normalized mean, if significantly different from 1, indicates systematic
errors of measurement. Performance increases as the normalized mean
approaches 1 and as the coefficient of variation decreases.
Because of the uncertainties in the true value of the abundances of an
element when determined by averaging the results of different laboratories,
there is an uncertainty in the coefficient of variation. This uncertainty
is reported in the last column of Table 7. Therefore, small differences may
not be significant. Of the 11 laboratories/techniques used for elemental
analyses, 8 have coefficients of variation between 15% and 30% and three
have a coefficient of variation between 50% and 70%. There is no statisti-
cally significant difference in the performance within each of these groups,
but there is between the groups. Thus, the performance of XRF, NAA, PES,
and AAS in this study was significantly better than that of SSMS, OES, and
other methods. Similarly, of the eight laboratories reporting water quality
analyses, five have coefficients of variation between 15% and 25% and two
have coefficients of variation between 35% and 40%. The coefficient of
variation for the eighth laboratory, G, falls into a group of 1. Thus,
195
-------
there was a statistically significant difference in performance for analysis
of water quality parameters.
The NAA, XRF, and AAS results are the most consistent and accurate of
the instrumental techniques evaluated. OES, PES, and SSMS have normalized
means significantly different from 1, suggesting systematic errors. How-
ever, SSMS detected more elements than any other technique evaluated and
consistently had the lowest detection limit. The “other 11 techniques shown
in Table 7 include specific ion electrode and colorimetric and wet chemical
measurements. The deviant mean and high coefficients of variation for these
measurements are due primarily to chemical interferences encountered with
the chlorine measurements.
ANALYTICAL CONSIDERATIONS
Many of the analytical techniques investigated in this study are inade-
quate for the analysis of complex matrices such as oil shale process waters.
Standard analytical methods including Standard Methods,” EPA’s methods,’ 2
ASTM methods, 25 and USGS methods’ 4 are often not applicable to these types
of waters due to the interferences and to the extremely high or low levels
of many parameters. Each method should be evaluated on a case-by-case basis
when used for highly complex samples. Nevertheless, most participating
laboratories used these methods without modification. This points to the
urgent need to develop and publish methods specific to complex sample types
not heretofore widely analyzed.
Although many of the wet chemical techniques evaluated gave reproduc-
ible results, the ccuracy pf measurement w s poor due to interferences.
This is true for Cl , S , SO , solids, and CO 3 .
The primary interferences for wet chemical measurements are high con-
centrations of organic or inorganic S, C, and N compounds; the presence of
strong color and emulsified oil and grease; and the diversity of organic
compounds. Some C, N, and S compounds combine with analytical reagents,
producing erron .ous r.esults. This type of interference affects the measure-
ment of COD, S, SO 3 , and Cl . The presence of color and oil and grease
interfere with some colorimetric and electrode measurements. This type of
interference may affect both the precisiQn an accuracy of measurement of
F , conductivity, pH, alkalinity, C0, HCOI, P0, phenols, and Cl
The precision obtained for many of the water quality parameters using
the same method in different laboratories was poor and generally outside of
quoted precisions.” 12 ...Thi . is true=of COD, phenol, inorganic and organic
C, conductivity, NH 3 , S0, S , and SO 3 . The poor precision is probably due
to differneces in pretreatment selected by the individual laboratories to
mitigate suspect interferences, and to the presence of color, oil, and
grease, all of which interfere with colorimetric and electrode methods.
The determination of HCO 3 , C0, NH 3 , NH, S, H 2 S, and other species
may depend on equilibrium calcuations. The ionic strengths of Omega-9 and
of similar waters, however, is so high (I 0.5) that the usual assumption
196
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Table 7. LABORATORY AND TECHNIQUE PERFORMANCE EXPRESSED AS A NORMALIZED
AVERAGE AND COEFFICIENT OF VARIATION
Elemental Analysesa
X-ray Fluorescence (A)
X-ray Fluorescence (D)
Neutron Activation Analysis (A)
Neutron Activation Analysis (B)
Neutron Activation Analysis (C)
Neutron Activation Analysis (D)
Spark Source Mass Spectrometry (E)
Optical Emission (F)
D.C. Plasma Emission (D)
Atomic Absorption Spectroscopy
Other
Number of
Elements
Included in
Normalized
Average (N)
14
9
16
7
10
12
23
12
11
13
12
Normalized
Average
(X ± hi)
0.96 ± 0.21
1.02± 0.31
0.97 ± 0.22
1.09 ± 0.35
0.94 ± 0.17
0.94 ± 0.20
1.29 ± 0.78
1.14 ± 0.60
0.88 ± 0.25
1.03± 0.17
1.15± 0.80
Coefficient
of
Variation
F 1 100
L
22%
30%
23%
32%
18%
21%
60%
53%
28%
17%
70%
Uncertainty in
Coefficient
of Variation
1 1100
L XJ
2(N-1)
4%
8%
4%
9%
4%
5%
9%
11%
6%
3%
15%
Water Quality Parametersa
aLetters A . N are coded descriptors for laboratories making measurements.
Laboratory A
12
1.00± 0.15
15%
3%
Laboratory C
9
1.05± 0.17
16%
4%
Laboratory F
7
1.05 ± 0.39
37%
11%
Laboratory G
5
0.96 ± 0.05
5.2%
2%
Laboratory H
10
0.90 ± 0.20
22%
5%
Laboratory I
8
1.11 ± 0.44
40%
11%
Laboratory J
10
0.92 ± 0.19
21%
5%
Laboratory N
8
0.94 ± 0.23
24%
6%
197
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of infinite dilution is not valid. Approximations, such as the Debye-Huckel
or Davies, to correct equilibrium constants for ionic strength are invalid
for I > 0.5 (Ref. 35). Laboratory measurements of appropriate equilibrium
constants need to be made so these species can be accurately determined.
Fewer interferences were identified for the instrumental methods (NAA,
XRF, SSMS, AAS, OES, PES) than for the chemical methods of analysis. The
extremely high Na level in the sample limited the sensitivity of NAA mea-
surements where radiochemical separation was not used and interfered with
some AAS, OES, and PES measurements. However, the overall precision of
measurements was poorer than for the chemical methods. A major reason for
this is that the mean concentration of elements determined instrumentally
was 6.3 mg/i; it was 8,200 mg/i for the water quality parameters. Another
factor is the variety of sample preparation methods used. There are few
standard methods for instrumental analysis, except AAS.
A number of the more significant interference problems noted in this
study are summarized and discussed below; other interferences are summarized
in Table 3. The discussion is limited to those constituents that occur at
high levels in Omega-9 or to those with interferences that are understood by
the authors. Additionally, routine chemical methods that appear to be
suitable for analysis of waters like Omega-9 are identified.
Chlorine
A significant analytical problem attends the measurement of Ci in oil
shale process water. The four methods used to measure C1--NAA, XRF,
Hg(N0 3 ) 2 titration, and the Technicon AutoAnalyzer--produced highly variable
results. Although NAA and XRF measure total Cl and the chemical methods
measure Cl , this distinction cannot. account for the large variablility
apparent in Table 4.
The Cl data have a trimodal distribution. The results obtained by NAA
and the single XRF measurement average 824 ± 61 mg/i; by the Technicon
AutoAnalyzer, 4,000 ± 140mg/i; and by the Hg(N0 3 ) 2 titration method, 2,211 ±
1,171 mg/i. The NM and Technicon AutoAnalyzer results are consistent
within each method while the Hg(N0 3 ) 2 results show large dispersion.
The Technicon AutoAnalyzer and the Hg(N0 3 ) 2 method both have interfer-
ence problems that were not considered in running the tests; those problems
are discussed below. Therefore, these results have not been used to compute
the best value for Cl in Table 6. In contrast, there is no known interfer-
ence for Cl measured by NM or XRF methods used in this work. Consequently,
the NM and XRF measurements were used to compute the Ci value shown in
Table 6.
The high values and dispersion obtained with the chemical methods can
be explained by examining the analytical methods in more detail. The
Hg(N0 3 ) 2 method i recommended in Standard Methods 1 ’ and by the EPA’ 2 for
the analysis of Cl in waters. It consists of titrating an acidified sample
198
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with Hg(N0 3 ) 2 using diphenylcarbazone as the endpoint indicator. Tests with
this method in one of the author’s laboratories indicate that there is an
interference problem.
The method is based on the reaction:
2C1 + Hg -÷ HgC1 2 (aq) (1)
However, in the presence of other constituents that react with Hg, the
method gives results that are high.
A number of constituents present in Omega-9 m y for precipitates with
the Hg used for titration. These include SCN , S0 , S 2 0, and some carbox-
ylic acids. During titration, a gelatinous precipitate forms before the
endpoint is reached. Its formation has two effects: first, the endpoint is
postponed, which causes a high result; and second, the muddy precipitate
makes detection of the endpoint difficult. This latter point probably
accounts for the dispersion in the Hg(N0 3 ) 2 titration results. - An addi-
tional minor interference is the simultaneous titration of Br and I
Oxidation with KMnO 4 removes the interference for some waters, yielding
results equivalent to those obtained by instrumental analysis. In the KMriO 4
method developed at the laboratory of one author, the sample is diluted 1:10
with distilled water, acidified to pH < 1 with HNO 3 , heated to boiling,
cooled in a water bath, 5 ml 0.2 N KMnO 4 added, and the sample titrated with
0.141 N Hg(N0 3 ) 2 when Hg reacts with Cl to form HgCl 2 . In the presence of
ferric ion, SCM forms the highly colored ferric thiocyanate in proportion
to the original Cl concentration. The presence of SCN and color interfere
with this method. Additionally, Hg reacts with constituents other than Cl
analogous to the Hg(N0 3 ) 2 titration interference, yielding high results.
Sulfide
Sulfide is measured quantitatively by the methylene blue or iodine
titrimetric methods 11 12 26 and qualitatively by the lead acetate paper,
antimony, or silver foil tests. 1 ’ In this work, the qualitative methods and
the iodine titrimetric methods following a CO 2 purge into An(C 2 H 3 0 2 ) 2 or
CdSO 4 were used. Table 5 indicates that there is considerable disagreement
between these two methods. The titrimetric method yielded an averag..e S
concentration of 146 mg/i-S and the oualitative test indicated that S was
absent.
The presence of reducing agents in oil shale process waters interferes
with the quantitative tests. Nota bie among these are S 2 0 3 and various
organics. The nigh (2,743 mg/i) S 2 0 3 concentration in Ornega-9 would prevent
the formation of the blue oior in the methylene blue method. If the sample
is titrated directly, S 2 O , phenol, and unsaturated fatty acids will react
with I during titration, again yielding high results.
Both Standard Methods” and EP1 methods 12 recommend pretreatment to
eliminate these interferences. Pretreatment consists of precipitating the
199
-------
as ZnS by adding 2 N Zn(C 2 H 3 0 2 ) 2 followed by separation of the precipi-
tate. This pretreatment was not used in this study as the presence of high
levels of reducing agents was not suspected. Therefore, results reported
using the titriq etric method are in error and are not used to compute the
best value for S summarized in Table 6.
The qualitative tests, on the other hand, are relatively free of inter-
ferences. Res lts obtained by laboratory C and subsequently by the authors
suggest that S , if present, occurs at low levels in Omega-9.
It is recommended that pretreatmenj be used if standard analytical
methods are used for the measurement of S in oil shale process waters. The
Zn(C 2 H 3 0 2 ) 2 pretreatment procedure should be evaluated in the laboratory to
determine if it is suitable for oil shale retort waters.
Organic Carbon
The data in Table 5 suggest that. there is an analytical problem asso-
ciated with the measurement of TOC in Omega-9. The reported TOC values
range from 780 to 1,300 mg/i and average 1,003 ± 193 mg/i. These values
were obtained using several commercially available instruments and both
direct methods (inorganic C removed by acidifying and purging) and indirect
methods (computed from independent measurements of total and inorganic C).
There are three principal source; of error in the standard TOC proce-
dure when it is applied to oil shale process waters. These are: (1) the
presence of suspended or emulsified organics and large organic particles
that are not taken up in microsyringes; (2) the formation of precipitates
when the sample is acidified; and (3) the loss of volatiles on purging with
N 2 or on storage. The loss of volatiles and precipitate formation are
eliminated when the indirect method is used.
Heterogeneities due to suspended materials, large organic particles, or
precipitates may be minimized by using large sample size for analysis. If
that is not possible, an effort to homogenize the sample should be made.
laboratory M noted that precipitation formation was alleviated by using
dilute 1M HC1 instead of concentrated HC1 for acidification.
Volatile organic carbon was measured at 250 mg/i by laboratory M.
Those volatiles could be lost during N 2 purging or during storage since the
samples were not maintained under an N 2 blanket. A method to eliminate the
loss of volatiles during the purging has been published 33 and should be
investigated fo— application to oil shale process waters.
Chemical Oxygen Demand
The chemical oxygen demand (COD) of a water is a measure of the oxygen
equivalent of the organic matter that is oxidized by a strong chemical
oxidant. The parameter is conventionally used to assess the performance of
biological treatment processes and to estimate the effect of waste dis-
charges on the oxygen level in receiving waters; in addition, it is some-
200
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times used to regulate the discharge of organic wastes. The COO is measured
in terms of the amount of potassium dichromate (K 2 Cr 2 0 7 ) reduced by a sample
during a 2—hour reflux in a solution of boiling, 50% H 2 S0 4 and in the pres:
ence of a Ag 2 SO 4 catalyst. HgSO 4 catalyst. HgSO 4 is added to complex Cl
and thus prevent its oxidation to Cl ?. Essentially complete (theoretical)
oxidation of many organic compounds is obtained in the presence of the
catalyst. Straight—chain aliphatic compounds, aromatic compounds, and many
N compounds are incompletely oxidized.’ 1 36
The COD data summarized in Table 5 range from 4,154 to 18,000 mg/i, a
range that is significantly outside of the precision of the method reported
in Standard Methods. 11 The fact that in-laboratory precision is good while
between—laboratory precision is poor suggests that the method is very sensi-
tive to some part of the procedure that is not carefully controlled since
all laboratories but one used the same method. The variability may be
related to the fact that neither Standard Methods” nor the EPA methods’ 2
specify an upper limit for the COD concentration. The ASTM COD method, 36
which is procedurally identical to these two methods, specifies an upper
limit of 800 mg/i COD for a 50-mi sample treated with 25 ml of 0.25 N K 2 Cr 2 0 7 .
The maximum COD that can be measured using a 50 ml sample and 2 ifi of 0.25
N K 2 Cr 2 O 7 is 1..000 mg/i (Ref. 37). A sample with a COD greater than 1,000
mg/i, such as Omega-9, would therefore have to be diluted to bring it within
the range for the method. Thus, different dilutions could cause the noted
variability. The high Cl concentratton could also contribute to the varia-
bility if the Hg added to complex Cl were complexed by constituents other
than Ci . Both the Standard Methods and EPA method for COD should be jnodi-
fied to include appropriate statements on the upper limits of the method.
Any inorganic compound that is oxidized by K 2 Cr 2 O 7 in an acid medium
will contribute to the measured COD and give a high v..alue. Ttie principal
known interferences from this source in Omega-9 are S 2 0 3 and S 4 0 6 ( .Refs. 38,
39). For example, the S 2 0 3 is readily oxidized by K 2 Cr 2 O 7 to SO 4 in acid
media as follows:
4 Cr 2 0 7 + 3 S O + 26 H 6 SO 4 + 8 Cr + 13 H 2 0 (2)
Thus, for each milligram of S 2 O present in a sample, 0.285 ml of 0.25 N
K 2 Cr 2 O 7 will be consumed, yielding a high result.. The effect of this on the
measured COD can be theoretically computed using Eq. (2). Since Omega-9 has
an $203 c .pcentration of 2740 mg/l, the theoretical COD due to oxidation of
S 2 O to 504 is 1270 mg/i COD.
The standard COD test” 12 should be modified to correct for the oxida-
tion of inorganic S compounds before the test is applied to oil shale
process waters containing high levels of compounds. Experimental work is
required to develop a method to eliminate this interference. Additionally,
the ability of the recommended quantities of Ag 2 SO 4 and HgSO 4 to respec-
tively, catalyze the oxidation of certain organics and complex Dl , should
be verified experimentally for oil shale process waters.
201
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Solids
Total dissolved and fixed solids were measured with good precision;
however, the significance of those measurements for waters similar to
Omega-9 is questionable.
Total dissolved solids (lOS) as operationally defined in Standard
Methods 1 ’ and by EPA 12 is the residue remaining after a sample has been
filtered and dried at 103°-105°C or at 180°C. This parameter is intended to
be a good indicator of total dissolved salts, which are not significantly
lost on heating. However, this parameter is a poor indicator of the dis-
solved salts in waters similar to Omega-9. This could be a significant
problem if this parameter is used to make regulatory decisions or to design
treatment facilities.
The degree by which the measured lOS differs from the total dissolved
salts present in Omega-9 is indicated by the following. The average mea-
sured lOS for this water is 14,210 mg/i while the calculated total dissolved
salts is 30,300 mg/i. The factor of 2 difference between the measured and
calculated TOS is typical of the results obtained with these waters.
The species C0 , HCO 3 , NH 3 , and NH constitute over 65 weight percent
of the dissolved salts present in Omega-9. On heating at 103°-105°C, these
species are lost from solution through the formation of volatile salts or by
stripping out dissolved gases. Linstedt, Daniel, and Bennett 33 investigated
lyophilization and evaporation of Omega-9 at room temperature, as an altern-
ative to evaporation at 103-105°C or 180°C, and found that neither procedure
gave satisfactory results. Substantial losses of NH 4 HCO 3 occurred even at
freezing temperatures. Therefore, the lOS determination, irrespective of
the drying temperatures, gives a value that is significantly low for oil
shale process waters and is not representative of the dissolved salts
present.
The same considerations apply to total solids. Work needs to be
directed at developing a method to measure both total solids and TOS in
these types of waters that accurately reflects the level of salts present.
This may be approached by determining a temperature at which a significant
fraction of the ammonia and carbonate species is lost without loss of other
components. The TDS could then be measured by running the standard analysis
at thjs elevated temperature and adjusting the value obtained by adding to
it NH 4 , C0, and HCO 3 . Alternatively, the CO 2 and NH 3 lost during the TDS
test could be collected and determined gravimetrically.
Alkalinity, Biocarbonate, Carbonate
Conventionally,” 12 HCO 3 and C0 are determined from alkalinity and pH
measurements. However, that method is not valid for oil shale_process
waters due to tne presence of buffering components other than the CO 3 system
(ammonia, borate, silicate, organic bases) and the high ionic strength of
th water. The presence of these species results in an overestimation of
CO 3 when the Standard Method” 12 is used.
202
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Since all o_f the paj’ticipating laboratories used conventional methods
to determine HCO 3 and CO 3 the measurements reported in Tables 4 and 5 were
not used to determine the best values shown in Table 6. Instead, an altern-
ative method was used to compute those species. This metho 1 is described
below and is recommended for the jnea;urement of HCO 3 and CO 3 in any water
not buffered exclusively by the CO 3 system.
An alternative way to determine HCO 3 and C0 is to measure the total
inorganic C and _pH and t.o use equilibrium expressions to compute the dis-
tribution of HCO 3 and CO 3 . This method is discussed by Stumrn and Morgan 35
and is summarized and applied to Omega-9 water in Table 8. Note that the
equilibrium constants K 1 and K 2 must be adjusted for the ionic strength of
the sample. Alternatively, a back titration may be used in conjunction with
the usual strong acid titration.
The computed value for HCO 3 compares favorably with the average of all
an&ytical determinations in Table 4 (15,940 vs 14,800 mg/i). However, the
CO 3 values are not in agreement (500 vs 1,720 mg/i). This is primarily due
to the variation in measured pH and the presence of ,..buffering components
which are neutralized during titration above the CO 3 equivalence point.
This is confirmed for Omega-9 by equivalence points at 7.5 and 4.3.
Recommended Analytical Methods
Based on the results of this study and the authors experience with
analytical instrumentation, the following instrumental techniques are recom-
mended for the analysis of waters similar to Omega-9.
Instrumental Methods -
XRF: As, Br, Ca, Cu, Fe, Ni, Rb, Se, Sr, Ti, U, V, Zn, Zr, Mo, Cl
NAA: Sb, As, Br, Cl, Co, Mn, Hf, Ce, Ba, Fe, Mo, Ni, Sm, Se, Na,
Sr, U, Zn
AAS: Se, Ca, Fe, Na, Zn, Mg, K
Chemical and Other Methods -
The following chemical methods are recommended for analysis of oil
shale process waters pending further laboratory evaluation.
1. Arsenic: silver diethyldithiocarbamate
2. Chloride: KMnO 4 oxidation/Hg(N0 3 ) 2 titration (this work)
3. Sodium: Technicon AutoAnalyzer
4. Uranium: Fluorimetric 3 ’
5. Fluoride: Specific ion electrode’ 1
6. Sulfate: Gravimetric”
7. Thiocyanate: Colorimetric”
8. Total Sulfur: Gravimetric 3 °
203
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Table 8. COMPUTATION OF HCO 3 AND CO FROM MEASUREMENTS OF INORGANIC C AND pH
Carbonate species distribution
[ HCO ] =
ECO} =
rH 1 K 2 ] -
+ +11
[ K 1 [ H J j
1 [ H+12 [ H ] 1
+ +1
[ K K K 2
CT = dissolved inorganic carbon, mg/Q as C
Ionic strength
I = 1/2 C Z 1 2
Z 1 = ionic charge
C, = molar concentration
Adjustment of equilibrium constants
pK’ = pK — AZ 2 0.31
A O.5
Application to Omega 9
=0.5
pK 1 = 6.22 at 25°C
pK 2 = 9.80at25°C
cx = 0.94
= 0.06
CT = 3340 mg/Q
HCO = 15,940 mg/Q as HCO
C0 = 500 mgIQ as C0
pH = 8.65
204
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9. Inorganic Carbon: Carbon Analyzer 11
10. Alkalinity: Titrimetric” 12
11. HCO 3 /C0: Calculation from inorganic C and pH (Table 8)
These recommendations are based on collaborative results from several
methods or from extensive knowledge of the technique. Emission techniques
and SSMS are not recommended because the data base compiled in this study is
not adequate to assess their general performance. Additionally, the perfor-
mance of these techniques as measured by the normalized average and coeff i-
cient of variation (Table 7) was poor.
Elements other than those listed above may be determined by XRF, NAA,
and AAS. The specific elements measured depend on the design of the instru-
mentation. A good example of this iE XRF. Laboratories using XRF in this
study used energy-dispersive systems and high energy X—rays (except labora-
tory N). Alternatively, a wavelength-dispersive system using low-energy
X-rays could be exployed and another set of elements, including Na, Ca, Fe,
Si, Mg, and Cl, determined.
Based on the work presenteu here, the 11 cr,emicai methods appear
adequate for u e with waters like Omega-9. However, the authors encourage
additional collaborative work on these methods to establish their validity
on a range of oil shale process waters before any major analytical work is
undertaken. The other chemical methods used in this study require modifica-
tion to correct for interferences.
CHEMICAL SIGNIFICANCE OF OMEGA-9 WATER
The composition of this water is influenced by the intrusion of ground-
water into the formation (see Ref. 3 for groundwater composition), process
operating conditions, and oil shale composition. The water-to-oil ratio of
22 obtained during the acquisition of the Omega-9 sample 1 suggests that
approximately 22 parts of groundwater were mixed with 1 part of combustion
water plus dehydration water. The chemistry of this specific water is
dominated by an alkaline pH and the presence of high levels of organic and
inorganic C, N, and S as well as Na and Cl. The high level of organic and
inorganic C, N, and S is typical of oil shale process waters and the high
level of Na and Ci are atypical of these waters and probably originated from
groundwater intrusion.
The TDS, as determined from the sum of the individual ions, is about
30,300 mg/i, which is roughly equal to the TDS of seawa er. The principal
ions, present at levels greater than 1,000 mg/l, are Na , NH 4 , HCO 3 , S 2 0 3 ,
and SO 4 ; they constitute about 95% o the total salts present on a weight
basis. O her constituents present at levels of 10 to 1,000 mg/i are B, Ca,
Mg, K, C0, Cl , F , S40 , and SCN . Constituents present at levels of from
1 to 10 mg/i are Sb, As, Br, Fe, P. Si, and Sr. Other constituents are
present at levels below 1 mg/l.
A charge balance for Omega-9 water is presented in Table 9. This
balance is based on the best values summarized in Table 6. The percent
205
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Table 9. CHARGE BALANCE FOR OMEGA-9 WATER
CATIONS ANIONS
mg/Q meq/ mg/f meq/Q
Calcium 12 0.60 Bicarbonate 15,940 261.27
Carbonate 500 16.66
Magnesium 20 1.65 Chloride 824 23.24
Fluoride 60 3.16
Potassium 46 1.18 Sulfate 1990 41.44
Thiosulfate 2740 48.93
Sodium 4333 188.47 Tetrathionate 280 2.50
Thiocyanate 123 2.12
Ammonium 3470 192.71
TOTAL 384.61 399.32
% Variation X 1 — X 2 100 = 1.9%
Lx i + x 2 j
variation (1.9%) is considerably less than the recommended limit of 3%. The
good agreement of the charge balance lends credibility to the accuracy of
some of the analytical results determined in this study.
SUMMARY
This study has evaluated existing chemical and instrumental methods for
the characterization of an oil shale process water. It demonstrated that
many standard analytical methods cannot be used to accurately measure water
quality parameters in these complex waters. Methods specific to these
waters need to he developed and published. The’following methods were found
to give incorrect results when used on waters like Omega—9: (1) Hg(N0 3 ) 2
titration and Technicon AutoAnalyzer methods for Cl ; (2) titrimetric
methods without pretreatment for S; (3) gravimetric method for solids; and
(4) the permanganate oxidation methQd for COD. Other methods, including
those for CN , phenols, P0 4 , and C0, do not yield reproducible results.
There may be interferences in other methods used in this study but there are
presently inadequate data to assess ..them. Some existing chemical methods
for the measurement of alkalinity, SO 4 , inorganic C ..Na, SCN , As, and total
S, and the methods presented in this work for C0 3 , HCO 3 , and Cl may be
adequate for routine analyses following limited additional laboratory test-
I ng.
The instrumental methods used were found to be free of interferences,
with the exception of the high Na concentration. Since this is not typical
of oil shale process waters, this may not be a problem for other oil shale
process waters. However, instrumental methods are subject to variations due
206
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to differences in sample preparation and the fact that most of these tech-
niques produce precision data for a subset of the total set of elements
reported. Results obtained with SSMS and the emission techniques were poor
compared with those obtained with other instrumental methods. SSMS consist-
ently gave the lowest detection limit but had the poorest precision of all
instrumental methods evaluated. XRF, NAA, and AAS produced precise and
accurate results.
ACKNOWLEDGEMENTS
Appreciation is extended to all laboratory personnel who participated
in this study. The participating laboratories were: General Activation
Analysis, Inc. , San Diego, California; the United States Geological Survey,
Denver, Colorado; Coors Spectro-Chemical Laboratory, Golden, Colorado;
University of Colorado’s Civil, Environmental, and Architectural Engineering
Department, Boulder, Colorado; Wyoming Department of Agriculture, Laramie,
Wyoming; Accu-labs Research, Inc., Wheatridge, Colorado; Battelle Pacific
Northwest Laboratory, Richiand, Washington; Geolabs, Golden, Colorado;
Laramie Energy Technology Center, Laramie, Wyoming; the Lawrence Berkeley
Laboratory, Berkeley, California; Amoco Research Center, Naperville,
Illinois; Huffman Laboratories, Inc. . Wheatridge, Colorado; and Dohrmann-
Envirotech, Santa Clara, California. Appreciation is also extended to Jon
S. Fruchter of Battelle Pacific Northwest Laboratory, and to Robert D.
Giauque and Frank S. Asaro of the Lawrence Berkeley Laboratory, who made
extensive analyses to establish the homogeneity of the sample, developed
advanced instrumental methods specific to Omega-9, and provided critical
comments during the formative stage of the manuscript. This work was funded
by the Division of Fossil Fuel Extraction of the Department of Energy.
REFERENCES
1. Farrier, 0.5., J.E. Virgona, I.E. Phillips, and R.E. Paulson,
“Environmental Research for In Situ Oil Shale Processing,” 11th Oil
Shale Symp. Proc., Cob. School of Mines, 1978.
2. Poulson, R.E., J.W. Smith, N.B. Young, W.A. Robb, and T.J. Spedding,
“Minor Elements in Oil Shale and Oil Shale Products,” LERC Rept. of
Invest. 77-1, 1977.
3. Jackson, L.P., R.E. Paulson, T.J. Spedding, T.E. Phillips, and I-LB.
Jensen, “Characteristics and Possible Roles of Various Waters Signifi-
cant to In Situ Oil Shale Processing,” Quart. Color. School of Mines,
70:105, 1975.
4. Wildeman, T.R., and R.H. Meglen, “The Analysis of Oil Shale Materials
for Element Balance Studies,” Environmental Trace Substances Research
Program of Colorado, Univ. of Cob. , March 1978.
5. Fox, J.P., “The Partitioning of Major, Minor, and Trace Elements During
Simulated In Situ Oil Shale Retorting,” Ph.D. Thesis, Univ. of Calif.,
Berkeley, 1979.
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6. Fox, J.P., R.D. Mclaughlin, J.R. Thomas, and R.E. Poulson, “The Parti-
tioning of As, Cd, Cu, Hg, Pb and Zn During Simulated In Situ Oil Shale
Retorting,” 10th Oil Shale Symp. Proc., Cola. School of Mines, 1977, p.
223.
7. Fox, J.P., D.S. Farrier, and R.E. Poulson, “Chemical Characterization
and Analytical Considerations for an ifl Situ Oil Shale Pv ocess Water,”
LETC/RI-78/7, Nov. 1978.
8. Long, A., Jr., N.W. Merriam and C.J. Mones, “Evaluation of Rock Springs
Site 9 In Situ Oil Shale Retorting Experiment,” 10th Oil Shale Symp.
Proc., Cob. School of Mines, 1977, p. 120.
9. Farrier, D.S., R.E. Poulson, Q.D. Skinner, J.C. Adams, and J.P. Bower,
“Acquisition, Processing and Storage for Environmental Research of
Aqueous Effluents from In Situ Oil Shale Processing,” Proc. of the 2nd
Pacific Chem. Eng. Cong., Denver, Cola., Vol. II, 1977, p. 1031.
TO. Felix, W..D., D.S. Farrier, and R.E. Paulson, “High Performance Liqu
Chromatographic Characterization of Oil Shale Retort Waters,” Proc. of
the 2nd Pacific Chem. Eng. Cong., Denver, Cob., Vol. I, 1977, p. 480.
11. “Standard Methods for the Examination of WAter and Wastewater,” 14th
ed., Am, Pub. Health Assoc.., 1976.
12. “Methods for Chemical Analysis of Water and Wastes,” EPA-625/6-74-003,
U.S. EPA, Office of Technology Transfer, Washington, D.C., 1974.
13. Subramanian, K.S., C.L. Chakrabarti, J.E. Sueiras, and 1.5. Maines,
“Preservation of Some Trace Metals in Samples of Natural Waters,” Env.
Sci. Tech. 50: 444, 1978.
14. USGS, Book 5, “Methods for Collection and Analysis of Water Samples for
Dissolved Minerals and Gases,” In: Techniques of Water Resources
Investigation of the U.S. Geological Survey , U.S. Govt. Printing
Office, Washington, D.C., 1970.
15. Analytical Methods for Atomic Absorption Spectroscopy , Perkin Elmer,
1973.
16. Analytical Methods for Flame Spectroscopy , Varian, Assoc.
17. Lansford, Myra, Emma M. McPherson, and Marvin J. Fishman, “Determina-
tion of Selenium in Water,” Atomic Abs. Newsletter, 13:103, 1974.
18. Fernandey, F.J., “Determination of Gaseous Hydrides Utilizing Sodium
Borohydri 1e Reduction,” Atomic Abs. Newsletter, 12:93, 1973.
19. Analytical Methods for Atomic Absorption Spectroscopy , Perkin Elmer,
1976.
208
-------
20. Inter-Bureau Report Methods for Use in Oil Shale and Shale Oil,
OSRD-32, 1945.
21. Jeris, J.S., “A Rapid COD Test,” Water and Wastes Eng. 4:89, 1967.
22. Wells, W.N. , “Evaluation of the Jeris Rapid COD Test,” Water and Sewage
Works, 4:123, 1970.
23. Fischer, R.D. , “Quantitative Chemical Analysis,” W.B. Saunders Co.,
1961, p. 278-281.
24. Instruction Manual, Ammonia Electrode Model 95-10. Orian Research,
Inc. , 380 Putnam Anvenue, Cambridge, MS, 1974.
25. ASTM Standards, Part 23, Water; Atmospheric Analysis.
26. Standard Methods for the Examination of Water and Wastewater , 12th ed.
1965.
27. Nor, Y.M. and M.A. Tabatabai, Soil Sci. 171:122, 1976.
28. Kelly, O.P., L.A. Chambers, and P.A. Trudinger, “Cyanolysis and
Spectrophotometric Estimation of Trithionate in Mixture with Thiosul-
fate and Tetrathionate,” Anal. Chem. 41:898, 1969.
29. Official Methods of Analysis of the Association of Official Analytical
Chemists, AOAC, 11:31, 1970.
30. Standard Methods for the Examination of Water and Wastewater , 10th ed.,
1955.
31. Gentanni, F.A., A.M. Ross, and M.A. BeSessa, Anal. Chem. 28:1651, 1956.
32. Dixon, W.J. “Processing Data for Outliers,” Biometrics, 9:74, 1953.
33. Van Hall, C.E., 0. Barth, and V.A. Stenger, “Elimination of Carbonates
from Aqueous Solutions Prior to Organic Carbon Determinations,” Anal.
Chem. 37:7159, 1965.
34. Ondov, J.M. , W.H. Zoller, I. Olmex and others, “Elemental Concentra-
tions in the National Bureau of Standards Environmental Coal and Fly
Ash Standard Reference Materials,” Anal. Chem. 47:1102, 1975.
35. Stumm, W. and J.J. Morgan, q atic Chemistry , New York, Wiley-
Interscience, 1970.
36. 1978 Annual Book of ASTM Standards , Part 31, “Water,” Am. Soc. for
Testing and Matl., Philadelphia, PA.
37. Cripps, James M. and David Jenkins, “A COD Method Suitable for the
Analysis of Highly Saline Waters,” Jour. WPCF, 36:1240, 1964.
209
-------
38. Linstedt, K. Daniel and Edwin R. Bennett, “Report on Characterization
and Treatment of Retort Waters from In Situ Oil Retorting,” Quart.
Report to LETC, June 10, 1977.
39. Stuber, H.A.., J.A. Leenheer, and D.S. Farrier, “Inorganic Sulfur
Species in Wastewaters from In Situ Oil Shale Processing,” J. Environ.
Sci. Health, A13(9):663-675, 1978.
210
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ROLE OF ORGANIC COMPOUNDS IN THE
MOBILITY OF TRACE METALS
M. Caolo, R.R. Meglen,
R.E. Sievers, and J.S. Stanley
Environmental Trace Substances Research Program
University of Colorado
Boulder, Colorado 80309
ABSTRACT
Preliminary studies of the possible mobilization of trace metals in
retorted shale by organic compounds have involved a class fractipnation of
crude sha’e oil followed by gas chromatographic and trace metal analysis of
the fractions. A reasonably good column chromatographic separation was
achieved with neutral alumina packing by successive elutions with solvents
of increasing polarity. The results from the trace metal analyses experi-
ments indicated a tendency toward association of the metal ion with specific
fractions from the separation, in particular the nitrogenous bases.
INTRODUCTION
The scale of shale oil recovery operations anticipated for the State of
Colorado has raised many questions regarding the environmental impact on
water quality, since large quantities of retorted shale and retort waste-
water will be produced by the various operations of shale oil recovery.’
Of special importance are the nitrogen- and sulfur-containing compounds
which may promote the mobilization of toxic trace elements in retorted shale
(such as cadmium, molybdenum, arsenic and selenium) through complexation or
chelation. Such metallo-organic complexes are sometimes more toxic than the
simple inorganic species and frequently exhibit a “solubilization” effect. 2
This effect would increase the mobility of the trace element and introduce
it in an altered form in the biosphere, thereby becoming available for
uptake by plants and animals.
The present study has focused on the development of a method for the
separation of the bulk organics from the nitrogen- and sulfur-containing
compounds found in shale oil. Trace metal analyses by atomic absorption
spectrophotometry of the various fractions obtained from this separation
have been conducted to determine whether selected metal ions tend to be
associated with specific classes of compounds.
211
-------
(‘AII( I (TI’II(
Studies on the fractionation of shale oil have shown that a fairly good
class separation is achieved by absorbtion of the crude oil onto neutral
alumina followed by successive elutions with solvents of increasing polari-
ty. The results of trace metal analyses for lead, zinc, arsenic, cadmium,
and selenium, on the column fractions, suggest specific metal association
with classes of organic compounds, notably the nitrogenous bases.
Mutagenicity studies performed at Oak Ridge National Laboratory on
shale oil samples have found the highest mutagenic activity to be exhibited
by the nitrogen-containing fractions.
In view of the toxicity of three metals (cadmium, arsenic and selenium)
found to be associated with the nitrogen-containing fractions of the shale
oil and the mutagenic activity also associated with these fractions, the
study of the speciation of metallo-organic complexes is of great importance
and much more work is required to fully assess the potential danger of
groundwater contamination.
RECOMMENDATIONS
The study of the speciation of metal ions and the identification of
metallo-organic complexes in water samples from shale oil recovery opera-
tions will provide information essential for minimizing adverse effects on
the quality of groundwater in the area. The results of this study indicate
that there is a tendency of metal ions to associate with specific chemical
classes of compounds in the crude oil. It is now necessary to determine
whether these associations are present in the water samples and to identify
any metallo-organic complexes. In addition, more information is needed
related to leaching and transport prccesses in the retorted shale. Studies
in these areas have been initiated with the recent acquisition of several
water samples.
MATERIALS AND METHODS
SAMPLE INVESTIGATED
This study was conducted on samples of Paraho shale oil.* These sam-
ples have been stored longer than desirable and the data obtained should be
interpreted bearing in mind the age of the samples.
This oil samp1e is not to be considered representative of the Paraho
process. It was used in these preliminary studies to develop methods which
will be used in the analysis of water samples.
*(Collected by Dr. Thomas Wildeman; Uay 2; Period 0000-0800; Sample 50-10;
Split #2; N 2 - F; 8/17/77.)
212
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ANALYTICAL METHODS
Gas Chromatographic Analysis
The detection of individual components of each fraction was accomplish-
ed by gas chromatography employing a flame ionization detector (FID) and a
nitrogen-phosphorous detector (NPD). The gas chromatographic columns used
in this study were high resolution glass capillary columns. The columns
were .20 m long and coated dynamically with 10% OV-1O1 in methylene chlo-
ride. FID analyses were conducted on a Hewlett-Packard HP5830A, while an
HP5730A was used for the NPD analyses. Both instruments are equipped with
Hewlett-Packard capillary inlet systems. The operating conditions for the
HP5830A equipped with an FID were: column temperature held at 70°C for 4
minutes, then increased 4°/mm to 2300 for 16 minutes; nitrogen carrier gas
.l mi/mm at 7 psig inlet pressure; hydrogen, 30 psig; air, 30 psig; injec-
tion port temperature, 250°C; detector temperature 300°C. Analyses with
nitrogen-selective detector were performed on a Hewlett-Packard HP 5730A GC
apparatus. Temperature programs and carrier gas flow rate were identical to
those in experiments with the HP5830A. The air flow rate was 50 mi/mm.
The alkali bead heater voltage was held at approximately 17 V.
Atomic Absorption Analysis
Trace metal analyses were carried out with a Perkin-Elmer 5000 Atomic
Absorption Spectrophotometer equipped with a graphite furnace, Perkin-Elmer
HGA 2100, and a Perkin-Elmer Auto-Sampling System AS-i. Conditions under
which the metal analyses were conducted are shown in Table 1.
TABLE 1. OPERATING CONDITIONS FOR ATOMIC ABSORPTION GRAPHITE FURNACE
Wave
Split
Sample
Temperature
Program
OC*
Length
Width
Size
Metal Lamp (nm)
(nm)
(ul)
Dry
Char
Atomize
As ED* 193.6 0.7 20 110 850 2500
Cd HC*k 228.8 0.7 10 110 400 1500
Pb HC 283.3 0.7 10 110 550 2000
Se ED 196.0 0.7 20 110 850 2500
Zn HC 213.9 0.7 10 110 500 2000
*Electrodeiess Discharge Lamp; 1% Ni(N0 3 ) 2 was used as a fixative for
both As and Se analysis.
**Hollow Cathode Lamp
***Typical times for temperature programs are: dry (40 sec), char (30 sec),
and atomize (7 sec).
In all cases, a ramp mode was used between maximum dry temperature and
maximum char temperature.
213
-------
I flr sr çs ,.Sr
cAr tuI1ca’ IP%L ri uuu cc.
ADSORPTION OF SHALE OIL ONTO NEUTRAL ALUMINA
A chromatographic separation procedure developed for the fractionation
of coal-derived solids and liquids 3 has been applied to the fractionation of
the shale oil. An aliquot (3.2 g) of Paraho shale oil was dissolved in 5 ml
of methylene chloride. Neutral alumina (activity I, ICN). 12 g, was then
added to the solution while stirring with a glass rod to give a material the
consistency of wet sand. The flask containing the mixture was subsequently
put on the rotary vacuum evaporator until all the solvent had been removed
and the alumina flowed smoothly. The alumina with adsorbed sample was then
added to a 1.8 cm OD x 340 cm chromatography column containing 92 g of
neutral alumina, activity I. The column was elated to give the fractions
shown in Table 2. Solvents were removed from the fractions by rotary evapo-
ration until tne volume was reduced to a few milliliters. A sample from
each fraction was saved for GC analysis. The remaining solvent in each
fraction was allowed to evaporate. Table 2 gives the weights of the col-
lected fractions.
GAS CHROMATOGRAPHIC ANALYSIS OF SHALE OIL FRACTIONS
The fractions obtained from the alumina column fractionation of the
shale oil sample have been analyzed by gas chromatography and the chromato-
grams are shown in Figures 2 through 8. Figure 1 indicates the relative FID
and NPD responses for a series of normal hydrocarbons. While the FID shows
good sensitivity to hydrocarbons, the NPD exhibits essentially no response.
TRACE METAL ANALYSIS OF SHALE OIL FRACTIONS
Analyses of lead, zinc, arsenic, cadmium, and selenium, in each of the
fractions obtained from the alumina column separation of the oil, were
performed using the atomic absorption-graphite furnace method. Initially,
the residue from each of the fractions was dissolved in one, two or three
milliliter aliquots of p-xylene. Direct injection of the solution sample
into the graphite furnace, however, did not give reproducible elemental
analytical results. It was necessary to take the samples to dryness again
and perform a nitric acid-peroxide digestion on the residue before analysis
of the aqueous digest by the atomic adsorption graphite furnace method. A
minimum volume of fuming nitric acid was added to each of the fraction
residues and allowed to react until a white precipitate formed. To ensure
the complete oxidation of the material, 30% hydrogen peroxide was added.
Each sample was then diluted to 25 ml with distilled water and the resulting
solution analyzed. The results from these experiments are shown in Table 3.
These values include the correction for the reagent blank containing HNO 3
and H 2 0 2 .
RESULTS AND DISCUSSION
Attention has focused principally on the nitrogen-and sulfur-containing
compounds, since they represent the greatest potential for involvement in
the mobilizatio i of toxic inorganics by the formation of stable metal
214
-------
f\5
h-1
cn
111
CO
z
o
Q.
CO
UJ
tr
(T
o
I-
o
UJ
H
Ld
Q
HYDROCARBON STANDARD
NPD
FID
RETENTION TIME(MIN)
60
Figure 1. FID and NPD chromatograms of a standard hydrocarbon
solution.
-------
TABLE 2. ALUMINA COLUMN FRACTIONATION OF SHALE OIL*
Fraction Number
Eluant
Weight
(g)
1
100 ml hexanes
0.6916
2
275 ml toluene
0.3075
3
275 ml chloroform (to the point
before a large dark band began
to be eluted from the column)
0.0399
4
350 ml chloroform (elution of
of dark band)
1.3765
5
275 ml chloroform (volume suffi-
cient to remove any remaining
dark color)
0.1188
6
200 ml tetrahydrofuran/ethanol
(9:1)
0.3295
7
250 ml methanol
TOTAL
was introduced into the alumina column.
0. 1132
2. 9770
g
*3.2 g of
shale oil
complexes. Since water samples were not readily available, initial investi-
gations were conducted on samples of shale oil. Adsorption of the crude oil
onto alumina followed by successive elutions with solvents of increasing
polarity resulted in 7 well-resolved fractions as shown in Table 2. Indi-
vidual column fractions were analyzed by GC/FID and GCINPD. The FID chrom-
atograms of the hexane and toluene fractions (Figures 2 and 3) show that a
large proportion of the hydrocarbons and light aromatics are being eluted
from the column. In the chloroform fractions, the presence of significant
amounts of nitrogen-containing compounds is shown by the NPD chromatograms
(Figures 4, 5, and 6). The FID response for thse fractions, however, is
negligible, giving no indication of appreciable amounts of hydrocarbons.
The THF/ETOH fraction shows both NPD and FID responses (Figure 7). The
methanol fraction should contain compounds of high polarity; it produced
relatively little FID and NPD response (Figure 8).
These results indicate that the separation achieved with the sample of
shale oil is similar to that obtained with coal-derived solids and liquids
on neutral alumina and appears to separate the hydrocarbons, aromatics,
nitrogen and polar compounds. Analysis of these fractions for metallo-
organic complexes by GC/MS/DS is currently being undertaken.
216
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TABLE 3. ELEMENTAL CONCENTRATIONS OF A SHALE SAMPLE BEING ELUTED FROM
AN ALUMINA COLUMN BY SOLVENTS WITH INCREASING POLARITY
Fraction
Cd
Pb
ug/g
As
Zn
Se
1.
Hexane
0.11±0.01
O.2
0.1
O.7
O.2
2.
Toluene
0.18±0.02
3.2±0.4
1.6
58.0±2.4
(74.9%)
0.4
3.
CHC1 3 (three
fractions corn-
bined)
0.13±0.01
(52.2%)
3.5±0.4
1.5
(37.1%)
2.3±0.1
O.3
(46.4%)
4.
THF/ETOH
0.09±0.02
4.6±0.2
(68.2%)
2.7±1.5
7.5±0.2
0.5±0.4
5.
MeOH
0. 15±0.06
6.0±1.1
18. 0±4.5
(32.3%)
9.5±0.7
1. 5±1.1
N)
I— .
*Numbers in parentheses refer to the percentage of an element in the principal fraction
containing that element.
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CO
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CD
z
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CL
tO
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oc
tr
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H
u
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LU
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A
0
HEXANE FRACTION
NPD
FID
u
RETENTION TIME (M1N)
60
Figure 2. FID and NPD chromatograms of hexane fraction from alumina
column separation of shale oil.
-------
LU
to
z
o
a.
CO
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o:
cr
o
H
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h-
TOLUENE
FRACTION
NPD
EiD
0
RETENTION TIME (MIN)
60
Figure 3. FID and NPD chromatograms of toluene fraction from
alumina column separation of shale oil.
-------
yj
CO
I st
CHCI3
FRACTION
NPD
o:
cr
I !
o
LU
FID
o
0
RETENTION TIME(MIN)
60
Figure 4. FID and NPD chromatograms of 1st chloroform
fraction from alumina column separation of
shale oil.
220
-------
ro
1X5
Ill
CO
o
CL
CO
Lul
o:
(T
o
H
o
LJ
I-
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O
2nd CHCI3 FRACTION
NPD
FID
RETENTION TIME (MIN)
60
Fiqure 5 FID and NPD chromatograms of 2nd cholorform fraction
from alumina column separation of shale oil.
-------
ro
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CO
o
CL
CO
111
IT
O
H
O
LU
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O
3rd
CHCK
FRACTION
NPD
^vA/«A^/Uv-A^
0
RETENTION TIME(MIN)
60
Figure 6. FID and NPD chromatograms of 3rd chloroform fraction
from alumina column separation of shale oil.
-------
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CO
2
o
CL
-------
MEOH FRACTION
yj
CO
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cr
a:
o
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LlJ
tiAJ
Q
,
J
<\
..>/
/
/A._ - ' "
1
u
'^\ NPD
/ "---x
•' '•""' ' /
-..
\
v~_
FID
i \
0
RETENTION TIME
-------
Since a r2ascnable class separation of the crude oil was achieved, it
was considered important to determine the concentration of selected metal
ions in each fraction. This should indicate whether certain metals had a
tendency to be associated with specific classes of compounds. Trace metal
analyses of the fractions were performed using the atomic absorption-
graphite furnace method. Three of the elements (arsenic, cadmium and sele-
nium) were chosen because of their toxicity, while the remaining two (lead
and zinc) were chosen because their chemistries are representative of those
of other elements present in the retorted shale. The results from these
experiments (shown in Table 3) indicat.e that there is a tendency for associ-
ation of the metal ion with specific fractions obtained from the separation.
Most of the cadmium and selenium present in the oil sample is concentrated
in the chioroform-eluted fractions, which also contain the majority of
nitrogen-containing compounds. Most of the zinc is in the toluene-eluted
fraction which is expected to contain the light aromatics, while most of the
lead is in the THF/ETOH fraction. Arsenic appears to be distributed between
the chloroform and methanol fractions. These data suggest specific metal
associations with classes of organic compounds, in particular the nitrogen-
ous bases. These bases are assumed to possess the greatest ability to form
metal complexes.
In the course of these preliminary experiments separation and analysis
techniques have been developed and refined. As samples of water from shale
oil processing activities become available, we plan to extend these studies
to learn more about possible mobilization of toxic elements by complexing
agents, particularly nitrogenous bases likely to be present.
ACKNOWI. EDGEMENT
We are indebted to Dr. D.W. Denriey for his continued interest in this
project and his helpful advice and assistance.
REFERENCES
1. J. Schmidt-Collerus, “Disposal and Environmental Effects of
Carbonaceous Solid Wastes from Commercial Operations,” 1st Annual
Report, Denver Research Institute, University of Denver, January, 1974.
2. A. Cantillo and D. Segar, “Metal Species Identification in the
Environment: A Major Challenge for the Analyst,” In: Proceedings of
the International Conference on Heavy Metals in the Environment,
Toronto, Canada, October, 1975, pp. 183-204.
3. J.E. Schiller and D.R. Mathiason, “Separation Method for Coal-Derived
Solids and Heavy Liquids,” Anal. Chem. Vol. 49 No. 8, July, 1977, p.
1225.
225
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RETORT WATER PARTICIJLATES
J.P. Fox
Lawrence Berkeley Laboratory
Energy and Environment Division
Berkeley, California 94720
Retort water may contain three types of suspended matter: oils and
tars, raw and spent shale particles and a finely divided residue generally
believed to be bacterial cells.’ These particulates result in an extremely
heterogeneous sample and complicated chemical analyses. Physical and chemi-
cal interactions between the retort water and these particulates, including
mineral dissolution (from the spent shale particles), adsorption on the
bacterial cells and oil-water solubility reactions can alter the composition
of the dissolved fraction during sample storage.
The heterogeneity of unfiltered waters complicates chemical analysis.
The coefficient of variation for replicate analyses may range from 20% to
over 100% for many waters for analytical techniques that typically yield 10%
precision. Therefore, a series of these waters was filtered to determine if
this would produce a homogeneous sample that was not significantly altered
in chemical composition from the original sample. Filtration of the first
water produced a high density of crystals, about 50 to 100 pm in length, on
the surface of the filter paper. Preliminary analyses of the particulate
and dissolved fraction of the water suggested that a significant fraction of
the dissolved constituents in the retort water could be removed by crystal
formation. This startling result led to a more detailed investigation of
the nature and origin of retort water particulates. This investigation and
its results are discussed here.
EXPERIMENTAL
Eleven retort waters from Laramie Energy Technology Center’s
controlled-state retort were filtered, and the particulate fraction and the
filtered water collected and analyzed for 17 elements by energy-dispersive
x-ray fluorescence spectrometry (XRF). Retort operating conditions for
these 11 waters are summarized in Table 1.
A 47 mm Millipore glass vacuum system with a fritted glass support
screen and Millipore type HA 045 pm filter paper were used to collect
particulates because they produced a uniform deposit of suspended material
with a minimum of paper wrinkling. Tared filters were washed to remove
readily soluble copper, iron, nickel, and zinc by filtering 100 ml of 0.06 M
NH 4 HCO 3 followed by 250 ml distilled water prior to filtration of the
sample. The glass frit was moistened with distilled water before placing
226
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TABLE 1. RETORT OPERATING CONDITIONS FOR LETC’S CONTROLLED-STATE RETORT
Shale
Shale
Isothermal
Gas Flow
Grade
Size
Oil Ylled, %
Advance
Maximum
Rate,
Shale
Run Type
Runa
Type
(liters!
tonne)
range
(mm)
Fischer Assay
(volume basis)
Rate
(rn/day)
Temp.
(°C)
Sweep
Gas
Standard
m 3 /m 2 mm.
CS-60 Colorado 1 123 3-13 46 1.83 540 100% N 2 0.12
CS-62 Utah C 126 3-13 95 1.83 540 100% N 2 0.12
CS-63 Antrim C 40 3-13 77 1.83 540 100% N 2 0.12
CS-64 Colorado C 248 3-13 94 1.83 540 100% N 2 0.12
CS-65 Moroccan C 79 3-13 88 1.83 540 100% N 2 0.12
CS-66 Colorado C 128 3-13 91 1.83 540 75% N 2 0.15
25% steam
CS-67 Colorado C 231 3-13 100 1.83 540 75% N 2 0.15
25% steam
CS-68 Colorado C 119 3-13 97 1.83 540 100% N 2 0.12
CS-69 Colorado C 118 3-13 98 1.83 760 64.5% N 2 0.15
25% steam
10.5% 02
CS-70 Colorado C 134 3-13 96 1.83 540 75% N 2 0.15
25% steam
CS-71 Utah C 137 3-13 91 1.83 540 75% N 2 0.15
25% steam
a C = Completed ‘ut t; I Ii terrupLed rut .
-------
the filter paper on it to prevent wrinkling during filtration. All glass-
ware and sample bottles were washed with soap and water, rinsed with distil-
led water, soaked for a minimum of 12 hours in 5 N HC1, again rinsed with
distilled water, dried in an 80°C oven for 4 hours and brought to room
temperature before use. In addition, the glass support screen, on removal
from the acid bath, was further washed by filtering 200 ml of 5 N HC1 fol-
lowed by 200 ml of distilled water.
The samples were removed from a 4°C refrigerator 24 hours before analy-
sis to bring them to room temperature and shaken for 30 sec immediately
before filtration. If excessive outgassing or foaming occurred, the samples
were allowed to stabilize before withdrawing a sample. A sample volume
sufficient to give a particulate unit mass of about 1 mg/cm 2 (5 to 25 ml)
was transferred to a filtration funnel with a Pyrex pipette and filtered by
vacuum at a rate of about 1 mi/sec. The filtrate was transferred to a
polyethylene container and the filter paper containing the particulates was
placed in a plastic petri dish in a dessicator under silica gel. The filter
papers were weighed daily until a constant weight within 2% was obtained
(this typically took 2 days). Two replicates of each sample were prepared
in this way. The filtrate from one replicate was stored at 4°C. The f ii-
trate from the other replicate was left at room temperature for 15 to 17
days and refiltered to investigate the effect of bacterial growth on soluble
metal content. Four blanks were carried through the entire procedure.
The abundance of 17 elements was measured by energy-dispersive x-ray
fluorescence spectrometry. The instrumental method has been previously
described. 2 The filter paper containing the particulates was cut into 2.5
cm discs and counted for 20 or 40 mm. Filtered retort waters were prepared
by pipetting seven 4 p1 drops of sample onto a 0.006 mm polypropylene film
tightly stretched in a plastic ring. Drop location was controlled with a
jig designed to produce seven concentric spots. These deposits were air
dried and the samples counted for 2000 sec. Chromium was determined by
neutron activation analysis, 3 and mercury was determined by Zeeman atomic
absorption spectroscopy. 4 X-ray diffraction was used to identify mineral
phases. Crystals were collected with tweezers under an optical microscope
and adhered to a glass rod with silicon grease. A powder pattern was taken
using copper Kor radiation with a nickel filter in the beam. The morphology
and chemical composition of individual particles were studied using a scan-
ning electron microscope (Advanced Metals Research Model 1000 A) equipped
with an energy-dispersive XRF analyzer (EDAX).
RESULTS AND DISCUSSION
Particulate Composition
The elemental composition of the particulates and the filtered waters
and the percent of the total elemental mass associated with the particulates
are summarized in Table 2. This summary shows that the major elements (>0.1
mg/i) in the particulate fraction are iron, nickel, potassium, and calcium.
All other measured constituents typically occur at less than 50 pg/i. The
228
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Table 2. X-RAY FLUORESCENCE .ANALYSES OF PARTICULATES AND FILTERED RETORT WATERS FROM THE
CONTROLLED—STATE RETORT (rng/2 .) (cont.)
Filtered Particulate
2
Particulate
Filtered Particulate
2
Particulate
!i...nt
Ti
V
Cr
Mn
F.
‘Ii
Ga
A.
S.
8r
Rb
Sr
y
Hg
Pb
K
Ca
Sn lide
CS—SI CS—68 CS—69
0.46 6 0.38
<0.42
0.011 6 0.00 5k
<0.21
1.07 ± 0.14
1.26 1 0.08
<0.06
3.50 ± 0.18
1.25 ± 0.06
0.38 ± 0.04
0.29 ± 0.06
0.10 1 0.08
<0.15
0,090 ± 0.009
<0.24
4.46 1 2.66
5.16 1 0.94
Filtered Pirticulat. Particulate Z1. .ent
2
<0.010
<0.0001
0.005 1 0.003
<0.004
0.140 ± 0.006
0.023 6 0.002
<0.001
0.0105 ± 0.0008
0.0090 ± 0.0008
<0.001
<0.002
<0.002
0.0023 1 0.0018
0.0099 ± 0.0016
0.0028 1 0.0026
0.038 1 0.029
0.075 1 0.011
241 ± 30
N)
to
<2
31
<0.10
12
2
0.3
<0.3
<1
<2
5
1
1
0.62 ± 0.40
0.19 ± 0.07
0.050 6 O.OO?
0.22 I 0.14
3.22 * 0.20
3.61 1 0.18
0.01 1 0.04
16.9 ± 0.8
0.71 ± 0.04
0.16 1 0.06
0.60 0.06
0.21 ± 0.08
<0.15
0.134 ± 0.014
<0.24
63.4 1 3.0
20.0 ± 6.6
0.008 1 0.007
0.011 ± 0.005
0.004 ± 0.003
<0.004
0.118 ± 0.008
0.122 ± 0.006
<0.001
0.054 ± 0.003
0.0034 0.0006
<0.002
0.0020 ± 0.0012
0.0017 ± 0.0014
<0.003
0.021 1 0.002
0.007 ± 0.003
0.20 ± 0.03
0.16 0.02
219 ± 78
1 0.61 1 0.40
8 0.35 1 0.28
1 0.061 1 0.006
<2 <0.21
5 4.35 ± 0.56
3 1.52 * 0.31
<1 0.06 1 0.04
0.3 7.32 0.93
0.5 0.60 0.06
<1 <0.09
0.3 0.50 ± 0.06
1 0.21 0.08
— <0.15
11 0.024 ± 0.013
— <0.24
0.5 36.7 ± 1.0
18.8 ± 2.7
Cs- b
0.013 1 0.010
0.018 * 0.007
0.011 1 0.005
0.013 1 0.004
0.157 1 0.007
0.54 ± 0.03
‘0.002
0.033 ± 0.002
0.074 ± 0.004
‘0.002
0.0020 ± 0.0016
0.0035 ± 0.0022
• ‘0.004
0.019 4 0.004
0.0075 ± 0.0042
0.078 ± 0.042
0.28 ± 0.03
375 ± 1
2 ‘ Ii
S V
15 Cr
- Mn
3 Fe
26 Ni
<3 Ga
0.5 U
12 S.
— k
0.4 Rb
2 Sr
— Y
•11 Hg
— Pb
0.2 K
1 Ca
e , .I •A
CS_li
Ti
0.81 ± 0.40
<0.03
<4
‘0.57
<0 014
—
V
0.40 ± 0.28
0.018 ± 0.013
5
0.32 ± 0.26
0.008 ± 0.007
2
Cr
0.055±0.006
0.015 ± 0.009
21
0.043 ± 0.004
<0.007
<14
Mn
<0.21
<0.010
<0.04
<0.21
0.0076 0.0036
—
Fe
4.36 0.22
0.135 ± 0.008
3
3.29 ± 0.16
0.23 ± 0.01
7
Ni
1.52 0.08
0.50 1 0.03
25
1.94 ± 0.10
0.021 ± 0.003
1
Ga
0.05 1 0.04
<0.003
<6
0.05 ± 0.04
<0.002
<4
U
7.48 ± 0.37
0.059 ±0.023
1
4.57 0.23
0.025 ± 0.003
1
S.
0.57 ± 0.04
0.101 ± 0.006
15
0.33 ± 0.04
0.0011 ± 0.0010
1
Br
<0.09
<0.004
—
0.14 ± 0.04
0.002
<1
Rb
0.50 ± 0.06
0.0069 ± 0.0031
1
0.17 ± 0.06
0.002
<3
Sr
0.21 0.08
0.0077 ± 0.0040
4
0.21 ± 0.08
0.0083 ± 0.0022
4
Y
(0.15
0.0068 • 0.0048
—
<0.15
<0.006
—
14g
0.025 0.007
0.013 0.005
74
0.048 0.002
0.0019±0.0003
4
Pb
<0.24
0.01! ± 0.008
—
<0.21
<0.006
—
K
36.1 ± 2.9
0.24 1 0.09
1
18.7 ± 2.7
0.077 1 0.043
0.4
Ca
23.8 ± 1.2
0.36 ± 0.05
1
14.0 1.0
0.93 0.10
6
So Iid8
65! 1 32
21)3 46
SoIlde
a Neutron activation nna1y .is
-------
Table 2. X-RAY FLUORESCENCE ANALYSES OF PARTICULATES AND FILTERED RETORT WATERS FROM THE
CONTROLLED-STATE RETORT (mg/9 ..)
2 2 2
I1..sst Fi1e.r.d Pirciculat. Particulat. Pilt.rad Particuists Particulate Pilt.r.d Particulata Particul.t. Elasent
03—60 03—62 C$—63
Ti ‘0.60 <0.053 — <0.60 <0.056 — 0.0 2 0.40 0.030 6 0.011 4 Ti
V ‘0.42 ‘0.037 — <0.42 <0.040 - 0.0 ± 0.26 <.040 V
Cr 1.74 2 0.22 0.15 * 0.02 8 0.51 6 0.011 0.43 6 0.02 43 0.09 * 0.011 0.056 * 0.006 39 Cr
I S a 0.23 * 0.16 0.03 2 0.01 14 <0.21 0.065 6 0.016 - 0.31 2 0.14 0.022 ± 0.005 7 M u
P. 1 .2*0.9 4.32*0.21 1 5 6.60* 0.33 8.73* 1.35 57 1.30*0.14 1.43*0.01 52 V.
Mi 2.06*0.10 3.32±0.16 62 1.34 0.08 9.32*0.14 87 0.98*0.08 0.03160.003 3 I ii
C i 0.05*0.04 0.006 <11 0.06*0.04 <0.006 ‘9 0.06±0.04 (0.003 <3 C s
8* 6.04 6 0.30 0.12 6 0.01 2 6.22 ± 0.31 0.130 6 0,006 2 1.82 ± 0.09 0.016 ± 0.002 1 As
5. 0.37 2 0.04 0.032 6 0.004 5 0.47 ± 0.04 0.043 ± 0.004 8 0.51 ± 0.04 0.004 * 0.001 1 S.
Sr 0.10 2 0.06 <0.006 <6 0.15 ± 0.06 < 0.001 <4 0.58 * 0.04 <0.002 <0.3 Br
Sb 0.28 *0.06 <0.009 <3 0.15 ± 0.06 <0.009 ‘6 1.21 ± 0.06 0.004 ± 0.002 0.3 Rb
Sr 0.41 ± 0.08 0.023 2 0.005 <6 (0.12 <0.012 — 0.20 ± 0.08 0.168 ± 0.008 46 Sr
V <0.15 <0.014 — ‘0.15 <0.014 — <0.15 0.003 ± 0.003 — V
liz (0.001 0.029 ± 0.008 .100 <0.00% 0.051 ± 0.009 ‘ .100 0.025 ± 0.006 0.055 ±0.003 69 Hg
Pb <0.24 0.02 * 0.01 — <0.24 0.023 0.015 — <0.24 0.022 ± 0.004 — Pb
16.8± 2.6 <0.24 <1 <4.11 <0.23 — 165±8 0.76 0.06 0.5 I I
C. 13.1 2 1.0 13.4 ± 0.6 51 5.75 ± 0.98 0.43 ± 0.09 7 5.97 1.2 5.48 2 0.42 59 Ca
SolId. 2190 160 2964 2 123 341 ± 41 Solids
CS—64 CS—os CS—66
Vi <0.57 <0.01 — <0,60 0.068 2 0.013 — <0.60 0.017 ± 0.012 — Ti
V <0.39 0.008 0.006 — <0.42 0.020 ± 0.009 — <0.42 0.022 ± 0.009 V
Cr 0.026 2 0.005 1 (0.007 <20 0.24 ± 0.20 0.072 ± 0.007 23 0.038 ± 0.006’ <0.009 <19 Cr
ISi 0.20 t 0.14 <0.005 <2 0.22 * 0.14 0.014 * 0.005 6 0.27 0.14 (0.007 <3 Mn
F. 1.91 6 0.14 0.105 1 0.005 5 0.42 2 0,12 1.60 ± 0.07 79 1.59 ± 0.25 0.39 ± 0.02 20 F.
M i 2.29 * 0.11 0.036 1 0.002 2 2.51 2 0.13 0.094 ± 0.005 4 2.74 ± 1.15 0.29 ± 0.01 10 Ni
Ci 0.05 ± 0.04 <0.002 <4 0.07 * 0.04 <0.002 ‘3 0.05 ± 0.04 <0.002 (4 Ca
As 3.66 0.18 0.023 2 0.001 1 2.47 t 0.12 0.016 ± 0.00% 1 14.1 2 2.3 0.055 2 0.003 0.4 As
S. 0.35 0.04 0.003 0.001 1 5.79 * 0.29 0.045 2 0.002 1 0.49 t 0.08 0.007 1 0.001 1 Si
Br 0.53 1 0.06 <0.002 <0.4 0.61 1 0.06 <0.002 <0.3 0.07 1 0.06 ‘0.002 <3 Br
Rb 0.28 * 0.06 <0.002 <0.7 0.34 2 0.06 0.0047 2 0.0020 1 0.66 1 0.09 0.004 ± 0.002 1 Rb
Sr <0.12 <0.003 — 0.12 2 0.05 0.032 2 0,003 21 0.13 ± 0.08 0.007 ± 0.003 2 Sr
‘6 0.12 0.10 <0.004 <3 <0.15 <0.005 - <0.15 0.005 — ‘6
Hg 0.181 1 0.005 0.015 ± 0.002 8 0.253 ± 0.025 0.067 ± 0.004 21 0.127 0.014 0.033 ± 0.003 21 Hg
Pb <0.24 0.006 ± 0.004 — 0.19 2 0.15 0.006 ± 0.005 3 <0.24 0.007 t 0.005 — Pb
V. <4.0 ‘0.06 — 60.3 ± 3.1 0.33 ± 0.05 1 53.6 ± 5.1 0.23 2 0.05 0.4 K
C I 4.33 0.92 ‘0.03 ‘1 6.2 1.1 3.19 0.15 34 22.9 1.3 0.57 ‘ 0.04 2 C.
S ti i. 248 33 I SO) 3 )7 17 So1Id
Neutron activation .nalyets
-------
Table 3. X-RAY FLUORESCENCE ANALYSES OF PARTICULATES FROM FIRST AND SECOND
FILTRATION OF WATERS CS—66, CS—68 AND CS—69 (ng/tnl)
CS—66 CS—68 CS—69
First Second First Second Firet Second
Element Filtration Filtration 8 Filtration Filtration 8 Filtration Filtration 8
As 54.6 ± 2.7 80.2 ± 4.0 54.1 ± 2.7 84 ± 4 33.1 ± 1.7 81.6 ± 4.0
Br <2.4 <3.3 <1.5 <2.4 <2.4 <4.5
Ca 573 ± 35 334 ± 34 156 ± 17 <43 282 ± 26 2990 ± 150
Cr <8.7 9.9 ± 6.0 4.1 ± 3.2 <8.4 10.9 ± 4.8 14.6 ± 11.6
Cu 5.2 ± 3.0 29.1 ± 4.0 41.4 ± 2.1 230 ± 11 63.4 ± 3.4 <13.5
Fe 390 ± 18 202 ± 10 178 ± 8 168 ± 8 157 ± 7 83.5 ± 43
Ga <2.1 <2.1 <1.2 <2.1 <1.8 <3.9
Hg 33.2 ± 3.4 142 ± 7 20.9 ± 1.8 11.2 ± 2.8 78.6 ± 3.9 14.3 ± 6.0
K 228 ± 54 374 ± 55 201 ± 30 276 ± 52 78.3 ± 42.2 291. ± 106
Mn <6.9 <6.9 <3.6 <6.6 12.8 ± 4.0 12.7 ± 9.0
Ni 294 ± 14 977 ± 48 122 ± 6 195 ± 9 542 ± 27 225 ± 11
Pb 6.8 ± 4.8 <7.5 6.7 ± 2.6 <7.5 7.5 ± 4.2 <14.7
Rb 4.0 ± 2.0 4.1 ± 2.0 2.0 ± 1.2 2.5 ± 2.0 2.0 ± 1.6 6.1 ± 4.0
Se 6.5 ± 1.2 32.7 ± 1.6 3.4 ± 0.6 5.3 ± 1.2 73.7 ± 3.7 19.7 ± 2.6
Sr 6.5 ± 2.6 6.8 ± 2.6 1.7 ± 1.4 <3.9 3.5 ± 2.2 29.0 ± 5.4
Ti 17 ± 12 <18 7.6 ± 6.6 <18 13.3 ± 9.8 <35.1
V 22 ± 9 <13 17.0 ± 4.8 9.6 ± 8.6 18.0 ± 7.2 <25,2
Y <6.8 <4.8 <2.7 (4.5 <3.9 <9.3
Zn 68.6 ± 3.4 13.0 ± 2.2 114 ± 5 133 ± 6 156 ± 7 9.6 ± 4,8
Total Solids 337 ± 17 257 ± 16 219 ± 78 153 375 ± 1 228 ± 1
aThe second filtration was performed on the filtrate from the first filtration after it had been maintained
at room temperature for 15 to 17 days.
-------
N)
Table 4. X-RAY FLUORESCENCE ANALYSES OF THE TOP AND BOTTOM 1 mm OF LIQUID IN WATERS CS-64,
CS-65 AND CS-67 AFTER STORAGE FOR 1 YEAR AT ROOM TEMPERATURE (ng/ml)
CS—64
CS—65
CS—67
Element
TOP
B YFTOM
TOP
BOTTOM
TOP
BOTTOM
As
4.6 ±
0.2
5.5 ± 0.2
3.1 ±
0.2
4.9 ± 0.2
5.4 ±
0.2
4.3 ± 0.2
Br
0.4 ±
0.1
06 ± 0.1
0.6 ±
0.1
1.2 ± 02
0.5 ±
0.1
0.5 ± 0.1
Fe
1.1 ±
0.4
2.5 ± 0.4
<0.5
0.7 ± 0.4
1.0 ±
0.4
1.6 ± 0.4
Ge
<0,2
0.3 ± 0.1
<0.2
0.4 ± 0.1
<0.2
0.3 ± 0.].
Hg
0,072.!
0.011
0.75 ± 0.014
0.059 ±
0.005
0.78 ± 0.08
0.065 ±
0.008
0.51 ± 0.10
Ni
2.2 ±
0.2
4.9 ± 0.3
2.0 ±
0.2
5.5 ± 0.3
1.3 ±
0.2
3.2 ± 0.3
Rb
0.3 ±
0.2
0.3 ± 0.2
0.4 ±
0.2
0.6 ± 02
0.3 ±
0.2
0.4 ± 0.2
Se
0.4 ±
0.1
0.5 ± 0.1
4.5 ±
0.2
12.3 ± 0.4
2.0 ±
0.2
1.8 ± 0.1
Sr
<0.3
<0.3
0.3 ±
0.2
0.3 ± 0.2
0.2 ±
0.2
‘ 0.3
-------
fraction of the total elemental mass present in the particulates (% particu-
late) is typically less than, or about, 1% for potassium, arsenic, selenium,
bromine, and rubidium. The percent particulate is significantly greater
than 1% for iron, chromium, mercury, and nickel in most samples.
Three of the filtered samples (CS-66, -68 and -69) exhibited remarkable
visual changes during storage at room temperature. All three samples became
turbid and a finely divided deposit collected at the bottom of each contain-
er. Similar, but less marked behavior was noted in all filtrates left at
room temperature. No visual changes occurred in the samples stored at <4°C.
These three samples were filtered after 15 to 17 days of storage at room
temperature and the particulates analyzed. The elemental composition of
particulates collected from these three waters during the first and second
filtration are compared in Table 3. This table shows that there is a sig-
nificant concentration of solids and of all of the elements in the particu-
late fraction from the second filtration. The ratio of the solids from the
second to those from the first filtration is 069 ± 0.08, i.e., 69% of the
mass collected during the first filtration was again collected during the
second. This could only occur if the first filtration was not successful jr
removing all the particulates that can be captured by a 0.45 im filter (not
likely) or if significant bacterial activity occurred in the sample. The
visual appearance of the samples (sediment at the bottom of the container)
plus the work of Farrier 1 support the conclusion that the high solids level
obtained on the second filtration is largely due to bacterial growth.
Microscopic examination of the sediment from one of the waters revealed
rod-shaped structures similar to those reported by Earner. 1
The effect of this sediment on the concentration of nine elements was
examined by sampling the top and bottom 1 mm of three waters that had been
stored under ambient conditions for about 1 year. The results of these
determinations, shown in Table 4, indicate that there is a concentration
gradient between the top and bottom of the sample container for mercury,
nickel, arsenic, iron, germanium, bromine, and selenium. The majority of
the mercury and varying amount of the other elements is at the bottom of the
container in the sediment. This suggests that the bacterial cells remove
these elements from solution. The high percent particulate values for
mercury and nickel in Table 2 support this.
The uniformly high level of most of the elements measured in the
particulates from the second filtration (Table 3) cannot be entirely
explained by removal by bacteria. As will be seen in discussion to follow,
precipitation during during filtration is an important factor.
Particle Morphol 2
The morphology and chemical composition of individual particles present
in the particulate fraction of each water are presented in Figures 1-12.
This series of figures presents scanning electron micrographs of particu-
lates from each water and x-ray spectra of individual particles shown in the
micrographs. Since only a small area is represented by each micrograph, it
should not be assumed that the types of particles present are limited to
those shown.
233
-------
A visual classification of the particles reveals that there are two
types present: crystals and amorphous solids. These particles are imbedded
in a uniform background of spongy or scaly material. The only element
detected in the matte material is sulfur (carbon and nitrogen are likely to
be present but cannot be detected by EDAX). The amorphous particles are
rounded (see Figures 4 and 5) and their chemical composition is silicon-
aluminum (calcium, potassium, iron, sodium). The crystalline particles are
varied in shape and are composed of iron, calcium, magnesium or nickel. The
particles range in size from a micron or less to about 100 pm.
The rounded amorphous particles are hypothesized to be spent shale
particles. This is supported by their composition and their similarity to
individual particles of spent shale and is consistent with the mineral
composition of spent shale particles. 5
The crystalline particles are highly varied in both form and chemical
composition. Three rather striking crystal types were obtained. Filtration
of water CS-62 (from an inert gas run using Utah shale) produce a high
density of cubic crystals (3pm sides) of iron and nickel (see Figure 2).
The associated anion is unknown. The small size of the crystals prevented
their identification by x-ray diffraction techniques. The unique formation
of nickel-iron crystals during filtration of water CS-62 is consistent with
the chemical composition of unfiltered water. This water contains 10.7 mg/l
of nickel, 15.3 mg/I of iron and 2.8% sulfur. These are the highest values
of these three elements found in any of the 11 waters.
Filtration of water CS-63 (from an inert gas run using Antrim shale)
produced a high density of long needle-like crystals in a radial array with
a diameter of about 7 pm (Figure 3). The simultaneous presence of high
levels of strontium, magnesium, potassium and sulfur distinguish this water
from others in the set studied. EDAX analyses indicate that the only cation
present ‘is calcium. X-ray diffraction on individual crystals identified the
mineral phase as aragonite. Solubility calculations support the x-ray
diffraction identification. Water CS-63 is supersaturated with respect to
both aragonite and calcite. The crystallization of aragonite is favored by
the presence of small amounts of barium, strontium, magnesium or lead salts
and CaSO 4 , by rapid precipitation and by relatively high concentrations of
reactants. 6 All of these conditions are met for water CS-63.
Water CS-69 (from a steam combustion run using Colorado shale) produced
a high density of prismoidal crystals (30 pm side) in which the predominant
cation is magnesium (Figure 9). These larger crystals coexist with clusters
of microcrystals of magnesium and sulfur. The larger crystals are probably
magnesium carbonate and the microcryst.als are probably gypsum (CaSO 4 2H 2 0).
Solubiuity calculations indicate water CS-69 is supersaturated with respect
to magnesite (MgCO 3 ), nesquehonite (MgCO .3H 2 O) and hydromagnesite (Mg 4
(C0 3 ) 3 (0H 2 ) 31120). Other work 6 indicates that precipitation at ordinary
temperature and pressure gives either nesquehonite or a basic carbonate such
as hydromagnesite. Thus, the larger crystals are likely one of these forms
of magnesium carbonate. X-ray diffraction on individual crystals failed to
iaentify the mineral phase. The crystals apparently decomposed between the
234
-------
initial filtration and the x-ray diffraction work (“ . . one year). This was
verified by re-examining the deposits by scanning electron microscope. The
crystals present in CS-69 had been replaced by deposits similar to those
shown in the micrographs in Figures 1 and 10 suggesting that both of these
deposits may have contained crystalline material at one time.
A number of other particles with a predominance of a single cation,
either calcium, magnesium, iron, aluminum or silicon, was also identified.
The density of these other particles was lower and their structure was not
readily discernible from the data at hand. Examples of these other
particles include: (1) concave particles with dark centers in which iron is
the predominant cation (particle 2A in Figure 3 and particle 20 in Figure
7), (2) rounded amorphous particles in which silicon, likely as Si0 2 , is the
predominant cation (all particles in Figure 5, and particle 1A in Figure 6),
and (3) obscured particles in which aluminum is the predominant cation
(particle 3A in Figure 10 and particle 1C in Figure 9).
Other particles were observed in which no element, except sulfur, was
founded (indicating a composition of elements lighter than aluminum). The
sulfur, in all cases, is attributed to the background matte and not the
particle. Examples of these crystals are seen in Figure 2 (particles 1A,
2A, 1C-4C) and Figure 6 (particles 4A, 5A). Based on the composition of
retort waters, these particles may be such compounds as N14 4 HC0 3 , NH 4 (C0 3 ) 2
or salts of organic acids, such as (NH 4 ) 2 C 2 0 4 H 2 0.
Particle Formation Mechanisms
Four mechanisms are adequate to explain the origin and composition of
retort water particulates. These four mechanisms are: (1) oil and spent
shale particle suspension during retorting, (2) evaporation of an equivalent
1 mm deep layer of retort water from the filter surface, (3) crystal forma-
tion during filtration due to CO 2 outgassing and (4) bacterial removal.
These mechanisms explain the following major observations:
1. The particulate fraction consists of a uniform fibrous matte
in which individual crystalline or amorphous particles are
embedded.
2. The concentration of 19 elements and solids in particulates
collected during two successive filtratioris of the same water
are similar.
3. The elements calcium, magnesium, iron, silicon, aluminum,
potassium, sodium, nickel, barium, and chromium are localized
in individual particles and are the major elements in the
particulates. The elements arsenic, selenium, rubidium,
strontium, mercury, gallium, lead, yttrium, titanium, and
manganese are uniformly distributed in the matte material and
occur at low levels, typically less than 10 pg/l.
235
-------
4. One percent or less of the total mass of potassium, arsenic,
selenium, bromine, and rubidium and considerably more than 1%
of the iron, nickel, mercury, and chromium present in the
unfiltered water occur in the particulate fraction.
5. A significant concentration gradient may exist between the
top and bottom of an unrefrigerated sample for the elemetit.s
mercury, nickel, germanium, arsenic, bromine, iron, and
seleni urn.
6. The elemental composition and morphology of the amorphous
particles are similar to spent shale. Crystalline particles
are typically composed of either calcium, magnesium or iron.
Suspension of Spent Shale Fines and Oil--
Oil shale becomes friable during retorting due to the removal of kero-
gen from the mineral matrix. Bed settling and errosion by hot combustion
gases may release spent shale fines which are entrained in the gases and
either settle out or are entrapped during the condensation of oil and water
vapors. Spent shale fines are composed of akermanite, diopside, calcite,
albite, analcime and other minerals; the principal elements are silicon,
aluminum, calcium, iron, magnesium, and sodium. 5 The morphology and compo-
sition of these fines are very similar to the silicon-aluminum-(calcium,
magnesium, iron, sodium) particles that are present in most of the waters.
The round shape of these particles, suggesting heat treatment, also supports
the theory that they are spent shale fines.
Oil and water condense out of the gas phase and move down the packed
bed as an emulsion. After separation of these phases, a small amount of oil
remains in the water phase. This oily material is removed during filtration
of the sample and collects as a spongy fibrous matte on the filter paper.
This is supported by its visual appearance, texture and odor and by the
presence of a strong sulfur peak in the x-ray spectrum of the backgrounds of
most of the samples. Calculations indicate that this oil does not signifi-
cantly contribute to the measured elemental abundances in retort water
particulates (<1%).
Surface Evaporation- -
Since the organic matte (oil and bacterial cells) and filter paper are
hygroscopic, some of the filtered water is retained following filtration.
When this retained water is evaporated, the dissolved ions present in it are
deposited on the filter paper. If it is assumed that the equivalent mois-
ture film thickness is 1 mm, then about 0.14 ml of water is retained on the
filter paper for a deposit with a diameter of 42 mm. If 25 ml of sample are
filtered, then 0.55% of the total elemental mass in the unfiltered sample
will be deposited approximately uniformly. This is within an order of
magnitude of the amount of potassium, arsenic, selenium, bromine, rubidium
and titanium found in all of the particulates for which 25 ml were filtered.
Five ml of waters (CS-60 and CS-62 and 10 ml of water CS-69 were filtered.
236
-------
Thus, about 1.5% and 3% of the elemental mass in the unfiltered sample
should be deposited for the 10 ml and 5 ml samples, respectively. This is
consistent within an order of magnitude (see Table 2) with the particulate
data and the elemental abundance data for potassium, arsenic, selenium,
bromine, and rubidium.
Surface evaporation of a 1 mm layer is also supported by the fact that
the particulate composition in two successive filtrations is similar and by
the fact that the elements occurring at low levels in the particulates
(i.e., about the right order of magnitude to have been deposited by evapo-
ration of a 1 mm layer) are not localized in the particulates with the
exception of potassium (occurs in spent shale fines).
Precipitation- -
The crystals noted in some particulates probably form during filtra-
tion. If these crystals were formed during or immediately subsequent to
retorting, they would likely redissolve as the solubility of carbonates
decreases at elevated temperatures.
Most retort waters are supersaturated with respect to a number of
mineral phases such as calcite, aragonite, magnesite and siderite. However,
the high concentration of organics in these waters may increase the solubil-
ity relative to that predicted for infinitely dilute solutions. During
filtration, CO 2 is stripped out of solutian. This drives the reaction to
the left and metal carbonates (MeCO 3 ) may precipitate.
MeCO 3 (s) + C0 2 (g) + H 2 0 Me 2 + 2HC0 3
This is dramatically supported by the presence of calcium and magnesium
carbonates in the particulates. It is also supported by the high percent
particulates for iron, nickel, calcium, magnesium and chromium in Table 2
and by the localization of these elements in particles. The very high
percent particulates and high elemental masses for iron, nickel and calcium
relative to other elements can only be explained by the precipitation of
carbonates of these elements during filtration. About half or more of the
iron, nickel and calcium were removed, presumably as crystals, during the
filtration of waters CS-60, -62 and -63. Surface evaporation, presence in
the oil fraction or bacterial removal cannot explain the high values.
Crystals were not observed in water CS-60, presumably due to crystal decom-
position prior to analysis as was verified for water CS-69.
Bacterial Removal
Bacterial cells that accumulate at the bottom of a sample container
stored at >4°C may remove a significant fraction of the dissolved mercury,
nickel and selenium and lesser amounts (<5%) of arsenic, bromine and iron.
High concentrations of rod-shaped bacteria have been identified in the
sediment that accumulates in retort waters stored at room temperature.’
These bacteria have a surface charge and provide a high specific surface
area which enhances adsorption. They may also remove elements by biological
237
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uptake. Table 4 indicates that large amounts of mercury and nickel are
associated with the sediment material in all three samples studied and that
lesser amounts of arsenic, bromine, iron, germanium and selenium are asso-
ciated with the sediment of one or more of the samples. The most dramatic
example of this behavior occurs for mercury. The samples in Table 2 with a
high percent particulate loading for mercury (CS-60, -62, -63, -69, and -70)
also have elevated percent particulate values for chromium, selenium and
nickel relative to samples with low percent mercury particulate values.
These elevated values cannot be explained by any of the previously discussed
mechanisms and are likely due to association with the sediment material.
SUMMARY
Particulates were collected from 11 retort waters and their chemical
composition and morphology studied using x-ray fluorescence spectrometry,
x-ray diffraction and scanning electron microscopy. This work indicates
that the particulate fraction of retort water consists of oils and tars,
spent shale fines and bacterial cells. Crystals and finely dispersed salts
may form during or after vacuum filtration and contribute to the particulate
fraction. The crystal phase aragonite was positively identified in one
sample. These particulates originate from the suspension of spent shale
fines and the formation of an oil-water emulsion during retorting, from the
evaporation of an equivalent 1-mm-deep layer of retort water from the filter
surface, from CO 2 outgassing during filtration and from bacterial growth in
samples maintained at >4°C.
The elements calcium, magnesium, iron, silicon, aluminum, potassium,
sodium, nickel, barium, and chromium may be localized in individual
particles and are major elements in the particulates. About one percent of
the total potassium, arsenic, selenium, bromine, and rubidium in retort
water is present in the particulate fraction and significantly greater than
one percent of the iron, chromium, mercury and nickel. The elements
arsenic, selenium, rubidium, strontium, mercury, gallium, lead, yttrium,
titanium, and manganese are uniformly distributed in the matte material and
occur at low levels. The elements mercury, nickel, germanium, arsenic,
bromine, iron, and selenium appear to be removed by the bacterial cells.
ACKNOLWEDGEMENTS
Appreciation is extended to Robert Giauque and Lilly Goda of Lawrence
Berkeley Laboratory for the x-ray fluorescence measurements, to Helena
Reuben of Lawrence Berkeley Laboratory for the x-ray diffraction analyses,
to Lucy Pacas of the Lawrence Berkeley Laboratory for the Zeeman atomic
absorption spectroscopy measurements, and to Robert Heft of Lawrence
Livermore Laboratory for the neutron activation analyses. This work was
supported by the Division of Fossil Fuel Extraction of the U.S. Department
of Energy under Contract No. W-7405-ENG-48.
238
-------
Figure 2.
(A)
110
Location 1A-3A
>..-
U)
C- -
a,
C- -
1 23 4 5
X-ray energy (key)
Scanning electron micrograph of (A) particulates from water CS—60
and diagrams of x—ray energy at locations 1A—3A. XBB 788—10560
(A)
(B)
>‘
U)
C
a)
C
> -
C
C
Location 1A2A
X-ray energy keVj
0
Location 3A-6A
and 1B-48
X-ray energy ikeV)
r 4
- i X-ray energy (keV)
Scanning electron micrograph of (A) particulates from water CS—62;
(B) particulates similar to those at locations 4A—6A in (A); (C)
particulates from water CS—62; and diagrams of x—ray energy at
locations 1A, 2A; 3A—6A and 1B—4B; and 1C—4C.
XBB 788—10557, 58 and 59
239
Figure 1.
-------
X•ray energy (keV)
Location 4A
2 4 6
X-ray energy (key)
Figure 3.
(A)
110
(B)
urn
0
X-ray energy (key)
Scanning electron micrograph of (A) particulates from water CS—63;
(B) detail of 1 cation 1A; and diagrams of x—ray energy at
locations IA ; A; 3A; 4A; 5A; and 6A. XBB 788—10567 and 69
Location 2A
X-ray energy (key)
X-ray energy (keV)
4
X-ray energy (key)
Location 6A
S
C
S
C
240
-------
>‘
C
C
Location SA
Figure 4.
(A)
J5
>‘
U,
C
-C
0 2 4 6
X-ray energy (keV)
8
- Location 8A
X-ray energy (key)
0 2
Scanning electron micrograph of (A) particulates from water CS—64;
and diagrams of x—ray energy at locations 1A—4A; 5A; location
resembling 1A—7A; 6A and 7A and 8A. XBB 788—10568
241
Location 1A-4A
S
1
K
X-ray energy (keV)
0 2
6
X-ray energy (key)
X-ray enPrg (keV)
-------
f\ (ic
24 6 8
X-ray energy (key)
Location 4A
(A)
j5
C) 2 4
X-ray energy (keV)
Figure 5.
Scanning electron micrograph of (A) particulates from water CS—65;
(B) detail of locations 1A,2A; and diagrams of x-ray energy at
locations IA, 3A; 2A; 4A; 5A; and 6A XBB 788—10572 and 74
(B)
1Mm1 L
Location 1A.3A -
C
Location 2A
I-
C
w
C
X-ray energy (key)
0
2
X-ray energy e.
4 6
>.
Wi
C
C
X-ray energy (keV)
- I I
- Location 6A
6 8
242
-------
Location 3A
X-ray energy (key)
6 8
Scanning electron micrograph of (A) particulates from water CS—66;
(B) detail of background in vicinity of location 1A; and diagrams
of x-ray energy at locations 1A; 2A; 3A; and 4A, 5A.
XBB 788—10578 and 80
243
(B)
2 1 m
Location A
C l ,
C
C
0
5
Location 2A
X-ra energy (keV
-1
1
X-ray energy (key)
b
>- --
w
Figure 6.
h
Location 4A,5A
0
2 4
X-ray energy keV
6
-------
2 4 6
X-ray energy (key)
I I
Location 10
S
X-ray energy (key)
(B) (C)
>‘
U)
C
a)
C
- Location 20
X-ray energy (keV)
Scanning electron micrograph of (A) particulates from water CS—67; (B) detail of 1A at
center field; (C) detail of 2A at top centers; (D) detail of upper left field; and
diagrams of x—ray energy at locations 1A; 2A, 3A; 4A; 1D; and 2D.
(A)
(0)
I
Location 1A
>‘
C l )
C
a,
C
I i
>‘
U,
C
a,
C
4
X-ray energy (key)
X-ray energy (key)
Figure 7.
XBB 788—10579, 76, 77, and 75
-------
(A) (B)
25pm
110 pm
0 j sI I
>‘
U)
C
C
2 4 6
X-ray energy (key)
—
Location 4A
I
0
0 2 4 2 4 6
X-ray energy (keV) X-ray energy (key)
Figure 8. Scanning electron micrograph of (A) particulates from water CS—68;
(B) detail of 1A; and diagrams of x—ray energy at locations 1A;
2A; 3A; 4A; 5A XBB 788—10571 and 70
245
Location 1A
(I )
C
C
S
Location 2A
C
C
>‘
U)
C
C
Location 3A
6 8
4
X-ray energy (keV)
-------
(A)
t’ A
1 150
Location 2B
-Mg
X-ray energy (keV)
Figure 9.
>‘
( I ,
C
C
f -
1
- Locata
;J
on 38
(8) VV V (C)
— -1 -
Location 1A
>.
C
C
N.)
Location lB
X-ray energy (keV)
— 4 I>
ray energy (k V
V -V oc tion 1
0
A
> . -
C.. -
8 4 0 2 4 6
X-ray energy (key) X-ray energy (keV) X ray energy (key)
Scanning electron micrograph of (A) particulates from water CS—69; (B) more particu—
lates from water CS—69; (C) particulates from water CS—69 one year aft’r micrographs (A)
and (B); and diagrams of x—ray energy at locations 1A; 2A; 1B, 2B; 3B; and 1C.
XBB 788—10565, 66 and XBB 795—6531
-------
Location 1A
X-ray energ’ 7 (keV)
•1
Location 3A
X-ray energy (keV)
(A)
4 ri
Figure 10. Scanning electron micrograph of (A) particulates from water CS—70;
(B) more particulateS from water cs—70; and diagrams of x—ray
energy at locations lÀ; 2A; 3A; and lB. XBB 788—10563 and 62
247
(B)
i mI
0
> .
C l )
C
Q.)
C
4
8
X-ray energy (keV)
>
U,
C
a)
C
X-ray energy (key)
-------
Figure 11. Scanning electron micrograph of (A) particulates
right field of (A); and diagrams of x—ray energy
5B; and 6B.
(B)
X-ray energy (kiV
8
Location 6B
0 2 4 6 8
X-ray energy (keV)
from water CS—71; (B) detail of upper
at locations 1A; 2A; 1B, 2B; 3B; 4B;
XBB 788—10555 and 56
(A)
-Y -
Location 1A
cc
X-ray energy (key)
X-ray energy (key)
Location 3B
( ,
X-ray energy (k&V
2
4
X-ray energy (keV)
>
U)
C
a,
C
-------
Location 1A
Fe
L 8
X-ray energy (key)
T
—1
2 4 6 8
X-ray energy e .
Figure 12. Scanning electron micrograph of (A) particulates from refiltered
water CS—66F; and diagrams of x—ray energy at locations 1A; and
2A. XBB 788—10564
249
(A)
C
C
Location 2A
> -
-------
REFERENCES
1. Farrier, D.S., R.E. Poulson, Q.D. Skinner, J.C. Adams, and J.P. Bower.
Acquisition, Processing and Storage for Environmental Research of
Aqueous Effluents from In Situ Oil Shale Processing. Proceedings of
the Second Pacific Chemical Engineering Congress, Denver, CO, 11:1031,
1
S
2. Giauque, R.D., B. Garrett, and L.Y. Goda. Energy-Dispersive X-ray
Fluorescence Spectrometry for Determination of Twenty-Six Elements in
Geochemical Specimens. Anal. Chem. 49:62, 1977.
3. Heft, R.E. Absolute Instrumental Neutron Activation Analysis at
Lawrence Livermore Laboratory. UCRL-80476, December 1977.
4. Hadeishi, 1. and R.D. McLaughlin. Isotope Zeeman Atomic Absorption, a
New Approach to Chemical Analysis. Am. Lab., August 1975.
5. Campbell, J.H. The Kinetics of Decomposition of Colorado Oil Shale:
II, Carbonate Minerals. UCRL-52089, Part 2, March 1978.
6. Palache, C., H. Berman, and C. Frondel. The System of Mineralogy,
Volume II. Halides, Nitrates, Borates, Carbonates, Sulfates,
Phosphates, Arsenates, Tungstates, Molybdates. New York, John Wiley
and Sons, 1951.
250
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ANALYSIS OF PARAHO OIL SHALE PRODUCTS AND EFFLUENTS:
AN EXAMPLE OF THE MULTITECHNIQUE APPROACH
J.S. Fruchter, C.L. Wilkerson, J.C. Evans and R.W. Sanders
Pacific Northwest Laboratory
Operated for the U.S. Department of Energy
by Battelle Memorial Institute
INTRODUCTION
The source characterization studies detailed in this paper, which were
sponsored by the U.S. Department of Energy, are intended to provide detailed
and accurate information on the types and amounts of various substances
which may be emitted to the environment from oi shale retorting processes,
or which may find their way to the environment during subsequent portions of
the shale oil cycle. Such information is of interest not only in its own
right, but is also vital to related health and environmental fate and effect
studies. The data presented here are related specifically to one process,
the Paraho direct heated Semiworks Retort at Anvil Points, Colorado. How-
ever, it is probable that many of these data will have considerable general
validity for all types of oil shale retorting processes.
The objectives of this initial characterization study were to: (1) ob-
tain information on the partitioning of a number of trace and major elements
of potential environmental significance into the various retort products and
(2) obtain information on the physical and chemical forms of certain ele-
ments emitted from the retort. An additional important goal of the study
was to find or develop and verify suitable analytical technology to meet
these objectives.
DESCRIPTION OF THE PARAHO RETORT PROCESS
The Paraho surface retorting process has been described elsewhere
(Jones 1976). It is presently operated by Development Engineering, Inc.
(DEl) at Anvil Points, Colorado at the former U.S. Bureau of Mines site.
The shale used in the process is obtained from a room and pillar mine in the
Mahogany Zone of the Green River Formation. The mine at Anvil Points is
located at about 2440 meters of elevation, some 600 meters above the present
retort. At the processing site the mined shale is crushed and screened
between minus 7.6 cm and plus 0.6 cm. The crushed shale fraction (10-15%)
less than 0.6 cm is presently stockpiled.
DEl has recently operated two retorts at Anvil Points, an O.77m ID by
18m high pilot plant unit and 2.6m ID by 23m semiworks retort. All of the
studies detailed in this report were conducted on the semiworks retort.
251
-------
Both of the retorts can be operated in either a direct or indirect heated
mode. Since all of the samples used in this study were obtained during
direct heat operation, this mode will be briefly described here.
In direct heated operation, illustrated in Figure 1, controlled combus-
tion within the retort provides the heat necessary for retorting. The
process is continuous and flows are countercurrent with the gas phase
flowing upwards. The uniform downward flow of shale is controlled by a
patented, hydraulically operated grate mechanism. Input raw shale is dis-
tributed evenly at the top by a rotating distributor and is then preheated
by rising hot gases in the mist formation zone. Next, the preheated shale
passes through the retorting zone where the organic kerogen” is decomposed
into an oil mist, gas and carbon residue (coke). The retorted shale then
enters the combustion zone of the retort where the carbon residue and
recycle gas serve as fuel for combustion. Input air is distributed evenly
across the bed along with the recycle gas in this retort zone. In the
bottom of the retort, the shale is cooled by the incoming bottom recycle
gas, giving its heat to this gas; the retorted shale then exits through the
bottom of the retort and is conveyed to a storage site. The oil mist pro-
duced is carried out the top of the retort through the of fgas collector, and
is separated from the gas by a coalescer and electrostatic precipitator.
The product collected is an oil-water emulsion of about 5 wt % water. The
emulsion produced during each shift is collected in a small gauging tank
where it can be sampled. It is then pumped to a settling tank where the
water is separated and drained, and “dry” product oil is then pumped to
storage.
SAMPLE COLLECTION
Sampling is critical to a program of this type because meaningful
analytical results are dependent on the integrity and representativeness of
the samples. Obtaining representative samples from each important process
stream at an operating pilot plant presents a number of difficulties.
Contamination of the samples is another potential problem during field
sampling operations. Fortunately, the Paraho Semiworks Retort is well
designed for the purposes of sampling. After consultation with DEl person-
nel, a sampling program was developed to achieve the desired results. The
samples were collected mainly during two field trips, one for four days in
August 1977 and one for three days in November 1977. Sample collection and
necessary onsite analyses were performed using a PNL camper-mounted mobile
laboratory. The sample collection procedures have been described in detail
elsewhere (Fruchter et al.., 1979).
GENERAL SAMPLE PREPARATION
Solid Samples
Samples of the raw and retorted shale were received from the Paraho
shale samplers as sand-sized particles or smaller. These samples were
further ground to pass a 140-mesh sieve using alumina jar mills. Further
grinding was considered unnecessary for analytical purposes because it had
252
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KM
OIL MIST
SEPARATORS
MIST
F0I 1AT ION
AND
PREHEATING
ELECTROSTATIC
RETORTING
OIL
ZONE PRODUCT
GAS
ZONE ‘ ECYCLE
COMBUSTION
OIL
RESIDUE BL(YNER
COOLING AND
GAS
\ PREHEATING /
0-
GRATE SPEED
CONTROLLER
aBLOHER
RETORTED SHALE
Figure 1. Paraho Surface Retorting Proces5 (Direct Mode).
-------
the potential for causing significant contamination and alteration of the
samples. The samples were blended in a polyethylene mixer and split, using
a riffle splitter, into 50-gram aliquots for analysis.
Liquid Samples
Oil samples were warmed to room temperature, shaken and sampled direct-
ly. They were not filtered.
The water samples were filtered at the plant site through quartz wool
to remove oil and grease. No noticeable precipitate had formed in any
samples except those that had been acidified. The acidified samples were
further filtered at the laboratory. The other samples were shaken thorough-
ly and directly sampled. Further sample preparations for each analytical
method are described elsewhere (Fruchter et al., 1979).
INORGANIC ANALYTICAL METHODS--THE MULTITECHNIQUE APPROACH
Many of the samples obtained from oil shale retorts are chemically and
pbysically complex, creating the potential for matrix effects as well as
other types of interferences in many of the commonly used methods for chemi-
cal analysis. Therefore, the techniques employed for inorganic analysis of
the Paraho samples were chosen when possible for their relative freedom from
matrix effects as well as their sensitivity and precision. Because no one
method can at this time meet all of these requirements for all elements of
interest, a multitechnique approach was adopted. This multitechnique
approach to inorganic analysis also provided an opportunity to assess the
strengths and weaknesses of the various methods for different samples. The
major techniques used included instrumental neutron activation analysis,
energy dispersive X-ray fluorescence analysis, D.C. arc plasma emission
spectroscopy, flame atomic absorption spectroscopy and graphite furnace
atomic adsorption spectroscopy, ion selective electrodes, hydride generation
and various gas monitoring devices were used to supplement these techniques
for specific elemental and speciation analysis.
ANALYSIS OF AUGUST 24, 1977 SAMPLES
Special attention was given to the analysis of samples collected on
August 24, 1977, particularly the raw and retorted shale. There were
several purposes behind this additional effort. First, a multitechnique
intercomparison was carried out to enable objective assessment of the accu-
racy of the analytical data. The August 24 raw shale in particular was
rigorously characterized for some 50 elements. A large quantity (15-20 kg)
of this material has been archived and will eventually be made available to
other workers in the field as an analytical reference material. The data
given in this report combined with additional analyses provided by col-
leagues at Lawrence Berkeley Laboratory will form the basis for standardiza
tion of the material. In addition, a more limited analysis has been carried
out on two lots to verify the homogeneity of the material.
254
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Data Analysis
Tables 1 and 2 give the complete analytical results for analysis of the
August 24, 1971 raw and retorted shale for 50 elements. In many cases
several analytical techniques were used and it is possible to intercompare
results. In general, six replicate samples were analyzed by each analytical
technique. The analytical errors shown in Tables 1 and 2 are thus derived
from the precision of six analyses. In a few cases only a single determina-
tion was made. These include the radjochemical measurement of Cd, Se, Zn,
and U, the graphite furnace AA determination of Cd, and the cold-vapor AA
determination of Hg. To reduce the data to a form suitable for graphical
representation, a simple computer code, INCMP, was used. INCMP performs the
following operations:
1. A minimum error of 2% is assigned to all data. Any data with
a reported error less than 2% is set equal to 2%.
2. An error weighted average of the data is computed for each
element.
3. If more than two analyses are reported, Chauvenet’s criterion
is then applied and, if necessary, the worst outlier reject-
ed. A new error weighted average is then computed. This
procedure was only followed once due to the small size of
each data set. Very few determinations were actually
rejected in this manner. Those which were rejected are noted
with an asterisk in Tables 1 and 2.
4. Once an appropriate average value has been determined, a
percentage deviation of each individual value from the mean
for that element is computed. These values together with the
appropriate percentage standard deviation for each point in
the raw shale sample are plotted in Figure 2. This format is
convenient for viewing all of the data at once. Only ele-
ments which were analyzed by more than one reliable technique
are plotted; individual determinations rejected by
Chauvenet’s criterion (step 3) are omitted.
5. Additionally, a percent root mean squared deviation is com-
puted for each group of data (shown at top of Figure 2) and a
chi squared test was applied to test the validity of the
error analysis.
A number of observations can be drawn from Figure 2. Agreement between
analytical methods is in general quite good, and in almost all cases the
error bars overlap. One notable exception is the determination of As in the
raw shale by instrumental neutron activation and X-ray fluorescence. This
difference is only noticeable since both methods have good precision. Even
in this case the disagreement is only 10-15%. A wide range of precision is
evident, illustrating the advantages of using several different analytical
techniques for multielement analysis.
255
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TABLE 1. MULTIELEMENT ANALYSIS OF PARAHO-FEEDSIOCK SHALE COLLECTED 8/24/77
(in ppm except as noted)
Element
Al (5)
As
8
Ba
Br
Ca (5)
Cd
Ce
Co
Cr
Cs
Cu
Dy
Eu
Fe (5)
I Ga
U Hf
Hg
Ho
K (5)
La
Lu
Mg Cs)
Mn
Mo
Na (5)
Nb
Nd
Ni
Pb
Rb
S
Sb
Sc
Se
Si (5)
Sm
Sr
Ta
Tb
3.42 ± 0.05 3.59 ± 0.3
314.0 ± 22.0
20.9 * 1.9
27.6 * 0.6
1.73 ± 0.01
Error Weighted
Average
3.77 ± 0.06
44.3 ± 0.6
94.0 ± 2.0
512.0 * 10.0
0.57 ± 0.13
10.1 ± 0.2
0.64 ± 0.03
43.1 ± 0.9
9.0 ± 0.1
34.2 ± 0.6
3.84 ± 0.22
40.3 ± 2.1
2.4 ± 0.4
0.60 * 0.02
2.07 ± 0.02
1.75 ± 0.05
1.75 ± 0.05
0.089 ± 0.005
0.67 ± 0.11
1.61 ± 0.02
20.6 ± 0.7
0.28 ± 0.03
3.46 ± 0.06
315.0 ± 12.0
22.0 ± 1.5
1.69 ± 0.03
8.0 ± 0.7
20.4 ± 2.1
27.5 ± 0.6
26.5 ± 2.1
74.5 ± 1.8
5730.0 ± 500.0
2.09 ± 0.08
5.77 ± 1.6
2.03 ± 0.09
15.0 ± 0.3
3.10 ± 0.3
696.0 ± 11.0
0.55 ± 0.02
0.37 ± 0.03
RCAA XRF
GFAA CVAA
1MM
PES
FAA
3.89 ± 1.4
3.78
± 0.08
3.69
± 0.11
48.0 * 0.7
41.6 ±
5.0
483.0 ± 34.0
94.0 ±
515.0 ±
2.0
8.0
0.57 ± 0.13
10.4 ± 0.5
10.7 ±
0.5
9.9 ±
0.1
11.0 ±
0.4*
0.64 ± 0.03
0.61 ± 0.08
43.1 ± 0.9
9.0 ± 0.1
36.7 t 1.8
39.7 ±
9.3
33.8 ±
0.6
3.84 ± 0.22
40.3 ±
2.3
40.0 ±
5.5
2.4 ± 0.4
0.60 ± 0.02
2.08 * 0.04
2.02
± 0.10
2.01
± 0.04
2.14
± 0.04
8.4 ±
0.8
1.75 ± 0.05
0.67 ± 0.11
1.69 ± 0.11
1.66
± 0.02
1.79
± 0.03
1.55
t 0.03
20.6 ± 0.7
0.28 * 0.03
312.0 ± 20.0
319.0 ±
24.0 *
20.0
2.5
1.68 ± 0.01
8.0 ±
0.7
20.4 ± 2.1
23.0 ± 5.3
24.2 ±
26.5 ±
1.2
2.1
74.9 ± 2.3
74.0 *
5730.0 ±
2.7
500
2.09 * 0.08
5,77 * 0.16
2.1±0.2
2.0±0.1
2.7±0.7
14.1 ±
0.7
15.2 ±
0.1
3.10 ± 0.3
674.0 ± 24.0
678.0 *
21.0
712.0 *
14.0
0.55 * 0.02
0.37 * 0.03
0.089 ± 0.005
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TABLE 1. MULTIELEMENT ANALYSIS OF PARAHO-FEEDSTOCK SHALE COLLECTED 8/24/77 (CONTINUED)
(in ppm except as noted)
Element
INAA
RCAA
XRF
PES
FAA
GFAA
Error Weighted
CVAA Average
Th
6.33 ± 0.13
6.33
± 0.13
Ti
0.18 ± 0.02
0.17 ± 0.02
0.18 ± 0.01
0.18
± 0.01
U
4.2 ±
0.3
4.6 ± 0.2
4.5 ±
0.2
V
86.0 ±
6.0
95.0 ±
6.0
96.0 ± 3.0
94.2 ±
2.4
Y
14.0 ±
1.0
14.0 ±
1.0
Yb
1.26 ± 0.11
1.26
± 0.11
Zn
67.2 ±
3.7
63.0 ± 3.0
62.6 ±
2.3
73.2 ± 4.0*
63.6 ±
1.6
Zr
36.2 ± 1.3
36.2 ±
1.3
*Deleted from error weighted average.
INAA - Instrumental Neutron Activation Ana ysis
RCAA - Radiochemical Neutron Activation Analysis
XRF - X-Ray Fluorescence Analysis
PES - Plasma Emission Spectroscopy (Sodium Carbonate Fusion)
FAA - Conventional Flame Atomic Absorbtion (Lithium Metaborate Fusion)
GFAA - Graphite Furnace Atomic Absorbtion
CVAA - Cold Vapor Atomic Absorbtion
-------
Elesient
Al (%)
As
B
Ba
Br
Ca (%)
Cd
Ce
Co
Cr
Cs
Cu
Dy
Eu
Fe (%)
Ga
(JI Hf
Hg
Ho
K (%)
La
Lu
Mg (X)
Mn
Mo
Na (X)
Nb
Nd
Ni
Pb
Rb
S
Sb
Sc
Se
SI (%)
Sm
Sr
Ta
Tb
3.68 ± 0.07
820.0 * 26.0*
0.65 ± 0.02
0.42 * 0.04
3.88 ± 0.03 4.32 ± 0.09
374.0 ± 28.0
41.3 * 3.9
2.24 * 0.03
Error Weighted
Average
4.83 ± 0.10
59.4 * LU
107.0 ± 2.0
604.0 * 9.0
0.80 * 0.18
13.3 ± 0.2
0.91 ± 0.04
51.5 ± 1.5
11.1 ± 0.2
44.2 ± 0.9
4.68 ± 0.21
55.9 * 1.1
2.4 * 0.4
0.73 ± 0.02
2.40 ± 0.030
11.6 * 1.2
2.11 ± 0.03
0.035 ± 0.003
0.88 ± 0.04
1.86 ± 0.03
24.7 ± 0.3
0.35 ± 0.03
4.07 ± 0.06
396.0 ± 14.0
33.7 ± 1.3
2.19 ± 0.03
9.2 ± 1.5
22.3 ± 1.1
36.2 ± 2.0
88.4 ± 1.8
6780.0 ± 620.0
2.63 ± 1.5
6.84 ± 0.15
2.3 ± 0.1
18.2 ± 0.4
3.68 ± 0.07
879.0 ± 12.0
0.65 ± 0.02
0.42 1 0.04
TABLE 2. MULTIELEMENT A$ALYSIS OF PARAHO RETORTED SHALE COLLECTED 8/24/77
($n pp. except as noted)
RCU XRF
GFM CVAA
0.99 ± 0.13
1MM
PES
FM
4.83 ± 0.05
59.2 t 0.9
593.0 ± 13.0
0.80 ± 0.18
59.8 ± 1.9
4.46 ± 0.05
107.0 ± 2.0
613.0 * 12.0
4.56 ±
0.17
13.1 ± 0.5
51.5 ± 1.5
11.1 ± 0.2
44.3 ± 0.9
0.90
* 0.04
13.9 * 0.7
49.6 * 8.9
11.1 ± 0.2
41.0 ± 3.8
13.2 *
0.2
4.68 * 0.21
3.5 * 0.2
0.73 * 0.02
2.42 * 0.04
56.3 ± 1.0
2.56 ± 0.13
46.9 ± 5.3
2.35 ± 0.03
2.47 t
0.08
2.11 ± 0.03
11.6 ± 1.2
0.88 ± 0.04
1,98 ± 0.20
1.94 ± 0.05
2.11 ± 0.05*
1.81 ±
0.03
24.7 ± 0.3
0.35 t 0.03
388.0 ± 23.0
420.0 ± 24.0
32.7 ± 1.4
2.15 ± 0,03
22.3 1 LI
29.7 ± 4.6
9.2 ± 1.5
32.1 * 3.9
36.2 ± 2.0
32.4 * 1.8
89.9 ± 4.1
2.63 ± 0.15
6.84 ± 0.15
2.3 ± 0.1
88.0 ± 2.0
6780.0 ± 620.0
3.4 ± 0.5
17.8 ± 1.3
866.0 * 1.0
892.0 ± 14.0
0.035 ± 0.003
18.2 ± 0.3
-------
TABLE 2. NULTIELEMENT ANALYSIS OF PARAHO RETORTED SHALE COLLECTED 8/24/77 (CONTINUED)
(in ppm except as noted)
*Deleted from error weighted average.
INAA — Instrumental Neutron Activation Analysis
RCAA - Radiochemical Neutron Activation Analysis
XRF - X-Ray Fluorescence Analysis
PES — Plasma Emission Spectroscopy (Sodium Carbonate Fusion)
FAA - Conventional Flame Atomic Absorbtion (Lithium Metaborate Fusion)
GFAA - Graphite Furnace Atomic Absorbtion
CVAA - Cold Vapor Atomic Absorbtion
Qi
Element
INAA
RCAA
XRF
PES
FAA
GFAA
Error Weighted
CVAA Average
Th
7.35
± 0.10
7.55 ± 0.10
Ti
0.21
± 0.04
0.24 ± 0.01
0.20 ± 0.03
0.24 ± 0.01
U
5.3 ± 0.2
4.9 ± 0.2
5.10 ± 0.14
V
111.0 ± 13.0
139.0 ±
19.0
133.0 ± 7.0
129.0 ± 6.0
Y
16.4 ±
0.9
16.4 ± 0.9
Yb
1.61
± 0.13
1.61 ± 0.13
Zn
89.2 ± 3.0
77.0 t 4.0
86.2 ±
5 5
93.6 ± 5.3
82.3 ± 2.2
Zr
61.5 ±
2.8
36.2 ± 1.3
36.2 ± 1.3
-------
i +«
UJ
+10
tr
r\)
CT)
O
-10
-20
-40
2.2
Al
7.3
O INAA
• XRF
I
4.1
4.3
R.M.S DEVIATION (%)
3.0 10.3 0.5 2.6
A FLAME AA
A GRAPHITE FURNACE AA
I
1
I
l
4.2
2,8
0.9
7.3
D PLASMA EMISSION
X RCAA
I
I
As
Ba
Ca
Cd
Cr
Cu
Fe
K
Mg
Mn
Mo
Figure 2. Relative Performance of Analytical Techniques Used
in the Multielement Analysis of Paraho Raw Oil Shale (8-24-77).
-------
+ :JU
=£ +20
<
UJ
•BlSl
o i in
LU T 1U
1—
o
LU 0
S
Q£
1 -10
o£
^iji
s
g -20
LJ_
| -30
tac
o -40
^
-50
R.M.S DEVIATION (%)
1.8 11.7 0.6 2.0 4.4 2.7 2.8 4.8 5.2 3.4
T T T
1
JT 1
2
c
—
_
—
1 T T T T TT 6
5 XT TT I 5TT il Tf 6n UT
^ li 1 8 ..i it n
I 1 T i
1
) J-
O INAA A FLAME AA O PLASMA EMISSION
• XRF A GRAPHITE X RCAA
FURANCE AA
—
ill II III
Na Ni Rb Se Si Sr Ti U V Zn
Figure 2. (Cont.) Relative Performance of Analytical Techniques Used
in the Multielement Analysis of Paraho Raw Oil Shale (8-24-77).
-------
Retorted/Raw Element Ratios
The ratio of an element in the retorted shale to the same element in
raw shale is useful for assessing a material balance. Furthermore, any
systematic analytical errors should tend to cancel when a ratio is computed.
To test that premise, the ratio was calculated in a number of different
ways. Table 3 shows the elemental ratios calculated from the data in Tables
1 and 2 with error propagated quadratically. An error weighted average of
the data for each element is given in the last column of Table 3. Table 4
provides a sunwnary of the average ratio calculated for each technique with
suspected volatile elements such as As, Cd, Se, S and Hg deliberately
omitted. An overall unweighted average was computed for the best data on
approximately 20 nonvolatile elements yielding a ratio of 1.20 ± 0.02. The
loss of raw shale mass expected simply from the Fischer assay should yield
mineral phase element enrichments of only about 1.10 in the retorted shale.
The additional 10% enrichment observed for the Paraho combustive retorting
process is evidently due to unaccounted water and gas losses and to CO 2 loss
through thermal decomposition of dolomite and calcite.
The dashed lines shown in Figure 3 show the limits of the average ratio
computed above (1.20 ± 0.02). Mercury is clearly being released and redis-
tributed into products other than the retorted shale. At the maximum
retorting temperature (‘.600°C), the mercury should be quantitatively
removed. The mercury found on the retorted shale has probably been recon-
densed from the recycle gas. Sulfur is presumably also lost; however, that
is not determinable from the XRF analysis due to poor precision. Cadmium
actually seems to show a small additional enrichment in the retorted shale.
Cadmium has been shown to be mobilized somewhat during in situ retorting
(Fox et al., 1977). Graphite furnace atomic absorption studies in our
laboratory on raw and retorted shale show volatilization of cadmium at
500-600°C, in the same range as the maximum temperature reached in the
Paraho process. Some of the cadmium may thus be redistributed by the
recycle gas during retort operations. Temperature fluctuation during retort
operation may account for a nonequilibrium excell. Clearly more work is
needed on cadmium because its behavior is very sensitive to retorting
conditions.
REDISTRIBUTION OF ELEMENTS AMONG THE PRODUCTS
The redistribution of elements from the raw shale to the products and
effluents was calculated from the data in Tables 1 and 2 and from analyses
for oil, water and gas presented in lables 5, 6 and 7. Tables 5, 6 and 7
also show the amount of redistribution to each phase. The total redistribu-
tion can be characterized into three categories. The first category (I)
includes the elements which remain almost totally with the retorted shale
and have partitioning coefficients of less than 0.01% for the product oil,
product water, and product gas. This group includes the elements Al, Ba,
Ca, Cr, K, Mg, Mn, Na, Rb, Si, and Sr. The second category is characterized
by elements which have a cumulative partitioning coefficient (products other
than retorted shale) of from 0.01 to approximately 5%. This second category
(II) includes the elements As, B, Co. Cu, Fe, Ni, Sb, Se, Fe, Th, Ti, V. and
262
-------
TABLE 3. RATIO OF ELEMENT IN RETORTED SHALE TO ELEMENT IN RAW SHALE
Error
Weighted
Element INAA RCAA XRF PES FAA GFAA CVAA Average
Al
As
B
Ba
Br
1.24 ±
1.23 ±
1.23 ±
1.40 ±
0.05
0.03
0.09
0.45
1.44 ± 0.05
1.18 ± 0.03
1.14 ± 0.03
1.19 ± 0.03
1.24 ± 0.06
1.24
1,29
1.14
1.19
1.40
± 0.024
± 0.03
± 0.03
± 0.03
± 0.45
Ca
Cd
Ce
Co
Cr
1.26 ±
1.20 ±
1.23 ±
1.21 ±
0.08
0.04
0.03
0.02
1.41 ± 0.09
1.30 ± 0.09
1.21 ± 0.06
1.12 ± 0.02*
1.21 ± 0.11
1.20 ± 0.05
1.62 ± 0.30
1.23 ± 0.04
1.43 ± 0.09
1.20 ± 0.04
1.23 ± 0.03
1.21 ± 0.03
Cs
Cu
Dy
Eu
Fe
(%)
1.22 ±
1.45 ±
1.22
1.16 ±
0.09
0.26
± 0.05
0.03
1.40 ± 0.08
1.27 ± 0.09
1.17 ± 0.21
1.17 ± 0.03
1.15 ± 0.04
1.22 ± 0.09
1.37 ± 0.08
1.45 ± 0.26
1.22 ± 0.05
1.17 ± 0.02
Ga
Hf
Hg
Ho
K
(%)
1.21 ±
1.31 ±
1.17 ±
0.04
0.22
0,15
1.38 ± 0.19
1.17 ± 0.03
1.18 ± 0.03
1.17 ± 0.03
1.38 ± 0.19
1.21 ± 0.04
0.39 ± 0.04
1.31 ± 0.22
1.17 ± 0.02
La
Lu
Mg
Mn
Mo
(%)
1.20 ±
1.25 ±
1.24 ±
0.02
0.17
0.11
1.32 ± 0.11
1.36 ± Q.15
1.13 ± 0.02
1.19 ± 0.12
1.98 ± 0.26*
1.20 ± 0.05
1.20 ± 0.02
1.25 ± 0.17
1.14 ± 0.02
1.24 ± 0.07
1.33 ± 0.15
Na
Nb
Nd
Ni
Pb
1.28 ±
1.09 ±
1.29 ±
0.02
0.12
0.36
1.15 ± 0.21
1.33 ± 0.17
1.37 ± 0.13
1.17 ± 0.07
1.29 ± 0.05
1.28 ± 0.02
1.15 ± 0.21
1.09 ± 0.12
1.20 ± 0.06
1.37 ± 0.13
Rb
S
Sb
Sc
Se
1.20 ±
1.26 ±
1.19 ±
0.07
0.09
0.04
1.15 ± 0.08
1.19 ± 0.05
1.18 ± 0.15
1.26 ± 0.37
1.19 ± 0.04
1.18 ± 0.15
1.26 ± 0.09
1.19 ± 0.04
1.16 ± 0.08
Si
Sm
Sr
Ta
Tb
1.19 ±
1.22 ±
1.18 ±
1.14 ±
0.03
0.06
0.06
0.14
1.26 ± 0.04
1.28 ± 0.04
1.25 ± 0.03
1.20 ± 0.02
1.20 ± 0.02
1.19 ± 0.03
1.26 ± 0.02
1.18 ± 0.06
1.14 ± 0.14
-------
TABLE 3, RATIO OF ELEMENT IN RETORTED SHALE TO ELEMENT IN RAW SHALE (CONTINUED)
Element
INAA
RCAA
XRF
PES
FAA
GFAA
Error Weighted
CVAA Average
Th
1.19 t 0.03
1.19
± 0.03
11
1.17 ± 0.26
1.41 ± 0.18
1.11 ± 0.17
1.33
± 0.15
U
1.26 ± 0.10
1.07 t 0.06
1.08
± 0.05
V
1.29 ± 0.18
1.46 ± 0.22
1.39 ± 0.08
1.38
± 0.07
V
1.17 ± 0.11
1.17
± 0.11
Yb
1.28 ± 0.15
1.28
± 0.15
Zn
1.25 ± 0.08
1.22 ± 0.09
1.38 ± 0.10
1.18 ± 0.10
1.26
± 0.05
Zr
1.16 ± 0.08
1.00 ± 0.10
1.10
± 0.06
*Deleted from error weighted average.
INAA - Instrumental Neutron Activation Analysis
RCAA - Radiochemical Neutron Activation Analysis
XRF - X-Ray Fluorescence Analysis
PES - Plasma Emission Spectroscopy (Sodium Carbonate Fusion)
FAA - Conventional Flame Atomic Absorbtion (Lithium Metaborate Fusion)
GFAA - Graphite Furnace Atomic Absorbtion
CVAA - Cold Vapor Atomic Absorbtion
-------
TABLE 4. RETORTED/RAW SHALE RATIO SUMMARY FOR NONVOLATILE ELEMENTS
PARAHO OIL SHALE COLLECTION 8/24/77
Method
Error Weighted
Average
Unweighted
Average
Error <5%
•
INAA
1.20
± 0.01
(32)*
1.20
± 0.02 (14)
XRF
1.24
± 0.02
(20)
1.21
± 0.05 (4)
PES
1.17
± 0.01
(15)
1.18
± 0.04 (8)
FAA
1.19
± 0.01
(8)
1.21
± 0.05 (7)
Overall
----
1.20
± 0.019 (33)
* Number in () is number of measurements used in average.
INAA Instrumental Neutron Activation Analysis
XRF X-ray Fluorescence (Energy Dispersion)
PES = DC-Coupled Plasma Emission Spectroscopy
FAA = Conventional Flame Atomic Absorption
265
-------
2.0
1.8
1,6
1.4
1.2
ce
o
INS
Oi
Ch
0,6
0.4
O.Z
1 1 1 1 1 1 1 1 1 1 1
Al As B Ba Br Ca Cd Ce Co Cr Cs Cu Dy Eu Fe Ga Hf Hg Ho K la lu Mcj MnMo Na Nb Nd Mi Pb Rb S Sb Sc Se Si Sm Sr Ta Tb Th Ti U V Y Yb Zn Zr
Figure 3.
-------
Table 5 . Percentage of Elements Transfered From
Feedstock Raw Shale to Product Oil
(8—24—77)
Element
Abundance In
Raw Shale, ppm
Abundance In
Product Oil, ppm
% Element Transfered
From Raw Shale To
Product Oil
H
C
N
S
Hg
15,000
155,000
6,600
6,000
0.089
117,000
860,000
23,900
7,400
0.30
65.4
46.5
30.3
10.4
28.2
As T
Se
Co
Ni
Sb
Cu
Zn
V
Fe
Th
Ti
44
2.0
9.0
28
2.1
40
64
94
20,700
6.3
1,800
31
0.91
0.93
2.74
0.028
0.42
0.41
0.25
49
0.009
2.6
5.9
3.8
0.87
0.82
0.11
0.088
0.054
0.022
0.020
0.012
0.012
My
Mn
Al
Ca
Si
Na
34,600
315
37,700
101 OOO
150,000
16,900
1 25
0.21
16
42
55
4 3
0.0061
0.0056
0.0036
0.0035
0.0031
0.0021
267
-------
Table 6 . Percentage of Elements Transfered From
Feedstock Raw Shale to Product Water
(8-24-77)
As
Hg
Ni
Cu
Mg
Sb
44
0.089
28
40
34,600
2.1
5.7
0.0023
0.54
0.70
490
0.020
0.050
0.010
0.0074
0.0067
0.0055
0.0037
Element
Abundance In
Raw Shale, ppm
Abundance In
Product Water, ppr
% Element Transfered
From Raw Shale To
Product tiater
H
S
Se
N
B
C
15,000
6,000
2.0
6,600
94
155,000
105,000
39,000
9.8
23,000
43
25,000
2.7
2.5
1.9
1.3
0.18
0.062
I
Sr
Mn
I V
I Ca
Si
Fe
I
700
315
94
101,000
150,000
20,700
I
0.85
0.18
0.045
38
41
1.5
0.00048
0.00022 1
0.00018
0.00015
0.00011
0.000028
[
Mo
Na
Zn
Co
Ti
Rb
22
16,900
64
9.0
1 ,800
75
0.15
111
0.41
0.03
5.1
0.14
0.0026
0.0025
0. 0025
0.0013
0. 0011
0. 00072
268
-------
TABLE 7. PERCENTAGE OF ELEMENTS TRANSFERRED FROM
FEEDSTOCK RAW SHA .E TO PRODUCT GAS
Element R
Abundance in
aw Shale, ppm
% Element Transferred
Abundance in from Raw Shale to
Product Gas Product Gas
H
15,000
26.7 (g/rn 3 )
40.0
C 155,000
146.0 (g/m 3 )
21.1
N
6,600
5.4 (g/m 3 )’
17.9
S
6,000
3.4 (g/m 3 )
12.7
Hg
0.089
75.0 (pg/rn 3 )
23.0
As
44
155.0 (pg/rn 3 )
0.079
N as NH 3
269
-------
Zn. While they were not detected in the product oil, water, or gas, three
additional elements, Mo, Pb, and U, cannot be ruled out as members of this
group due to their detection limits. The third category (III) is character-
ized by the elements which have greater than 10% of their mass redistributed
into products other than the retorted shale. This group includes the major
elements C, H, N, and S, and the volatile heavy metal Hg.
The redistribution of category I element into the product oil is
primarily in the form of raw and/or retorted shale fines. This statement is
based on data in Table 5, which tabulates the partitioning coefficients for
all elements detected in the product oil. The partitioning coefficients for
the mineral elements Al, Ca, Mg, Mn, Na, and Si ranged from 0.0021 to
0.0061% with a mean and relative standard deviation of 0.0040 ± 0.0015%.
The small but essentially equal fractions of these six elements in the
product oil indicate that they are most likely being redistributed together
as shale particulate matter.
If the base level of elements associated with shale particulates in the
product oil is set equal to the mean value calculated above (0.0040%) plus
two standard deviations (0.0030%), it is reasonable to conclude that ele-
ments with partitioning coefficients exceeding the sum (0.007%) are present
in additional chemical forms. Analytical errors or sample contamination may
invalidate this statement for a few elements close to the 0.007% limit
(e.g. , Ti and Th); however, for elements with partitioning coefficients of
0.02% or greater (e.g., Fe), this difference is considered to be real. The
elements of category II are probably redistributed as volatile metallic or
organo-mettalic compounds. This statement is supported by other mass
balance studies (Fox et al., 1977; Chendrikar and Faudel, 1978) and by
investigations which report that As and Fe (Sullivan et al., 1977) cannot be
removed from Paraho shale oil filtered through 15-micron filters. An addi-
tional explanation is that very fine grained and insoluble trace minerals,
chemically unlike the shale fines, are being preferentially precipitated or
transported into the product oil.
The observed redistribution of Category III elements into the product
oil ranged from 10.4% for S to 65.4% for H. A significant fraction (30.3%)
of the trace element Hg was observed to partition into the product oil.
This partitioning coefficient agrees well with others reported for Hg in
various oil shale retorting processes (Fox et al., 1977) and in laboratory
retorting studies (Donnell and Shaw, 1977).
The abundances and partitioning coefficients for elements detected in
the product water are summarized in Table 6. Because the August 24 water
sample was collected from one of the small gauging tanks, it represented
product water which had only recently been coproduced by the retorting
process. Other more “aged” product waters were collected on August 26 and
November 15 and 16 from the large settling tanks used for oil-water separa-
tion. The product water obtained from these tanks is typically several days
older than water in the gauging tanks.
270
-------
The partitioning coefficients for S and Se in the August 24 product
water are 2.5 and 1.9%, respectively. This rather close agreement may be
related to the elements’ chemical simi1 ritjes. The total S measured in the
product water was 3.9%; however, the S levels were only a few ppm. About
0.05% of the raw shale As was observed to partition into the product water.
The ch mical form of the arsenic in the product water is about 50% As and
50% As . No methyl or dimethyl arsenic was detected.
The abundances and partitioning coefficients for elements detected in
the product gas are summarized in Table 7. Significant fractions of the
elements C, H, Hg, N, and S are released from the raw shale and redistrib-
uted into the product gas. In addition, a small fraction of the As (0.08%)
was also observed to partition into the gas. The element hydrogen is
redistributed in the gas chiefly as water vapor, H 2 , CH 4 , and other hydro-
carbon gases. Carbon is redistributed as hydrocarbon gases, as CO and CO 2
from combustion, and as CO 2 from decomposition of the carbonate minerals.
Nitrogen is redistributed as NH 3 and other nitrogen species in the gas
phase.
The partitioning coefficient for total sulfur redistributed into the
product gas was observed to be 12.7%. The partitioning coefficient for Hg
in the gas was observed to be 23.0%. The chemical form is assumed to be
elemental mercury vapor (Hg°) as organic forms should be condensed with the
product oil. The small fraction of arsenic in the product gas was primarily
inorganic AS 2 0 3 vapor. Only a small quantity of arsine or methylated arsine
was detected.
SUMMARY AND CONCLUSIONS
In organic analysis of solid, liquid and gaseous samples from the
Paraho Semiworks Retort were complet.ed using a multitechnique approach.
Most of the techniques used instrumental methods, so that interferences from
chemically complex matrices could be minimized. In many cases, analytical
techniques were altered or improved in order to make them applicable to oil
shale samples.
The data were statistically analyzed to determine both the precision of
each method and to see how closely the various techniques compared. The
data were also used to determine the redistribution of 31 trace and major
elements in the various effluents, including the offgas for the Paraho
Retort operating in the direct mode. The computed mass balances show that
approximately 1% or greater fractions of the As, Co, Hg, N, Ni, S and Se are
released during retorting and redistributed to the product shale oil, retort
water or product offgas. The fraction for these seven elements ranged from
almost 1% for Co and Ni to 50-60% for Hg and N.
Approximately 29% of the S and 5% of the As and Se are released. The
mass balance redistribution during retorting for Al, Fe, Mg, V and Zn was
observed to be no greater than 0.05%. These redistribution figures are
generally in agreement with previous mass balance studies made for a limited
number of elements on laboratory or smaller scale pilot retorts (Fox et al.,
271
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1977; Shrendrik r and Gaudel, 1978; Wildeman, 1977; Fruchter et al. , 1978).
Thus, the mass balances reported here for the Paraho Retort may have some
general validity for other types of oil shale retorting technologies.
Prepared for the U.S. Department of Energy under Contract EY-76-C-06-
1830.
REFERENCES
Donnell, J.R. and V.E. Shaw. 1977. “Mercury in Oil Shale from the
Mahogany-Zone of the Green River Formation, Eastern Utah and Western
Colorado.” Journal of Research of the U.S. Geol. Survey , Vol. 5, No.
2, March-April, 1977, pp. 221-226.
Fox, J.P. , R.D. McLaughlin, J.F. Thomas and R.E. Poulson. 1977. “The
Partitioning as As, Cd, Cu, Dg, Pb, and Zn During Simulated In Situ Oil
Shale Retorting.” Proc. of the 10th Oil Shale Symposium , Colorado
School of Mines, Golden, Colorado.
iruchter, J.S., J.C. Laul, M.R. Petersen, P.W. Ryan and M.E. Turner. 1978.
“High Precision Trace Element and Organic Constituent Analysis of Oil
Shale and Solvent-Refined Coal Materials.” Advances in Chemistry
Series 170. pp. 225-289, American Chemical Society.
Fruchter, J.S., C.L. Wilkerson, J.C. Evans, R.W. Sanders and K.H. Abel.
1979. “Source Characterization Studies at the Paraho Semiworks
Retort.” Pacific Northwest Laboratories Report PNL-2945. In prepara-
tion.
Jones, J.B., Jr. , 1976. “The Paraho Oil-Shale Retort.” Quart. Cob. School
of Mines , 71 (No. 4), pp. 39-48.
Schendrikar, A.D. and G.B. Faudel. 1978. “Distribution of Trace Metals
During Oil Shale Retorting.” Environmental Science and Technology , Vol
12, No. 3, March 1978, pp. 332-334.
Sullivan, R.F., H.A. Frumkin, C.E. Rudy and H.C. Chen. 1977. “Refining and
Upgrading of Synfuels from Coal and Oil Shales by Advanced Catalytic
Processes.’ U.S. Department of Energy Report FE-2315-15, p. 4.
Wildeman, 1. 1977. “Mass Balance Studies in Oil Shale Retorting.” In
Trace Elements in Oil Shale , Progress Report, June 1, 1975-May 31,
1976. Environmental Trace Substances Research Program, University of
Colorado, Colorado State University and Colorado School of Mines,
February 1977.
272
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APPLICATIONS OF DISSOLVED ORGANIC CARBON FRACTIONATION ANALYSIS
TO THE CHARACTERIZATION OF OIL SHALE PROCESSING WATERS
Jerry A. Leenheer
Hydrologist,
U.S. Geological Survey
Denver. Colorado
David S. Farrier
Section Supervisor,
Laramie Energy Technology Center
Department of Energy Laramie, Wyoming
ABS1RACT
Dissolved organic carbon (DOC) fractionation analysis is a method which
separates and quantitates organic solutes dissolved in water into hydrophob-
ic acid, base, neutral, and hydrophilic acid, base, neutral compound
classes. Analytical-scale DOC fractionation, which gives DOC distribution
of the six solute fractions, has been used to determine changes in organic-
solute composition of surface and groundwater with input of oil shale
processing water. Preparative-scale DOC fractionation, which generates
organic solute fractions for further study, has been used for characteriza-
tion of organic solutes in oil shale processing waters, and, through a
modification utilizing activated carbon, has been used to prepare gram-sized
fractions for biological testing of organic solutes in oil shale processing
water. Both analytical and preparative-scale DOC fractionation were used to
study sorption of organic solutes from oil shale processing waters on
processed-shale and soil sorbents to predict the transport of these solutes
in surface and groundwater systems.
INTRODUCTION
Methodology for dissolved organic carbon (DOC) fractionation analysis
was recently developed at the National Water Quality Laboratory (U.S. Geo-
logical Survey) (Leenheer and Huffman, 1976) to serve as an organic-compound
class analysis that fills the gap in organic-solute characterization between
organic-solute concentration in water (DOC) and specific compound analysis.
The fractionation is based on physical adsorption of hydrophobic solutes on
a nonionic, acrylic-ester macroreticular resin, and ion-exchange adsorption
of hydrophilic solutes. Six characteristic fractions (hydrophobic acids,
bases, neutrals, and hydrophilic acids, bases, neutrals) are obtained at the
end of the fractionation procedure. The procedure is quantified by organic-
carbon analysis of the various fractions.
273
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Since its inception, DOC fractionation analysis has generally been used
in two different manners. (1) Analytical-scale DOC fractionation which
determines the sixfold separation of DOC in water into various compound
classes, can be performed on a small (200-mL) water sample whose DOC content
is 5-25 mg/L and whose specific conductance is less than 2,000 imhos/cm at
25°C. A standard method for analytical-scale DOC fractionation is published
(Leenheer and Huffman, 1979). (2) Preparative-scale DOC fractionation is
used to generate gram-sized organic solute fractions for additional study
and characterization; no standard method is intended for preparative—scale
DOC fractionation because of the highly variable nature of research objec-
tives for organic-solute fractions isolate from water.
Use of DOC fractionation analysis and its various modifications is most
valuable when the research objective is to obtain intermediate-level quali-
tative and quantitative information about the nature of organic solutes
dissolved in water. It provides the basis for a phramidal classification of
organic solutes in water, beginning with DOC at the pyramid apex, and ending
with each specific organic compound at the pyramid base (Leenheer and
Huffman, 1976, p. 739). DOC fractionation analysis is also very useful for
quantitative determination, isolation, and study of high molecular-weight
r.atural organic polyelectro ytes, which constitute tne bulk of organic
carbon dissolved in natural surface and groundwaters (Malcolm, Thurman, and
Aiken, 1977).
Oil shale processing is accompanied by coproduction of wastewaters
which are heavily laden with organic and inorganic constituents. Wastewater
production is particularly significanL for in situ oil shale processing. A
recent report (Farrier and others, 1978) describes the sources and amounts
of waters derived from in situ processing and summarizes water—related
research being conducted by the Laramie Energy Technology Center (U.S.
Department of Energy). This research includes management, treatment, and
utilization of process waters. Environmental,studies include their effects
on biological systems, their characterization, and their disposition, trans-
port, and fate in the environment. DOC fractionation analysis has been
applied in many of these studies and has been proven a useful analytical
approach toward characterization of organic—solutes in process-derived
waters.
The purpose of this report is to cite and evaluate applications of DOC
fractionation analysis to characterization of oil shale processing waters.
The use of DOC fractionation analysis for determining the nature of sorptive
interactions of process waters with processed shale and soil sorbents will
also be discussed.
DISCUSSION OF EXPERIMENTAL PROCEDURES
Analytical-Scale DOC Fractionation of Oil Shale Processing Waters
A description of the standard method for analytical-scale DOC fraction-
ation is under preparation and should be published in the near future
(Leenheer and I uffman, 1978). The flow chart for this analytical scheme is
given in Figure 1.
274
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Water samples for DOC fractionation should be filtered through a 0.45
im silver membrane or glass fiber filter before they are processed. Passage
of a iater sample through the three columns is a two-step process. It is
first passed through the )(AD-8 column; then, after the hydrophobic base
fraction is eluted and the sample pH is adjusted, it is passed through all
three columns in series. Oesorption of the hydrophobic-acid fraction and
collection of sample aliquots for DOC analysis at various points in the flow
scheme of Figure 1 constitute the remainder of the procedure. All DOC
analyses should be performed with a carbon analyzer whose limit of detection
is 0.1 mg/I. Oil shale processing waters typically contain high concentra-
tions of organic solutes (500-20,000 mg/I DOC); they should be diluted with
distilled water before the fractionation is performed, until the DOC is near
25 mg/I. This dilution is required for the following reasons: (1) high
organic-solute concentration lead to ion-pair formation between organic
acids and bases, which results in acids in base fractions and bases in acid
fractions; (2) high inorganic-solute concentrations enhance the adsorption
of organic—inorganic acid and base salts; as a result, the separation
between organic and inorganic salutes is decreased; (3) acidification of oil
shale processing waters frequently produce elemental—sulfur precipitates
which foul the resin acisorbents (Stuber and Leenheer, 1978A); sulfur-
Figure 1. Flow diagram for analytical-scale DOC fractionation.
275
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precipitate forc ation is almost eliminated if DOC frac .ionation is performea
on highly-diluted process water; (4) organic-solute distribution coeffi’
dents change when a resin adsorbert becomes “saturated” at high solute
concentrations.
Preparative-Scale DOC Fractionation of Oil Shale Processing Waters
For most applications, analytical-scale DOC fractionation cannot be
used as a preparative procedure because the quantity of each fraction
desired (10 mç.-lOg) requires the use of prohibitively large volumes of
diluted samples and large columns of resin adsorbents. A compromise must be
struck between diluting to the “ideal” conditions of the analytical-scale
fractionation versus no dilution, with all of the attendant problems
discussed previously. As most of the solute-solute and solute-resin inter-
actions occur in the hydrophobic-solute fractions, an assessment of these
interactions can be determined by passing a process water at various dilu-
tions through columns of XAD-8 at a fixed ratio of water to resin adsorbent,
without “saturating” the resin and monitoring the DOC adsorbed versus dilu-
tiQn. Table 1 shows the effect of dilution on hydrophobic DOC adsorption of
an oil shale processing water obtained from the 150-ton simulated in situ
retort of tile Laramie Energy Technology Center (Run 13, Barrel 66). At the
100 and 1,000-fold dilution level, hydrophobic D CC adsorption was indepen-
dent of concentration, whereas at the 0 and 10-fold dilution level there was
a high dependence. Based on the results of Table 1, considerations of water
volumes (<40 L), resin quantities (<4 1), and the fraction quantities
desired (1-3 g organic carbon), the 10-fold dilution factor (DOC 5O7) was
chosen for preparative-scale fractionation. Variability in amounts and
types of solutes found in different process waters (Jackson and others,
1975; Fox and others, 1978) will require this type of preliminary evaluation
for each study sample.
TABLE 1. ADSORPTION OF WVDROP}-IOBIC DOC FROM 150-TON
RETORT WATER ON XAD-8 VS DILUTION FACTOR
Dilution
Factor
DOC
Percent DOC
Adsorbed
Percent DOC
Eluted
0
5,070.0
59.8
40.2
10
507.0
40.2
59.8
100
50.7
31.8
68.2
1,000
5.07
32.2
67.8
A second major problem with preparative-scale DOC frationation of oil
shale processing waters is formation of sulfur precipitates when these
waters are acicified. Removal of the elemental sulfur precipitate by f ii-
tration or centrifugation is not a good approach, because sulfur is a good
adsorbent for hydrophobic organic acids, and a substantial portion of this
fraction is removed with the sulfur. The approach used was to convert
276
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c -’- ’ 4- - 1 + - 4-,. 4- .. 4- .. -.4 ’ .I_._
— . . .. . - . . . . . —. ..i .. . . .. . . _ , _ .. . - . .
sample with a toichiometric quantity of sodium tn-iodide. Acidification
of the titrated sample did not cause any precipitates to form.
The final major difficulty with preparative-scale DOC fractionations of
oil shale processing waters is high concentration of carbonate and bicarbon-
ate species in these waters. When most of the anions were carbonates and
bicarbonates, it is advantageous to modify the sample-flow schem of DOC
frationation, so the water is acidified by passage through hydrogen-
saturated ion-exchange resin, instead of by NC1 addition. The ion-exchange
resin converts carbonates and bicarhonates to CO 2 gas. A much smaller
amount of anion exchange resin is required in the next column if the carbon-
ate and bicarbonate equivalents have been removed as CO 2 in the cation
exchange column, rather than being replaced by chloride equivalents which
must later be removed on anion-exchange resin. To remove carbon-dioxide
gas, a I was placed in the sample delivery tube at the head of the column,
and the CO 2 was drawn off the column headspace by a vacuum generated by a
water asperator. If the cation-exchange column is greater than 5 cm in
diameter, CO 2 gas that froms will rise in bubbles through the exhausted
portion of the resin bed until it collects in the headspace area. With
small diatneler columns, CO 2 gas moves downward through the column, disturbs
the flow, and diffuses the reaction front which decreases the resolution of
the solute separation.
The flow scheme finally devised for preparative-scale DOC frationation
of a sample of processing water from the 150-ton oil shale retort is shown
in Figure 2. Preparative-scale DOC fractionation is quantified by organic-
carbon analysis of the sample stream at each step. Recoveries of the
various fractions from the resin sorbents are determined by comparing organ-
Ic carbon adsorbed vs organic carbon desorbed in each eluent. Five of the
fraction eluent.s are in water, and DOC can be determined by a number of
standard procedures. However, the hydrophobic neutral fraction is dissolved
in methanol (Casterline and Leenhee”, 1978). Recoveries of hydrophobic-
solute fractions are essentially quantitative from the XAD-8 resin; however,
recoveries vary between 60 and 80 percent for the hydrophilic bases and
acids from ion-exchange resins.
Preparation of Fractions for Study of Biological Effects
A modified preparative-scale DOC fractionation was recently developed
by Huffman (1979) to generate organic-solute fractions suitable for delinea-
tion of toxic materials in conjunction with evaluating biological effects of
in situ-produced waters (Farrier and others, 1978). The preparative-scale
thodology discussed previously is ur suitable because it may induce changes
in a sample during pH adjustment; certain fractions were in solvents unsuit-
able for testing; and too many fractions were obtained by the procedure for
use in the testing program. An in situ oil shale processing water desig-
nated Omega 9 (Farrier and othe , 1977, 1978) was fractionated by this
modified procedure. A flow-chart of the procedure is given in Figure 3.
The procedure was quantified by organic-carbon analysis of the sample flow
stream and the column eluents.
277
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Figure 2. Flow diagram for preparative-scale DOC fractionation of oil shale processing
water.
-------
Comparison of DOC Fractionation Procedures
A basis for comparison of various DOC fractionation procedures is
provided by analytical-scale DOC fractionation. After the organic-solute
concentration of a water sample is diluted to DOC 25 mg/L, the
hydrophobic -hydrophilic separation is mainly dependent on the distribution
coefficient k’ for organic-solutes sorption on SAC-8 resin. The column
distribution coefficient k’ , and its relation to the mass distribution
coefficient D , is shown as follows:
mass of solute sorbed on XAD-8
k’ = (1)
mass of solut.e dissolved in water
Hydrophobic
Materials
Adsorbed
Hydrophilic
Materials
Adsorbed
[
R ,covered
Hydrophyllc
Organics
I
279
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.—,
mu Oi a r
D=k’ . (2)
g of XAD-8
In analytical-scale DOC fractionation, the experimental procedure is defined
so organic solutes that sorb in the r!onionic state, whose k’ are > than 110
are termed hydrophobic, and those whose k’ are <110 are termed hydrophilic
(Leenheer and -Iuffman, 1979). A listing of k’ values for various organic
standard-compound sorption on XAD-8 is given in a report by Thurman,
Malcolm, and Aiken (1978).
An oil shale processing water from the 150-ton simulated in situ retort
and the in situ “Omega 9” oil shale processing water were fractionated by
both preparative-scale and analytical-scale procedures; results are present-
ed in table 2. The major difference between these preparative-scale and
analytical-scale DOC fractionations is the increase in the percentage of
hydrophobic solutes for preparative-scale fractionation. This difference
was expected because the lower ratio of sample volume to XAD-8 resin in
preparative—scale fractionation gave a hydrophobic: hydrophilic break at a
k’ of 110 for analytical-scale fractionation. Most of the change going from
analytical-scale to preparative-scale fractionation is caused by conversion
of hydrophilic acid and base solutes to hydrophobic-neutral solutes. The
solute-solute and solute-resin interactions discussed previously, along with
the differenc in k’ break, are believed to cause the changes in preparative-
scale fractionation. Because these changes in preparative-scale fractiona-
tion are an undesirable departure from fractionation produced by analytical-
scale procedure, additional purific ition of individual solute fractions
obtained from preparative-scale fractionation is often necessary. For
example, the hydrophobic base fraction can be purified of most undesirable
solute species by another cycle of adsorption and desorption on XAD-8 resin.
A comparison of preparative-scale fractionation modified for
biological-effects testing (Figure 3) vs analytical-scale DOC fractionation
of Omega 9 water was given in the report by Huffman (1979). The results are
given in Table 3.
Two major changes of the modified preparative-scale procedure are
omission of sample acidification and recycle through XAD-8, and substitution
of activated carbon for ion-exchange resins. The effect of not acidifying
the sample was assessed by omitting this step in an analytical-scale frac-
tionation. The effect, as shown in Table 3, was to shift most of the hydro-
phobic acids into the hydrophilic-acid fraction. Altering the procedure
from analytical-scale fractionation without sample the pH adjustment to
modified-preparative scale fractionation, increased the hydrophobic solute
percentage (as discussed in the previous section of this report). The
“carbon-adsorbable” fraction and “ionadsorbable” fraction of modified
preparative-scale fractionation were characterized by redissolving these
fractions in water and performing an analytical-scale DOC fractionation on
each fraction. The “carbon adsorbables” consisted mainly of hydrophobic
acids, hydrophilic acids, and hydrophilic neutrals, whereas the “nonadsorb—
ables” consisted mostly of hydrophilic acids and neutrals. The main
280
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TABLE 2. PREPARATIVE-SCALE VS ANALYTICAL-SCALE DOC FRACTIONATIONS IN PERCENT OF DOC
150-ton Omega 9
Retort Water D0C5,000 mg/L Retort Water DOC1,000 mg/L
Analytical-Scale Preparative-Scale Analytical-Scale Preparative-Scale
Fraction
Fractionation Fractionation Fractionation Fractionation
Hydrophobic
solutes 55 65 49 61
Hydrophilic
solutes 45 35 51 39
Hydrophobic
bases 9 9 13 14
Hydrophobic
acids 26 28 19 18
Hydrophobic
neutrals 20 28 17 29
Hydrophilic
bases 15 8 12 9
Hydrophilic
acids 23 17 29 15
Hydrophilic
neutrals 8 10 10 15
TAbLE 3. MOD1 IED PREPARATiVE-SCALE VS ANALYTICAL-SCALE DOC FRACT1ONAT1ONS
FOR OMEGA 9 WATER IN PERCENT OF DOC
Analytical-Scale
Fractionation without
Analytical-Scale Sample pH Adjustment Modified-Preparative-
Fraction
Fractionation and Recycle Step Scale Fractionation
Hydrophobic
solutes 49 34 38
Hydrophilic
solutes 51 66 62
Hydrophobic
bases 13 13 16
Hydrophobic
acids 19 4 --
Hydrophobic
neutrals 17 17 22
Hydrophilic
bases 12 12 Carbon adsorbables 47
Hydrophilic
acids 29 46 Nonadsorbables 15
Hydrophilic
neutrals 10 8 --
-------
linitaticn of this modified preparative-scale fractionation scheme is lo’
recovery (32 percent) of the carbon-adsorbable fraction.
APPLICATIONS OF DOC FRACTIONATION ANALYSIS
Various developments in, and modification of, DOC fractionation analy-
sis have led to many diverse applications of the methodology. Most applica-
tions have been in characterization of oil shale processing waters, and the
data is being considered to develop guidelines for control-technology
aspects of environmental research dealing with oil shale processing.
Water Quality Monitoring
A recent report by Stuber and Leenheer (1978B) assessed analytical-
scale DOC fractionation as a water quality monitoring parameter for inputs
of oil shale processing water into natural surface waters. Several natural
surface waters from the White River Basin in Eastern Utah were characterized
by OOC fractionation analysis. Inputs of oil shale processing water into
these surface waters would change the hydrophobic base fraction significant-
ly. The authors concluded that inputs into natural surface waters of less
than one part of 250 of Omega 9 processing water, or one part in 1,000 of
150-ton processing water could be detected by DOC fractionation. Changes in
the other five fractions were not sufficiently diagnostic to justify a
monitoring program utilizing the complete DOC fractionation, so they sug-
gested using only the hydrophobic base fraction for monitoring processing
water inputs into surface waters.
Changes in groundwater quality determined by DOC fractionation as a
result of in situ oil shale processing are given in another report by Stuber
and Leenheer (1978A). Analytical-scale DOC fractions were performed on
groundwater withdrawn from the Rock Springs Site 9 in situ retort of the
Laramie Energy Technology Center (Long and others, 1977) before retorting,
during retorting (Omega 9), and one year after retorting. These DOC frac-
tionations give characteristic fraction ratios for organic-solute distribu-
tions in the natural groundwater, tI e Omega 9 process water, and of most
interest, the degree of mixing of natural organic solutes with process-
derived organic solutes one year aft€r the burn. More than one-half of the
organic solutes from water withdrawn from Site 9 production wells one year
after the burn were natural organic solutes from incursive groundwater.
Organic Solute Sorption Studies
DOC fractionation analysis is an excellent method for studying organic-
solute sorption phenomena from complex solute mixtures, such as oil shale
processing water upon heterogeneous sorbent surfaces, for example, soil,
sediment, and processed shale. Both preparative-scale and analytical-scale
fractionations can be used to define and quantify the affinity of various
solute fractions for a sorbent, and this information can be used in organic-
solute transport models for oil shale processing water movement in surface
and groundwater systems.
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The preparative -scale fractionation method presented in Figure 2 was
used to generate organic-solute fractions for sorption isotherm studies of
150-ton retort processing water on processed shale from the TOSCO II process
(Stuber and Leenheer, 1978A). TOSCO II processed shale was found to have a
greater affinity for organic-acid fractions than organic-base fractions, and
greater affinity for hydrophobic fractions compared with respective hydro-
philic fractions.
Analytical-scale fractionation is presently used to assess sorptive
interactions of Omega 9 processing water with soil sampled on Site 9. In
this procedure, a sorption isotherm of unfractionated processing water is
run for various concentrations of soil; after sorption is complete, an
analytical-scale DOC fractionation is run on the solution phase of each
soil-water mixture. Preliminary results indicate that organic-base frac-
tions are sorbed preferential to organic-acid fractions using soil as a
sorbent; that s the reverse case to using TOSCO II processed shale as the
sorbent. Omega 9 processing water also acts to extract significant amounts
of natural organic solutes from soil organic matter; thus, an independent
determiantion of DOC fractionations of these extractable solutes must be
made for various soil-water mixtures to correct sorption isotherm data. It
is the opinion of the authors that USE: of analytical-scale fractionation for
assessment of sorption phenomina is more accurate than use of preparative-
scale fractionation because: (1) all sample matrix effects are present when
the sorption isotherm is run using unfractionated processing water; and
(2) analytical-scale DOC fractionation is higher in accuracy than
preparative-scale DOC fractionation.
Organic-Solute Characterization of Oil Shale Processing Waters
Organic-solute characterization of oil shale processing water by DOC
fractionation can range from the use of DOC fraction ratios as diagnostic
indicators to generation of organic-solute fractions on which specific
compound analyses can be performed. DOC fraction ratios are useful to
compare organic-solute class distributions in various oil shale processing
waters, natural surface waters, and natural groundwaters.
Some preliminary compound identification has been performed on
hydrophobic-base, hydrophobic-acid, and hydrophilic-acid fractions prepared
by preparative-scale fractionations of Omega 9 and 150-ton retort processing
waters. By using the techniques of (iC-elemental analysis and GC-UV analy-
sis, pyridine, aniline, quinoline, a d mono-, di-, and trimethyl pyridines
were determined in hydrophobic-base fractions. Fatty acids from C 1 to C 3
were found in hydrophilic-acid fractions; and fatty acids from C 3 to C 8 were
in the hydrophobic acid fraction. Fatty acids greater than C 8 stay on XAD-8
resin even in the ionized state are found in the hydrophobic-neutral frac-
tion.
Performing a preparative-scale DOC fractionation preliminary to gas-
chromatographic or liquid-chromatographic analysis gives the analytical
advantage of having relatively-homogeneous organic solute fractions for
which it is relatively easy to selec:t the correct chromatographic column-
p ’cking. Another significant advantage is knowledge of the quantitative DOC
283
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fractionation and DOC concentrations in each solute fraction. If only
n icrogram quantities of organic solutes are needed in each fraction for GC
or LC analysis, analytical-scale DOC fractionation can also serve as
preparative-scale fractionation.
SUMMARY AND CONCLUSIONS
In the two years since the origination of DOC fractionation analysis,
this analytica1 methodology has been applied to oil shale processing water
characterization in the areas of water quality monitoring of processing
water inputs to surface and groundwaters; interpretation of organic-solute
sorption-phenomena on soil, sediment, and processed shale sorbents; charac-
terization of organic-solute content of oil shale processing water; and
generation of organic-solute fractions for use in biological-effects testing
programs. Because DOC fractionation analysis is new, much additional funda-
mental research to define organic-solute composition of each fraction, and
to understand competitive sorption phenomena in complex solute mixtures is
in progress. DOC fractionation mnalysis has provided much useful
intermediate—level qualitative and quantitative information on organic-
solute composition of oil shale processing waters, and natural surface and
groundwaters which these processing waters may impact.
REFERENCES
Casterline, C.E., and Leenheer, J.A. , 1979, Determination of dissolved
organic carbon in methanol: American Laboratory, v. II, no. 5. In
press.
Farrier, 0.5., oulson, R.E., Skinner, Q.D., and Adams, J.C., 1977, Acquisi-
tion, processing, and storage for environmental research of aqueous
effluents derived from in situ oil shale processing: Proceedings of
the Second Pacific Chemical Engineering Congress, Denver, Colorado, v.
2, p. 1031-5.
Farrier, 0.5., Virgona, J.E., Phillips, I.E., 1978, Environmental research
for in situ oil shale processing: Proceedings of the 11th Annual Oil
Shale Symposium, Colorado School of Mines, Golden, CO, April 12-14; in
press.
Fox, J.P., Farrier, U.S., and Poulson, R.E., 1978, Chemical characterization
and analytical considerations for an in situ oil shale process water:
Laramie Energy Technology Center, Department of Energy, Report of
Investigations, no. 7817, 47 p.
Huffman, E.W.D., Jr. , 1979, Isolation of organic materials from in situ oil
shale retort water using macroreticular resins, ion exchange resins,
and activated carbon: Proceedings of the ASTM Symposium on Measurement
of Organic Pollutants ‘in Water and Wastewater, Denver, CO. June 19-20;
in press.
Jackson, L.P. Poulson, R.E., Spedding, T.J., Phillips, T.E., and Jensen,
FiB., 1975, Characteristics and possible roles of various waters
284
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significant to in situ oil shale procesing Cüiör do School of Miiies
Quarterly, v. 70, p. 105-134.
Leenheer, J.A., and Huffman, E.W.O., Jr., 1976, Classification of organic
solutes in water by using macroreticular resins: Journal Research,
U.S. Geological Survey, v. 4, no. 6, p. 737-751.
Leenheer, J.A., and Huffman, E.W.D., Jr., 1979, Analytical method for
dissolved organic carbon fractionation: U.S. Geological Survey Water
Resources Investiagation No. 79-4.
Long, A., Jr., Merriam, N.W., and Mones, C.G., 1977, Evaluation of Rock
Springs site 9 in situ oil shale retorting experiment: Tenth Oil Shale
Symposium Proceedings, Colorado School of Mines Press, p. 120-135.
Malcolm, R.L., Thurman, E.M., and Aiken, G.R., 1977, The concentration and
fractionation of trace organic solutes from natural and polluted waters
using XAD-8, a methylmethacrylate resin: Proceedings of XI Symposium
on Trace Substances in Environmental Health, University of Missouri,
Columbia, Missouri, p. 307-313.
Stuber, I -LA. , and Leenheer, J.A. , 1978A, Fractionation of organic solutes in
oil shale retort waters for sorption studies on processed shale,
Preprints American Chemical Society, Division Fuel Chemistry, v. 23,
no. 2, p. 1.68.
Stuber, H.A., and Leenheer, J.A. , 1978B, Assessment of a resin-based
fractionation procedure of monitoring organic solutes from oil shale
retorting wastes: Proceedings of Symposium on Establishment of Water
Quality Monitoring Programs, American Water Resources Association, San
Francisco; In press.
Thurman, E.M., Malcolm, R.L., and Aiken, G.R., 1978, Prediction of capacity
factors for aqueous organic solutes adsorbed on a porous acrylic resin:
Analytical Chemistry, v. 50, no. 6, p. 775-779.
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SAMPLING STRATEGIES IN GROUNDWATER TRANSPORT AND FATE STUDIES
FOR IN SITU OIL SHALE RETORTING
Kenneth D. Pimentel
Daniel F l. Stuermer
Environmental Sciences Division
University of California
Lawrence Livermore Laboratory
Livermore, California 94550
Maria M. Moody
Rio Bianco Oil Shale Company
Denver, Colorado 80231
ABSTRACT
This paper proposes a new concept for designing groundwater monitoring
programs to assess the effects of in situ oil shale retorting. The concept
includes new ways to characterize pollution source terms, build and cali—
brate hydrological models, estimate stochastic systems, and optimize
measurement system designs. The solution to the monitoring problem would be
the minimum—cost program that estimates pollutant concentrations throughout
the groundwater region within an acceptable error criterion. That program
would specify the lowest number of wells that need to be drilled, their best
locations and depths, and how seldom they need by sampled. The approach can
be applied to meet differing requirements of characterizing regional hydrol-
ogy, studying geochemistry, determining pollutant transport and fate, and
designing monitoring networks to demonstrate compliance with effluent regu-
lations.
INTRODUCTION
Millions of dollars have been spent on programs to monitor groundwater
in assessing the effects of extracting energy by in situ oil shale retort-
ing. Not all of these programs have yielded useful data for decision makers
and environmental planners. The purpose of this paper is to propose a new
concept in rationally designing groundwater monitoring systems. The concept
combines new techniques available in source term characterization, hydro-
logical modeling, stochastic system estimation, and dynamic systems theory
in a new way to approach the solution of the problem of how to
Work performed under the auspices of the U.S. Department of Energy by the
Lawrence Livermore Laboratory under contract number W-7405-ENG-48.
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monitor groundwater. The solution would be a minimum-cost monitoring
program that yields best estimates of groundwater pollutant concentration
To estimate pollutant concentrations throughout the groundwater region
within an acceptable error criterion, that program would specify the most
advantageous locations and depths of measurement wells, the lowest number of
wells that need to be drilled, how seldom samples need be taken from these
wells, and what types of samples should be obtained.
The need for this new approach seems clear. We will briefly summarize
some of the remarks from related papers given at this symposium that
substantiate the need for our new approach to monitoring groundwater.
Douglas Skie, the Quality Assurance Coordinator for EPA Region VIII,
outlined minimum requirements for oil shale environmental sampling quality
assurance programs at this symposium.1 He subsequently mailed to symposium
participants several EPA documents describing various aspects of regional
implementation plans for quality assurance programs. One of these docu-
ments, entitled Minimal Requirements for a Water Monitoring Quality
Assurance Program,2 outlines a minimum quality assurance program the EPA
regions are requiring states to comply with to receive certain Federal EPA
grants under a 1976 law. The document includes detailed sections on general
water chemistry, water chemistry specific to organics, water microbiology,
field sampling, and data handling.
What is notable about this report is that it contains a wealth of
detailed information on chemistry and data handling but a dearth of informa-
tion on sampling network design. The essence of the only remarks on
assuring the quality of a design for a sampling network is contained in the
following quotation from that report:2
Assurance of representative sampling, both as to site selection
and frequency, requires a sampling network design which provides:
1. A sufficient number of representative sampling loca-
tions.
2. Types of samples.
3. Frequency of sampling.
If properly addressed, these three factors will provide a valid
representation of the characteristics being assessed and should
insure that program objectives are met.
What is not clear at all in these statements is just how the states are to
go about determining the adequacy of the sampling networks to assure the
quality of the data they produce. In particular, Skie mentions nothing
about the design of the groundwater sampling networks. We believe the
vagueness of the one paragraph devoted to those remarks out of that 56-page
document demonstrates that a gap exists in the ability to design rational
quantitative groundwater monitoring programs. We believe that by approach-
ing the monitoring problem as one in stochastic dynamic systems, definitive
statements can be made about the wells—the least numbers needed, their
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location, and how seldom they need to be sampled--to assure the quality of
the data resulting from a monitoring program.
Butch Slawson presented a paper closely related to our paper at this
symposium (Slawson and McMillion 3 ). In it he outlined the methodology
developed by General Electric-TEMPO for designing cost-effective programs
for monitoring groundwater. The methodology includes four basic steps:
(1) identify and characterize potential groundwater pollutant sources,
(2) characterize the location of these sources in their hydrologic frame—
work, (3) assess the mobility of potential pollutants underground, and
(4) develop a priority ranking of potential pollutants. 4 He discussed some
key issues important in applying this methodology to the complex hydro-
geological systems encountered in oil shale developments, i.e., selection of
the sampling site, methods of well construction and sample collection and
frequency of sampling. All these issues bear heavily on the quality of the
data that results from a given monitoring system design. Slawson and
McMillion concluded that the assurance of quality data depends on the plan-
ning and structured designing of programs as much as on the activities more
normally associated with quality control and quality assurance programs. We
aqree with this conclusion. They also identified a need for detailed hydro-
geologic evaluation as an integral part of a monitoring design methodology.
We also agree with this need and believe it further supports our premise
that stochastic, quantitative methods can now be used to solve the problems
of identifying subsurface geohydrologic structure, 5 calibrating and verify-
ing models, 6 and minimizing the cost of monitoring system design. 7
Thomas Sanders presented an unscheduled paper at this symposium. He
called for taking a new view of water quality associated with oil shale
developments. 8 The view was that water quality issues must be regarded as
stochastic in nature. He presented some examples of how to deal with water
quality parameters from a random variable perspective. In a brochure, he
describes an entire week-long course to be held July 1979, at Colorado State
University, devoted to the design of water quality monitoring networks from
a stochastic systems point of view. 9 Surface water will be dealt with in
the course, and this reaffirms our contention that significant research is
still needed in this area for groundwater systems.
APPROACH
The approach we propose to solve the problem of monitoring groundwater
consists of several straightforwaro steps. The first step is to gather
together and analyze existing data for characterizing “source term” rela-
tionships of potential pollutants that could be leached out of oil shale
during and after retorting. The second is to assess the applicability of
existing models of groundwater mass and constituent transport. The third is
to select the best optimal estimation scheme from available methods that
will apply to groundwater problems. The fourth is to incorporate results
from statistical experimental design theory that may apply to solving the
problem of what is the optimal groundwater monitoring system. The fifth
step is to combine all of the above into a unified, rational computer-based
methodology for optimally designing environmental monitoring systems.
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STATE-OF-THE-ART
In this section we will review the available techniques in each of the
“component” research areas we believe can go together. to approach solvinq
the problem of optimal monitoring.
Source Term Characterization
The first step in the design of environmental monitoring techniques is
to determine the natures of the potential pollutant sources before attempt-
ing to assess their possible effects on the environment. Considerable data
exist on the types and quantities of pollutant species that could result
from underground retorting and groundwater leaching of spent shale. The
controversy surrounding the interpretation of these data was one of the main
driving forces behind holding this conference. We are here to learn what we
can about existing source term information so that we can incorporate those
data into our analysis.
Literature data are abundant already from leaching experiments done by
other researchers.’ 0 13 Some of these data indicate that the major constit-
uents in high concentrations in leachate of spent shale include the follow-
i ng:
Na , K , NH 4 , HCO 3 , SO 4 , Cl, F, and Total Dissolved Solids (TDS).
Significant amounts of toxic trace elements were also observed and include:
As, B, Hg, Mo, Ni, Pb, and Se.
Complex mixtures of organic compounds are also of concern.
One aspect of data interpretation necessary to design a rational
monitoring system is to quantify uncertainty in source-term generation
rates. If we extrapolate results from data obtained in experiments to
commercial oil shale ventures, errors or variations will be induced in the
numbers calculated and the numbers actually observed. This concept of error
or uncertainty is central to the development of rational measurement
design. 14
A new method for facilitating well-head sampling was recently described
by Garvis and Stuermer.’ 5 This technique uses a portable instrument package
to continuously monitor pH, oxidation/reduction potential, conductivity, and
temperature during the time a well is flowed for sample collection. When
these parameters stabilize, it is likely that the water being produced from
the well head is representative of the water in the formations at depth;
this is the time samples for detailed chemical analysis should be taken.
This technique relates to some of the needs that arise in sampling oil shale
waters cited above in Slawson and McMillion. 3 This well-head sampling
system has recently been used in coal gasification experiments to study
groundwater transport of organic compounds. Distribution of neutral organic
compounds was estimated for underground gasification experiments in north-
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eastern Wyoming by Stuermer et al.’ 6 Adsorption by the coal seemed to be an
important scavenging mechanism that affected resulting organic distribution
in groundwater. Laboratory experiments have been carried out by Wang 17 to
study certain organics and trace metals by batch and fixed bed methods.
Thus, we have experience from field and laboratory studies in related source
term and leaching experiments for coal gasification that has direct applica-
tion to oil shale studies.
Hydrological Models
Solutions to the equations of groundwater motion evolved considerably
in the past several years. Before digital computers, analytical approaches
to mass transport equations were popular for linear time-invariant descrip—
tions. With the advent of digital computers, differencing schemes were
implemented to approximate solutions in time and space. Finite difference
methods were used widely to study mass transport in confined and unconfined
flow. Recently, finite element methods became very popular in the study of
general nonlinear, time-varying, mathematical descriptions of groundwater
flow.’ 8 Many of the methods in use within the U.S. Geological Survey (USGS)
were recently summarized in a status report by Appel and Bredehoeft.’ 9
There it was indicated that extensions were made to include mass and solute
transport in some of the more recent work. Chemical reactions and adsorp-
tion phenomena were included in some of the implementations. Pimentel 2 °
summarized many special topics in models now available.
Weeks et al. 2 ’ of the USGS used a method of Bredehoeft and Pinder to
analyze baseline and affected hydraulics in the Piceance Basin. The exist-
ing data were used to compute regional gradients for potentiometric surface.
Hypothetical developments at tracts C-a and C-b were incorporated in their
model to demonstrate the effects of dewatering on basin hydrology. The USGS
developed transport models that Saulnier and others are using to calculate
groundwater and mass and solute transport from the prototype lease tracts to
the White River in the Piceance Basin.
Besides that directly in the Piceance Basin, a great deal of work was
done for other groundwater applications. These applications include
analyzing breakthrough times for geothermal reservoirs with production and
reinjection wells; and analyzing pollutant migration associated with in situ
coal gasification, migration of radionuclides in groundwater adjacent to
underground nuclear test cavities, and the study of the fate of nuclear
waste buried in underground repositories. 22 25 Closed form solutions,
though lacking the detail and sophistication of the more powerful techniques
of finite difference and finite element that apply to the general time-
varying non-linear case, can be useful in studying approximate solutions for
the general case. These solutions may yield very useful information about
how groundwater motion occurs over limited ranges of system variables and
parameter values.
Thus, there is a wide range of computer implementations of methods to
solve the equations of groundwater mass and solute transport. On one end of
this range are the methods for finite difference and finite element that car.
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be made arbitrarily detailed in their spatial resolution. On the other end
of the range are the exact solutions that apply to only linear approxima-
tions to real world groundwater problems and that, in general, will lack the
detail of the more sophisticated discretization schemes. The differences in
the ends of this range of methods naturally affect how easy it is to imple-
ment and compute the solutions in actual applications. This will be an
important consideration in selecting the most appropriate hydrological
models for use in designing the optimal monitoring system.
Two criticisms were made of our approach at the symposium. One dealt
with the simplicity of the models that we described and how these simple
models could not adequately simulate complex hydrogeology. We agree that
this is one of the important aspects of needed research in this area and
include it in our future research plans for actual monitoring system appli-
cations. We have discussed similar problems with John Wilson of MIT. 26 He
is interested in basic research in the area of fracture flow for nuclear
waste storage applications; his results would likely translate to oil shale
studies. The other criticism had to do with our approach being “data rich,”
requiring significant hydrology and geology data to support the models used.
At the outset this is true, but the intent of our approach is to eventually
incorporate methods that use available data from existing wells to Identify
models for flow and transport. 27 These models would then be the basis for
determining which other wells were needed to adequately monitor groundwater
in a cost-effective manner.
Optimal Estimation
The problem of optimal estimation was first formally treated in the
early 1960s. To solve this problem, it is necessary to obtain best
estimates of system variables in dynamic processes described by systems of
ordinary differential equations with stochastic (noisy) input terms. In
these processes only stochastic measurements can be made. This theory
evolved and was applied early in the days of our country’s aerospace
projects.
The original framework in which the problem was cast dealt with systems
of equations that described the dynamics of particles in space. These
systems included random wind gusts that acted on masses moving through space
at the edge of our atmosphere. Measurements of the motions of these objects
consisted only of noise-corrupted radar sitings. Thus, the problem in those
terms was to generate best estimates of the positions and velocities of
objects acted upon by random forces from data that had intrinsic noise mixed
with their signals.
The original solution to this estimation problem appeared in 1961 in
the form of Kalman Filter. 28 A wealth of literature followed Kalman’s
initial work that refined his initial results, provided other solutions to
the same problem, and extended the results to apply to general non-linear
time-varying systems. Many of these extensions are reported in Gelb, 2 &
Jazwinski, 3 ° Schweppe, 3 ’ and in IEEE. 32
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The unique feature of optimal estimation schemes that makes them
valuable in designing environmental monitoring systems is their prediction
of both the best estimates of system variables and the errors in those
estimates. In the estimation algorithms are incorporated deterministic
models to compute the mean values of flows, velocities, and concentrations.
Also calculated are the variances and covariances in the variables that may
be used as measures of error in the estimates. These concepts are shown in
Fig. 1. Suppose a groundwater process is acted upon by random forcing terms
“v,’ 1 and further that we are interested in estimating values of the pollut-
ant concentration “p ” at some point in the groundwater region. We cannot.
however, make perfect measurements of the concentration, and the measure-
ments we do make are corrupted by additive measurement noise “w.”
If the estimation algorithms are used, we can generate both the
expected values of the concentration “p” conditioned on all past measurement
data for the process and the variance ac,” or the error in the expected
value. These two variables are shown schematically in Figs. 2a and 2b. The
object of the monitoring problem is to determine the fewest number of
measurements and the best locations and times to make the measurements that
will minimize the total cost of taking measurements while maintaining the
error in the pollutant estimates below some allowable maximum value.
Suppose the process starts at time t 0 . We can allow it to proceed without
making any measurements until the error “C ” first reaches its allowable
maximum g . Suppose the error limit is first reached at time t 1 . Then we
are requfWd to make some key decisions at time t 1 that will result in
making measurements that will minimize the cost of making measurements over
the duration of the measurement program. What seems obvious from the sketch
in Fig. 2b is that the best positions and number of measurements should be
chosen to result in the longest time that the process can drift before
another measurement is required; that is, when the error c next reaches its
limit at time t 2 . This was one of the immediate results in applying
Kalman Fter schemes of optimal estimation to linear time-invariant dif-
fusive transport processes. 33 ’ 34 . We anticipate that more complicated
optimization schemes will result for the general case of non-linear time-
invariant transport phenomena as shown in the postulated algorithm at the
bottom of Fig. 1. Finding ways to minimize the cost of a total measurement
program for this general case is what the proposed research we describe in
this paper is all about.
Extensions of the original concepts of optimal estimation were proposed
and to some extent accomplished in the past several years in other areas of’
systems analysis besides aerospace. Estimation techniques were applied to
chemical process modeling, to general mechanical systems, and recently to
several studies of the environment. Applications in water quality evolved
over the past 10 years. Numerous papers appeared recently in Europe and in
the United States. The International Federation for Information Processing
(IFIP) hosted a working conference in Belgium in the autumn of 1977 that
included several papers dealing with optimal estimation methods applied to
environmental problems. After three days of papers presented at that meet-
ing, the indication was that optimal estimation techniques had come of age
and matured in the area of environmental studies. 35 The American
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RANDOM
SOURCES, v
POLLUTANT CONCENTRATION,p
MEASUREMENT
+ NOISE,w
PHYSICAL
SYSTEM
MIN1MUM-COST MONITORING PROGRAM
(NUMBER, LOCATIONS, DEPTHS, AND TYPES
OF WELLS AND SAMPLING FREQUENCIES)
Our concept for the optimal design of groundwater monitoring
systems includes three major parts: (1) measurements from
the actual physical system corrupted with measurement noise;
(2) computer—based algorithms that yield optimal estimates of
pollutant concentrations and the errOrs in those estimates;
(3) computer-based optimization methods to determine the
minimum—cost monitoring program.
GROUNDWATER
PROCESS
RANDOM
SOURCES, V
PROCESS
MODEL
OPTIMAL
POLLUTANT
ESTI MATE, d
ERROR
IN THE
ESTIMATE, e
ERROR
MODEL
OPTI MAL
ESTIMATION
ALGORITHM
MO1 \HTORING
SYSTEM
OPTIMIZATION
OPTIMAL
DESIGN
ALGORITHM
Figure 1.
293
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_J h-
0<
Q. OC
QH
LU Z
(_ 10
I-
£ O
f- O
C/3
UJ
TIME
MAXIMUM ALLOWABLE ESTIMATION ERROR, e
max
TIME
Figure 2. The pollutant estimate (a) and the error in the estimate ~(b)
are indicative of the concept behind our approach to optimal
monitoring system design. As the optimal pollutant estimate
p is calculated, the error in the estimate e is also calcu-
lated and used to determine the optimal monitoring program.
At the first required sample time tx, we must choose the best
number, locations, depths, and types of samples that will
lead to the minimum-cost monitoring program. The best choice
results in the longest time between
the next required sample.
and t2, the time of
294
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Geophysical Union recently devoted an entire Chapman Conference to the
subject of Kalman Filtering in surface and groundwater hydrology. 36
Included were a wide range of applications of the Kalman Filter technique to
problems in rainfall-runoff prediction, stream and lake surface water
quality, and groundwater hydrology and quality. In the papers on ground-
water applications it was demonstrated that although successful solutions
were available for relatively simple problems, solutions for large-scale.
real-world applications are still lagging. Reasons for this include insuf”
ficient data to adequately support groundwater models, insufficient infor-
mation on mathematical structure for undergound hydraulic processes, and
need for the large dimensionality of the numerical models necessary to
adequately characterize complex flow patterns that may occur in groundwater
systems. However, the overall impression left after the conference was that
the Kalman Filter approach can be a powerful tool in hydrology and, though
not well developed at present in groundwater applications, deserves further
research in this area to address some of the significant energy development
issues that involve our groundwater resources in the future.
Statistical Experimental Design
Experimental design has long been a central tool for the natural and
physical scientist. These methods allow isolating effects of the factors in
an experiment that yield considerably more information about the structure
of response surfaces than is obtained by simply investigating one variable
at a time. 37 Federov 38 summarized many of these techniques. Recent work 39
applied many of these ideas to studies of environmental effects of oil
spills on intertidal ecosystems. It is clear that the concepts emerging
from logical experimental design should affect strategies for minimum-cost
measurement of the effects of in situ oil shale retorting on groundwater.
Key results incorporated in this area will be an important part of our
approach.
Optimal Monitoring System Design
Considerable interest has existed over the past 10 years to find
optimal methods for designing measurements of dynamic processes. Much of
the initial work in this area was generic in nature, applying to mathe-
matical descriptions in the form of systems of first-order, ordinary dif-
ferential equations . 4 ° It quickly became evident that many applications of
these concepts were needed in the environmental area. Some of the early
work centered in the area of optimal siting of air quality sensors. 4 ’ This
is such an important problem that members of the EPA Environmental Moni-
toring and Support Laboratory in Las Vegas hosted a week-long workshop in
July 1976. National experts recommended strategies for monitoring criteria
pollutants in urban and rural airsheds. The approaches discussed in the
working groups were largely heuristic, based on experience of the partici-
pants in air quality work. There was an identified need, however, for more
research in analytical approaches to the problem of optimal design of air
quality monitoring networks. 42
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Much of the early analytical approaches to optimal measurement system
design is summarized by Pimentel. 33 Since 1975, other important work
includes the paper of Mehra, 43 the work of Seinfeld, 44 ’ 45 the continuing
interest in the problem by Brewer, 46 and the thesis by Henwood. 5 ° Mehra
showed results for optimizing measurement sensor designs and sampling
schedules for systems described by models using linear time-varying,
icc 4-. 1 - ,“—+ .. V.... . . . . 44 —- - ,
orul. 1 ai,, .eren , 1 a, u - dPI)rOdLIIeu t iie
problem from the outset as one in distributed processes described by partial
differential equations. They developed approximate solutions to the design
of optimal measurements by using an upper bound to the actual solution of
the error covariance matrix. Recently, Koda and Seinfeld, under contract to
the EPA, approached the analytical design of minimum-cost monitoring net-
works for urban airsheds from a desiqn objective point of view. 45 Brewer is
delving deeply into the mathematics of the optimization of sensor locations.
Recently, one of his students 50 applied Brewer’s results to the optimal
design of witness well networks to monitor the effects of two-dimensional
advective and diffusive transport of a conservative pollutant on an aquifer
being affected by a waste leach field.
HOW THIS APPLIES TO OIL SHALE
We believe there is an obvious need for further research in measurement
strategies for assessing the effects of in situ oil shale retorting on
groundwater resources. It is necessary to fully understand baseline condi-
tions in regional groundwater resources where in situ retorting is slated
for development. During development, dewatering of the region being
prepared for retorting will alter regional hydrology; flow paths will
change, creating areas where flow will be almost at a standstill. During
the retorting itself, combustion products will be carried away with the
process water; its fate is of concern as it relates to groundwater in the
area. After the retorting is complete, the most critical phase of the
development cycle begins. As dewatering ceases and native groundwater
reenters the area that was retorted, if sufficient environmental controls
are not effected, combustion products could leach from the spent shale into
groundwater resources and ultimately affect surface water quality.
Thus, we believe the need is clear to develop quantitative, analytical
approaches to the rational design of groundwater monitoring systems that
will produce the most statistically significant data for the dollar. These
cost-effective measurement designs are needed at a variety of different.
points in the development cycle that have correspondingly different data
requirements. The resulting optimal network designs may also be quite
different to meet these needs. Optimal monitoring networks to characterize
regional groundwater hydrology prior to development may be quite different
than those that better apply to making measurements for studying geo-
chemistry. To establish flow relationships, using a large network of small,
simple wells may be the best way to make simple measurements of piezometric
surfaces; however, for geochemistry and water quality, a sparse network of
larger, more sophisticated wells may be required for sampling affected water
near developed areas. Measurement programs to study transport and fate may
require one type of network to estimate regional hydrology and another to
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monitor groundwater chemistry. Monitoring systems to meet the compliance
requirements of effluent regulations may be of still another best-design
depending on the regulations.
SUMMARY
We described our concept of the optimal groundwater monitoring problem.
Our proposed methods for solving this problem to apply in the area of in
situ oil shale retorting have severa’i straightforward steps: —
o Source Term Characterization - Ongoing experiments with oil shale
processes will make it possible to gather data about combustion
products that develop during oil shale retoring, about the leach-
ing of contaminants from spent shale, and about the sorption of
contaminants onto aquifer materials. The extrapolated data
obtained from simulated in situ retorts operated on the surface
may yield better numbers than data obtained from actual in situ
developments, because there is background noise from groundwater
intrusion and a lack of process control during underground retort-
ing.
o Hydrological Models - There is a great deal of expertise that
exists in numerical simulation of groundwater mass and solute
traisport. Closed-form analytic solutions exist for linear,
time-invariant systems descriptions; sophisticated finite differ-
ence and finite element methods can be used for more general
cases. We believe that simpler analytical solutions will be more
appropriate for applying optimal estimation theory because they
are easier to compute and store; this result was true in our
survey of models for monitoring system design in the geothermal
industry. 20
o Optimal Estimation - State and parameter techniques estimating
stochastic dynamic systems evolved to a high degree of sophistica-
tion over the past several years. We have experience with these
techniques that includes process monitoring and identification in
the nuclear safeguard programs, feasibility studies for use in
sophisticated strategic weapons disarmament applications, and
initial applications in t.he area of optimal monitoring system
design.
o Statistical Experimental Design - Results from experimental design
theory will yield important information to assess designs of an
optimal monitoring system. They are a powerful technique to
estimate statistical parameters including variance and consist
ency.
o Optimal Monitoring System Design - Many approaches were made to
find minimum-cost designs that measure stochastic dynamic systems.
Some of these methods are likely to be powerful techniques for
approaching the problem of synthesizing cost-effective groundwater
monitoring networks fur in situ oil shale deveiop eiit activii,e .
297
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We believe the stage is set for solving real-world problems in this area,
and we have familiarity with the above areas of research that will be
important in solving groundwater and monitoring problems in oil shale.
REFERENCES
1. Skie, 9. M. Field Sampling, Laboratory Analysis and Data Handling QA
Water Regulations on States. In: EPA Oil Shale Symposium: Sampling,
Analysis and Quality Assurance, Westcott, P.A. (ed.). Cincinnati, U.S.
Environmental Protection Agency. in press.
2. Skie, D. M. Denver, U.S. Environmental Protection Agency, Region VIII.
Private communication, 1979.
3. Slawson, G. C., Jr. and L. G. McMillion. Groundwater Quality Sampling
Approaches for Monitoring Oil Shale Development. In: EPA Oil Shale
Symposium: Sampling, Analysis and Quality Assurance, Westcott, P.A.
(ed.). Cincinnati, U.S. Environmental Protection Agency, in press.
4. Todd, D. K., R. M. Tinlin, K. 0. Schmidt and L. G. Everett. Monitoring
Groundwater Quality: Monitoring Methodology. U.S. Environmental
Protection Agency. EPA-600/4-76-026, June 1976.
5. Baecher, G. B. Analyzing Exploration Strategies. Cambridge,
Massachusetts Institute of Technology. Unpublished draft, 1979.
6. Candy, J. V. On-Line Structural Response Analysis: Using the Extended
Kalman Estimator/Identifier. Livermore, Lawrence Livermore Laboratory.
UCID-18175, May 5, 1979.
7. Pimentel, K. D. Groundwater Monitoring Design: In Situ Oil Shale.
Livermore, Lawrence Livermore Laboratory. Department of Energy
Proposal LLL/EV-81-63, April 1979.
8. Sanders, 1. G. and R. C. Ward. Factors to Consider in the Design of a
Water Quality Monitoring Network. In: EPA Oil Shale Symposium:
Sampling, Analysis and Quality Assurance, Westcott, P. A. (ed.).
Cincinnati, U.S. Environmental Protection Agency, in press.
9. Colorado State University Research Institute. Design of Water Quality
Monitoring Networks: A Short Course. Fort Collins. July 23-27, 1979.
10. Parker, H. W., R. M. Bethea, N. Guven, M. N. Gazdar, and J. C. Watts.
Interactions Between Ground Water and In Situ Retorted Oil Shale.
Lubbock, Texas Tech University, 1977.
11. Stollenwerk, K. G. and D. 0. Runnells. Leachability of Arsenic,
Selenium, Molybdenum, Boron, and Fluoride from Retorted Oil Shale.
Boulder, University of Colorado, 1977.
298
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12. Jackson, L. P. , R. E. Paulson, T. J. Spedding, 1. E. Phillips, and H.
B. Jensen. Characteristics and Possible Roles of Various Waters
Significant to In Situ Oil-Shale Processing. Laramie, Laramie Energy
Research Center, 1975.
13. Amy, G. and J. Thomas. Factors That Influence the Leaching of Organic
Material from In Situ Spent Shale. Berkeley, Lawrence Berkeley
Laboratory, 1977.
14. Pimentel, K. 0. Asymptotic Estimation Error Growth Applied to
Monitoring. In: Applications of Kalman Filter to Hydrology,
Hydraulics, and Water Resources, Chiu, C.-L. (ed.). Pittsburgh,
Universit.y of Pittsburgh, 1978. pp. 681-691.
15. Garvis, D. G. and 0. H. Stuermer. A Well-Head Instrument Package for
Multi-Parameter Measurement During Well Water Sampling. Livermore,
Lawrence Livermore Laboratory, in preparation.
16. Stuermer, 0. H. , 0. J. Ng, C. J. Morris, and A. Cotton. Distributicr
of Neutral Organic Compounds in the Ground Water at the Hoe Creek II
Underground Coal Gasification Site, Northeastern Wyoming. Livermore,
Lawrence Livermore Laboratory, in preparation.
17. Wang, F. 1. A Laboratory Study of the Adsorption of Organic and
Inorganic Compounds by Coal. (Presented at 177th National Meeting of
American Chemical Society. Honolulu. April 1-6, 1979.) pp. 507-509.
18. Pinder, C. F. and W. G. Gray. Finite Element Simulation in Surface and
Subsurface Hydrology. New York, Academic Press, 1977.
19. Appel, C. A. and J. D. Bredehoeft. Status of Ground-Water Modeling in
the U.S. Geological Survey. U.S. Geological Survey Circular 737, 1976.
20. Pimentel, K. 0. Survey of Models to Predict the Effect of Geothermal
Power Development on Domestic Water Supplies and to Design Pollution
Monitoring Networks. In: Modeling and Simulation of Land, Air, and
Water Resources Systems, VanSteenkiste, G. C. (ed.) New York, Elsevier
North-Holland, 1978. pp. 651-662.
21. Weeks, J. B., G. H. Leavesley, F. A. Welder, and S. J. Saulnier, Jr.
Simulated Effects of Oil-Shale Development on the Hydrology of Piceance
Basin, Colorado. U.S. Geological Survey Professional Paper 908, 1974.
22. Kasameyer, P. W. , L. Thorson, and C. McKee. Modeling Thermal and Flow
Fronts for Arbitrary Well Arrays. In: Trans. Geothermal Resources
Council, 1. Davis, Geothermal Resources Council, 1977. p. 163.
23. Campbetl, J. H. and H. Washington. Preliminary Laboratory and Modeling
Studies on the Environmental Impact of HIn_SituIl Coal Gasification.
In: Proc. 2nd Ann. Underground Coal Gasification Symp. Morgantown.
August 10-12, 1976.
299
-------
24. Holly, 0. E., N. L. Guinasso, and E. H. Essington. Hydrodynamic
Transport of Radionuclides: One—Dimensional Case with Two-Dimensional
Approximation. Teledyne Isotopes. NVO-1229 --179, 1971.
25. Naymik, 1. G. and G. D. Mendez. User’s Manual for a Material Transport
Code on the Octopus Computer Network. Livermore, Lawrence Livermore
Laboratory. UCID-17986, 1978.
26. Wilson, J. 1. Cambridge, Massachusetts Institute of Technology.
Private communication, 1979.
27. Pimentel, K. 0., J. V. Candy, and D. R. Dunn. Simplified Ground Water
Contaminant Transport Modeling: An Application of Kalman Filter Based
Identification. Livermore, Lawrence Livermore Laboratory, in
preparation.
28. Kalman, R. E. A New Approach to Linear Filtering and Prediction
Problems. Trans. ASME J. Basic Engineering, Series 0. 83:95-108, 1961.
29. Geib, A. (ed.). Applied Optimal Estimation. Cambridge, MIT Press,
1974.
30. Jazwinski, A. H. Stochastic Processes and Filtering Theory. New York.
Academic Press, 1970.
31. Schweppe, F. C. Uncertain Dynamic Systems. Englewood Cliffs,
Prentice-Hall, 1973.
32. IEEE Trans. Automatic Control. AC-16(6), December 1971.
33. Pimentel, K. D. Toward a Mathematical Theory of Environmental
Monitoring: The Infrequent Sampling Problem. Ph.D. Thesis, Davis,
University of California, 1975.
34. Pimentel, K. D. The Environmental Monitoring Problem: Optimal
Solutions for Control and Surveillance Applications in the Case of
Infrequent Sampling. In: Modeling and Simulation of Land, Air, and
Water Resources Systems, VanSteenkiste, G. C. (ed.). New York,
Elsevier North-Holland, 1978. pp. 89-99.
35. VanSteenkiste, G. C. (ed.). Modeling and Simulation of Land, Air, and
Water Resources Systems. (Proc. IFIP Working Conference held in Ghent,
Belgium August 30-September 2, 1977.) New York, Elsevier North-
Holland, 1978.
36. Chiu, C.-L. (ed.). Applications of Kalman Filter to Hydrology,
Hydraulics, and Water Resources. (Proc. AGU Chapman Conference held at
University of Pittsburgh May 22-24, 1978.) Pittsburgh, University of
Pittsburgh, 1978.
300
-------
37. E. I. duPont de Nemours & Co. , Inc. Strategy of Experimentation.
Wilmington, 1975.
38. Federov, V. V. Theory of Optimal Experiments. New York, Academic
Press, 1972.
39. Moore, S. F. and D. B. McLaughlin. Design of Field Experiments to
Determine the Ecological Effects of Petroleum in Intertidal Ecosystems.
Lafayette, Resource Management Associates. RMA 6200, 1978.
40. Meier, L. , III. Optimal Control of Measurement Subsystems. IEEE
Trans. Automatic Control. AC-12(5):528, 1967.
41. Seinfeld, 3. H. Optimal Location of Pollutant Monitoring Stations in
an Air Shed. Atmospheric Environment, 6:847, 1972.
42. U.S. Environmental Protection Agency, Environmental Monitoring and
Support Laboratory, Las Vegas. Report of the Air Monitoring Siting
Workshop, July 1976.
43. Mehra, R. K. Optimization of Measurement Schedules and Sensor Designs
for Linear Dynamic Systems. IEEE Trans. Automatic Control,
AC-21(1):55, 1976.
44. Kumar, S. and 3. H. Seinfeld. Optimal Location of Measurements for
Distributed Parameter Estimation. In: Proc. Joint Auto. Contr. Conf.
San Francisco, 1977.
45. Koda, M. and 3. H. Seinfeld. Air Monitoring by Objective. U.S.
Environmental Protection Agency. EPA-600!4-78-O36, 1978.
46. Brewer, 3. W. The Gradient wit,h Respect to a Symmetric Matrix. IEEE
Trans. Automatic Control. AC-22:265, 1977.
47. Brewer, J. W. The Derivative of the Exponential Matrix with Respect to
a Matrix. IEEE Trans. Automatic Control. AC—22:656, 1977.
48. Brewer, u. W. The Derivative of the Riccati Matrix with Respect to a
Matrix. IEEE Trans. Automatic Control. AC-22:980, 1977.
49. Brewer, 3. W. Analytical Gradients for Optimal Environmental
Monitoring Studies. In: Proc. International Conf. Cybern. and
Society, Washington, D.C. , September 19-21, 1977.
50. Henwood, M. I. A Numerical Method for Environmental Monitoring. M.S.
Thesis, Davis, University of California, 1978.
NOTICE
“This report was prepared as an account of work sponsored by the United
States Government. Neither the United States nor the United States Depart-
-------
ment of Energy, nor any of their employees, nor any of their contractors.
subcontractors, or their employees, makes any warranty, express or implied
or assumes any legal liability or responsibility for the accuracy, complete—
ness or usefulness of any information, apparatus, product or process
disclosed, or represents that its use would not infringe privately-owned
rights.”
Reference to a company or product name does not imply approval or
recommendation of the product by the University of California or the U.S.
Department of Energy to the exclusion of others that may be suitable.
302
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THE DETERMINATION OF FLUORINE IN OIL SHALE RELATED MATRICES USING
GRAPHITE FURNACE MOLECULAR ABSORPTION
Robert Meglen and Alexandra Krikos
The Environmental Trace substances Research Program
University of Colorado
Boulder, Colorado 80309
The quantitative determination cf fluoride in complex sample matrices
is difficult because most detection techniques are adversely affected by the
presence of interfering species. The widely used ion selective electrode
method is not sufficiently reliable and rapid to permit detection of fluo-
ride in geological matrices without prior separation of the analyte. lEn
this paper we describe the adaptation of a rapid separation technique whict
may be used on complex samples in conjunction with the detection by ion
selective electrode or absorption spectrophotometry. The second part of
this paper describes the adaptation of electrothermally heated graphite
furnace analyzer for the detection of fluoride using an atomic absorption
spectrophotometer.
Steam distillation of hydrogen fluoride or hydrogen hexafluorosilicate
from acid solution has been widely used, but it is tedious and time consum-
ing. Taves’ has described a rapid fluoride separation which is both quanti-
tative and easy to adapt to a variety of sample matrices. In this method
hexamethyldisiloxane (HMDS) is used to accelerate the diffusion of fluoride
by formation of trimethyl fluorosilane (TMFS) which is volatile at room
temperature. The TMFS releases its fluoride in aqueous alkaline solution.
this method provides a complete separation of fluoride from severe inter-
ferences and provides a quantitative transfer of the analyte to a simple
interference-free solution. The method also permits preconcentration of
dilute samples.
EXPERIMENTAL
One-tenth gram geological solids ground to less than 200 mesh was mixed
with 1.0 gram of sodium carbonate-potassium carbonate (1:1 by weight) fusion
flux in 25 ml platinum crucibles. The mixtures were fused at 1000°C for 0.5
hours. After cooling 10 ml of 1.0 M hydrochloric acid was added to the
fusion mixture to initiate dissolution. The contents were then transferred
to a 100 ml plastic specimen cup. A second 10 ml portion of 1.0 M acid was
added to the crucible, stirred, and transferred to the plastic cup. A third
rinse of 1.0 ml concentrated hydrochloric acid and 4.5 ml of deionized water
was sufficient to complete the dissolution and transfer the fusion residue.
The sides of the crucible were scraped with a rubber spatula and the
contents transferred to the plastic cup with a 4.5 ml deionized water. Som2
303
-------
resistant fusiGns required a fourth rinse consisting of 10 ml of 1.0 i
hydrochloric acid. The final soluticn volume was brought to 50.0 ml using
1.0 M acid.
The fluoride separation was performed in 100 ml specimen cups which
have air tight closures. Ten m l aliquots of aqueous standards and acidifi&!
samples were pipetted into the specimen cups. A 5 ml nl st.ic microhe ker
containing 1.0 ml of 0.1 M sodium carbonate solution was floated on the
sample solution. A 0.5 ml aliquot of diffusion reagent (6 M hydrochloric
acid saturated with hexamethyldisiloxane, Eastman Kodak Co. , Rochester NY)
was carefully added to the sample avoiding contact with the carbonate solu-
tion in the microbeaker. The diffusion vessel (plastic cup) was immediately
sealed and the diffusion process was Gilowed to proceed for approximately 20
hours. After that period the microbeaker into which the fluoride had dif-
fused was removed and the contents transferred to a plastic container. The
solution was then brought to 10.0 ml for subsequent fluoride analysis. At
this point any detection technique may be used. Thirty to fifty samples may
be conveniently diffused using this method. A schematic diagram of the
diffusion apparatus is shown in Figure 1.
T
6 cm
I
Figure 1. Schematic Diagram Showing 100 mL
Diffusion Vessel with 5 mL Microbeaker.
Diffusion Vessel — polyethylene
304
-------
Tsunoda et al. 2 have described a fluoride detection technique which
makes use of the formation of gaseous molecular aluminum monofluoride in a
graphite furnace. In this technique, A1F is detected and quantified using a
conventional atomic absorption spectrophotometer equipped with a continuum
U.V. source such as a hydrogen hollow cathode or deuterium arc lamp. We
have successfully used this technique on aqueous samples with low concentra-
tions of dissolved species. However, geological fusion digest tes and
aqueous samples containing high salt. concentrations lead to severe inter-
ferences. We have adapted the molecular absorption technique for the
analysis of complex matrices by first performing the separation described
above.
All molecular absorption measurements were performed using a Perkin—
Elmer Model 5000 atomic absorption spectrophotometer. The absorption
intensities of standards and samples were made using the deuterium back-
ground corrector continuum lamp as the sole light source. A wavelength of
227.3 nm and slit width of 0.2 nm ba id pass was used for all measurements.
Peak height measurement with a 10 second window was used to quantify the
transient absorption signals. No scale expansion was used. The digital
signals were recordea with a PRS-10 digital printer. A Perkin-Elmer
graphite furnace (HGA-2100) was used for the formation of the molecular A1F.
Sample injections were made using a Perkin-Elmer Model AS-i atomatic sam-
pler. Twenty p1 injections of a 1:1 solution of sample plus reactant solu-
tion were used for all determinations. The reactant solution was prepared
from the nitrate salts of Al (.01 M), Ni (.005 M) and Sr (.005 M). The
injected solutions were dried at 105°C for 40 seconds. Charring occurred
over 30 seconds using a logarithmic temperature program between the final
drying temperature and a maximum char temperature of 500°C. Atomization
(volatization and formation of gaseous A1F) was effected at 2250°C for 7
seconds. A continuous Ar flow at 25-30 ml per minute was used to purge the
furnace. Fluoride standards between 0 and 3.0 pg/im carried though the
diffusion separation were used for preparation of the standard working
curve. Severe curvature above 5.0 ppm limits the useful detection range of
this method.
RESULTS
The detection of fluoride as aluminum monofluoride by molecular absorp-
tion in the graphite furnace is adversely affected by the presence of high
concentration of most anions and cations. Figure 2 shows the calibration
curve of aqueous standards in the absence of any interfering species.
Figure 3 shows the percent recovery of 2.0 p g/ml fluoride when a variety of
ions are present at 1000 pg/mi. The extent of signal suppression at these
concentrations shows that prior separation of analyte from interferent is
necessary in order to ensure accurate analysis. Figure 3 shows the recovery
of 2.0 p g/ml fluoride in the presence of the same interferents after
separation by the gaseous diffusion procedure. This procedure yields better
than 90% recovery of fluoride for all interfering species except for
aluminum and silicate. Figure 4 shows the concentration dependence of
fluoride recovery for these two species. Silicate does not significantly
interfere below concentration of 500 pg/mi. Quantitative recovery of
305
-------
TABLE 1. INTERFERENT CONCENTRATIONS IN REPRESENTATIVE SAMPLES
Sample Type
Al
pg/mi
Si
Retort-- Process Waters
#A
<2
‘
13.4
#B
<2
11.4
#G
<2
31.4
#0
14.8
10.6
#P
<2
3.5
Plant Tissue
62.4
37.3
Shale
67.5
57.5
Soil
106 ±
30
84.6
Sediment
80 ±
30
178.0
Figure 2. Representative Calibration Curve for Graphite Furnace
Detection of Fluoride as Aluminum Monofluoride.
2.0
Fluoride pg /mL
306
-------
No
K
Ca
Mi
Sr
Cr
Mn
Fe
Ni
Co
Bi
La
Cu
z^
Cd
Pb
i- 60,
f SQ3
(T N'
UJ p
K M
u As
z Se,
SiFc
SiO
A!
0 20 40 6O 8O 100
% reco*.
NO seporofian
20 40 60 80 100
% recov
WITH separation
Figure 3. Left: Fluoride recovery obtained by detection of aluminum
monofluoride in the presence of selected interferents.
Right: Fluoride recovery after gaseous diffusion separa-
tion.
100 200 300 4OO 5OO 600 TOO
INTERFERENT CONCENTRATION fi<}/mL
800 900 1000
Figure 4. Interferent Concentration Dependence of Fluoride
Recovery after Gaseous Diffusion Separation.
-------
fluoride was obtained between 0 and 5 pg/mi. The effect of aluminum is more
severe, therefore the concentration of aluminum must be less than 250 pg/mi
for quantitative separation of fluoride. These restrictions have not
severely affected the application of t.his method to the matrices encountered
in our work. Table 1 shows representative aluminum and silicon concentra-
tions in prepared aqueous solutions • f a variety of sample types to which
this method has been applied. In all cases, the prepared solutions have
silicon and aluminum concentrations below the threshold of significant
interference.
In order to estimate the accuracy of this procedure, it is necessary to
have standard reference geological samples for which certified fluoride
analyses are available. Until these become available we have had to rely
upon the samples for which there are published analytical results available.
Table 2 shows a comparison of the results obtained using the method describ-
ed here with published literature values obtained by other methods. The
present method has also been applied to plant materials for which the only
available, uncertified SRM value has been provided. (NBS Orchard leaves SRM
1571 information only value: 4 pgF/g. This method: 3.8 pgF/g). Other
estimates of the accuracy of this method have been performed on surface,
ground and drinking waters analyzed by ion selective electrode and
colorimetry. Agreement among the an.ilyses was within 10% on more than 50
samples which had concentrations between 0.5 and 6.0 pgF/ml.
TABLE 2. RESULTS OBTAINED ON USGS SOILS
Sample
This Work Lit. Val.*
pg/g
GxR-1
1280.0
1180.0
± 190.0
GxR-2
435.0
450.0
± 180.0
GxR-3
87000.0
79400.0
± 14600.0
GxR-4
2930.0
2800.0
± 490.0
GxR-5
290.0
286.0
± 116.0
GxR-6
290.0
304.0
± 131.0
* D.M. Hopkins, J. Res. U.S. Geol. Survey, 5, 589 (1977).
We have adopted a simple but effective quality control scheme for
detection of systematic errors in routine analyses performed by the tech-
nique described here. This two-sample control procedure is based upon the
Youden technique developed for interlaboratory tests. 3 The modification
used in our laboratory was adapted from the method described by King. 4 We
have prepared a composite sample of each sample matrix (plant material,
geological, water, etc.). Two aliquots of the appropriate quality control
sample labled UA and are analyzed with each set of unknowns. The sum
of the apparent concentrations are plotted after each set of samples have
been analyzed. (The concentration is plotted as the ordinate and the
308
-------
5.0
4.0
A+B ; ; • ;! - i!
3.0
2D -
10
A—B 0 — . —
-1.0 -
RUN NUMBER (TIME)
Figure 5. Quality control charts used for detection of systematic and
random errors in fluoride method. Abscissa is p gF/mL in
the prepared sample solution.
abscissa is the time order number of the sample set, or analytical number.)
By continuously monitoring the plotted results it is possible to detect the
onset of systematic errors in the method. The differences between the
apparent concentration of aliquot “A” and “B” are also depicted in a time
ordered plot. This plot is used to detect random errors in the procedure.
Outliers in the sum plot (A+B) are determined using a control limit of ± 2
standard deviations. Similar rejection criteria are used for the rejection
of results obtained from analytical runs where the (A-B) plot indicates
significant random errors. Figure 5 shows representative results obtained
for the aqueous digest of a fused oil shale sample. The relative standard
deviation obtained for 25 separate determinations is 4.5%.
CONCLUSIONS
The detection of fluoride by molecular absorption in a graphite furnace
can be used successfully on aqueous samples without prior separation when
other dissolved species are present at concentrations below 100 pg/mi.
Separation by gaseous diffusion should be used for more complex matrices.
The detection method described is most useful when coupled with automated
sampling techniques. Automated injection of sample into the graphite fur-
nace improves precision and permits large numbers of samples to be analyzed
with a minimum of operator attention.
The gaseous diffusion separation described here and by Taves is applic-
able to a variety of sample types. The quantitative separation of fluoride
from potential jnterferents makes this method particularly useful for
preparation of samples prior to other interference-prone detection tech-
niques such as the ion selective electrode and the alizarin complexone
spectrophotometric techniques. The techniques described in this paper are
currently being applied to oil shale related matrices.
-------
REFERENCES
1. Taves, Donald R. Separation of Fluoride by Rapid Diffusion Using
Hexamethyldisiloxane. Talanta, 15:969-974 (1968).
2. Tsunoda, K., K. Fujiwaru and K. Fuwa. Subnanogram Fluorine Determina-
tion by Aluminum Monofluoride Molecular Absorption. Anal. Chem.
49:2035-2039 (1977).
3. Youden, W.J. Statistical Techniques for Collaborative Tests. AOAC
(Washington, DC) 1973.
4. King, Donald E. Detection of Systematic Error in Routine Trace Analy-
sis. In Accuracy In Trace Analysis, Philip 0. LaFluer (ed.) NBS
Special Publication 422 (1976) pp. 141-150.
310
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SAMPLE SIZE REQUIRED FOR ANALYSIS OF OIL SHALE
OF WIDELY VARYING GRADE AND PARTICLE SIZE
James F. Carley
Oil Shale Project
Lawrence Livermore Laboratory
P.O. Eox 808
Livermore, California 94550
ABSTRACT
Almost all the interesting properties of oil shale vary with its kero-
gen content, which usually shows a wide range of variation in any lot of
mine-run shale, sometimes even in a single large block. Even when approved
procedures of sampling and subdivision are foliwed, misleading analytical
results may be obtained if the sample size is inadequate for the range of
properties in the lot and the particle sizes involved.
A statistical rationale is developed in this paper that leads to an
equation and graphical procedure for finding the minimum representative size
of a sample of oil shale. This is applied to several types of analyses
commonly done on oil shale. We then look into the case of a retorting
experiment in which the oil yield exceeded the Fischer assay.
INTRODUCTION
In a recent report on quality assurance in Federal environmental moni-
toring’, ES&T Managing Editor Stan Miller showed how the error variance of
any monitoring measurement is the sum of the variances of five major sources
of error. This paper is concerned wit.h two of these, the sampling error and
the error of the measurement method, as they are involved in assessing
chemical and physical characteristics of oil shale rubbles. The special
character of mine-run or crushed oil shale, rather different from that of
most other minerals, has an important influence on the size of the sample
needed to keep sampling error under control.
Minerals are usually prepared for extraction by one or more processes
of size reduction, and oil shale is no exception. In many metal ores the
metal containirg crystals are dispersed in a highly erratic manner amid the
gangue. When such ores are crushed, the differing mechanical properties of
gangue and metal-bearing minerals, together with the usual high preponder-
ance of gangue, result in particles that are either mostly ore or are
essentially all gangue. It is because of this mechanical separation accom-
panying crushing that floatation methods work so well in ore recovery.
-------
Oil shale, however, is rather differently constituted. Built up at a
rate of about 1 millimeter of shale per century, the laminar sedimentation,
the inorganic and organic components of what native Americans used to call
“the stone that burns” are so intimately bound together that even very small
particles can contain some of both components. Still, the percentage of
kerogen in Green River oil shale can range, within a few feet of depth in
the deposit, from 0 to 30 or higher. Oil shale people like to think in
terms of the Fischer assay of the shale, called its “grade,” which measures
the amount of oil that can be extracted from the shale, in gallons per ton
(or liters per Mg). The range for this property is from 0 to about 75
gal/ton (0 to 313 1/Mg), though most of the Green River shale assays at less
than 30 gal/ton.
If one picks out a single particle of crushed metal ore, there is a
good likelihood that the particle will either contain a high percentage of
the metal sought or none at all. Fob’ this reason, the rationales proposed
for setting the sizes of ore samples have been based on the binomial proba-
bility distribution. With this model, in a single trial, one either
succeeds or fails. Once the true probability of success is known, or
accurately estimated, the distributions of successes and failures in large,
randomly chosen samples can be calculated. One can also calculate the
standard deviat.ions of the success rates in samples of various sizes, so it
is fairly straightforward to set up the size of a random sample needed to
make the sampling error as small as one desires. Procedures based on such
thinking have been widely used for many years; they are well described in
the literature 2 and have more recently been applied to chemical analyses. 3
A particle of crushed oil shale, though, is unlikely to be either all
kerogen or completely free of kerogen; rather it will have some fractional
kerogen content more or less close to the true mean content of the lot from
which it comes. Some years ago, a 2-inch diameter coring was made through
the “Mahogany Zone” (so-called because of its color) near Rifle, Colorado.
One hundred fourteen consecutive, 1-foot deep sections of this core, weigh-
ing about 3 pounds apiece, were each ground to pass an 8-mesh screen, then
subjected to Fischer assay. 4 These samples had grades ranging from 4 to 77
gal/ton (17 to 321 1/Mg). The mean grade was 23.4 and the standard devia-
tion was 14.9 gal/ton*. The picture was very similar for 86 consecutive
specimens taken from a second core drilled in another county of Colorado.
Because each core was targeted on the Mahogany Zone, neither sample can be
considered wholly random, though hundreds of other cores from the Green
River Basin show that these two are ftirly typical.
Another property measured on the powders made from these cores 4 was the
specific gravity (relative to water at 60°F) and, as expected, the grade of
the shale decreases steadily, though not linearly, as the specific gravity
increases. For specific gravity, the mean of the 114 samples was 2.266
while the standard deviation was 0.220*.
* These values were not given by Smith 4 but were calculated by me from point
coordinates read off Smith’s Figure 1.
312
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CHOOSING A SAMPLE SIZE
Suppose now that we must sample some broken shale and estimate the
value of some property like grade or density. The problems connected with
actually collecting the sample are dealt with in handbooks’ and in the ASTM
procedure for sampling coal, 5 and I shall not dewll on the mechanics of
assuring randomness but shall assume that, whatever the size, randomness can
be achieved. “Randomness” means, simply, that each of the particles of the
lot has the same chance of being chosen for the sample. It should be appar-
ent that if only one particle were taken, its grade might be anything
between 5 and 70 gal/ton. However, f our sample consisted of 10 randomly
chosen particles of about the same size, the mean grade of the 10 would have
a standard error of GI.Jl0, where a the standard deviation of grade in the
lot. If we selected n particles, the sampling error of the mean grade (or
property of interest) would be cr/.Jn. However, this is only the sampling
error. Even ‘if we could reduce this error component to zero, the error of
measurement would still be present. Actually, since the two are indepen-
dent, their squares, the sampling and measurement variances, are additive.
The variance of the estimated mean is given by--
+ cr ,/r = a 2 In + u /r (1)
where o = the sampling variance;
a 2 = tt ’ie variance of the measurement method (one determination);
r the number of determinations made.
Equation 1 is valid no matter what the form of the distribution of the
property of interest in the lot. Since the standard error of the estimated
mean a- cannot be smaller than the larger of its two constituent errors and
will n t be reduced appreciably by making the smaller one still smaller. It
is shown in the Appendix that, if the costs of sampling and measurement are
known, there is an optimal choice of r and n that minimizes the cost of
obtaining any desired a-. In the absence of such cost data, a reasonable
rule of thumb is to le the two variance components be equal. The final
combined error of the estimate will then be Gm times / 2/r, which will
usually be acceptable. If that equality is solved for sample size we get--
n r(cj/a) 2 (2)
An interesting property on which to try this equation is density. The
standard deviation for single determinations of density on finely ground
shale by helium pycnometry is about 0.005 g/cm 3 . If the practice were to
determine density for three samples of the powder, then r would be 3.
Taking the value of a equal to that. calculated from Smith’s data, 0.220
g/cm 3 and applying Eq. 2, we find the sample size to be n 3(0.220/0.005)2
5800 particles.
Now, we don’t really want to count out these particles, especially when
they are very small. To what mass of particles in a given narrow size class
313
-------
does this number correspond? Particle mass in grams is given by the equa-
ti on
M = f d 3 p for a single particle and by (3a)
M = n f d 3 p for n particles (3b)
in which d = the “size” of the particle, cm
p = the density of the material, g/cm 3 ;
f = the volume shape factor.
It is generally accepted that screening measures the second largest princi-
pal dimension; this is the dimension we call “size.” The shape factor f
would be unity if the particles wer cubes or /6 if they were spheres, but
for our shale particles, which vary from near-cubes to thin flakes, f
exhibits considerable variation. For 10 particles taken from our “master-
batch” material, which passes a 1-inch (2.54 cm) screen but remains on a
half-inch (1.27 cm) screen, d ranged from 1.250 cm to 2.620 cm, averaging
1.890 cm, while f, calculated from Equation 3a, ranged from 0.148 to 0.397,
averaging 0.284. For particles like these, Eq. 3a gives M = 0.284 x 1.890
x 2.209 = 4.24 g. ihis compares rather well with the measured average mass
of 4.68 g, considering the small size of the sample and the large variations
in d and f. The “masterbatch” was created by crushing, thoroughly blending
and screening larger material from a 1 ] parts of our shale pile and may be
expected to contain the whole range of density of our pile of Anvil Points
shale. For these particles, then, the total mass of a random sample would
be II = 5800 x 4.24 = 24,600 g.
In dealing with a specific size fraction, specified by the openings of
screens retaining and passing all of the sample, there is a question as to
what average size should be used to represent the fraction. If the two
screen openings defining the fraction differ by no more than a factor of 2,
the arithmetic average opening may be used for the mean size with an error
in (d) 3 of less than 10 percent (low). That is
d 0.5 (d 1 + d 2 ) (4)
By combining equations 2, 3b and the concept of mean size, we get the
final equation for the sample mass in grams.
M f a r (a/o) 2 (d) 3 (5)
For density estimation, and assigning conservatively high values of 0.4
to f and 2.5 to and assuming r = 2, Eq. 5 reduces to
M = 3870 (d) 3 (6)
This equation is plotted in Figure 1 with some familiar screen fractions
indicated.
For some very precise determinations, for which a is very small,
setting the saripling variance cr 2 /n equal to the measuren nt variance
314
-------
PARTICLE
5 10
SIZE, cm
20 50 100
I03
10'
SAMPLE
: MASS, g
10
10"
10"
i i r i m r
r
2-4 IN
100-140 MESH 30-40 M
t t i i i * i i i I
6-12 M
SAMPLE
MASS, kg
0.5-1 IN
_,J|
10'
10
0.0i 0.02 0.05 O.i
PARTICLE
0.2 0.5
SIZE, cm
1.0 2
Figure 1. Plot, showing dependence of sample size required on size of
particles for measurement of density in oil shale by helium
pycnometry (two determinations).
315
-------
aay provide an error of estimate, a-, that is much smaller than is necessary
for the use that is to be made d f the estimated mean. One can instead
choose a larger a- that will effectively be equal to a/Jn. Then the sample
size n would be iven by n = (a/a-) 2 and, since repeating the measurement
would not improve precision, r wou’d be 1. This variance ratio, and r = 1,
can be substituted for (a/a ) 2 and r in Eq. 5 and its derivatives.
I chose density as the illustrative property in this development not
only because it is a property of interest but because many chemical and
physical properties of oil shale are directly or inversely related to den-
sity. Grade, kerogen content, organic:-carbon content and heat capacity, for
example, are inversely related while mineral content and strength properties
are directly related. However, some properties vary more than density or
may be measured with more or less precision than density and, as Eq. 5 tells
us, these characteristics are strong determinants of sample size. What
minimum size sample should be taken for the determination of grade, the
property of greatest interest?
From the results of a roundrobin test program undertaken a couple of
years ago, 6 I calculated the measurement error from duplicate determinations
for the nine laboratories using the USBM method to be 0.371 gal/ton (with
degrees of freedom v = 216). With the value of 14.9 gal/ton calculated from
Smith’s data 4 for a and the same values for f, r and a as for density, Eq. 5
condenses to
M = 3220 (d) 3 (7)
Organic carbon content of shale is proportional to grade 7 and is given
approximately by the equation.
OC 0.467 G (8)
Where OC is in weight percent of raw shale weight and G in gal/ton. For the
region sampled by Smith, then, a for organic carbon would be 0.467 x 14.9
6.96 wt X. From many sets of organic-carbon analyses made on samples of oil
shale in our Chemistry Department, I have estimated a for this determina-
tion to be O.063%C (u)50. Again applying Eq. 5 with Lhe same values of f,
r and p we get
P4 = 24,400 ( )3 (9)
The much larger sample size requireo here is mostly a consequence of the
greater precision of the organic-carbon determination and our initial
decision to equalize the error contributions of sampling and analysis. If
an estimation error comparable, percentagewise, to that for density or grade
is acceptable for organic carbon, then the sample size required for density
will suffice for organic carbon, too.
Equation 5 differs from sample-sizing methods offered elsewhere mainly
in its dependence on the third power of the particle size rather than the
second (or lower) power, and in the introduction of the standard deviations
316
-------
of the continuously distributed property being estimated and the method of
measurement. Except for relatively large particles, about 10 cm and larger,
Eq. 5 tends to give sample sizes smaller than those prescribed in references
2 and 3. Figure 2 is a concurrency chart that solves Eq. 5, taking fp 1,
from inputs d, r and a/am.
PARTICLES WITH A WIDE RANGE OF SIZES
The foregoing treatment has dealt with particles of a single size or,
at least, with a narrow size range. Mixing and crushing operations always
yield particles with size distributions spanning one or more orders of
magnitude, and it may not be practical (or even advisable). 8
Consider how sampling-error variance is made up when there are only two
distinct sizes. In general the property to be measured will either consume
the whole sample, or it will be subjected to further size reduction and
splitting. For simplicity, consider the first case. The sample will con-
sist of a mass fraction, m 2 , of the smaller size and remainder, m 2 , of the
larger size. Suppose the average value of the property of interest, say
grade, in each of the size fractions is G 1 for the smaller-size material, G 2
for the larger. The average for the entire batch will be G m 1 G 1 + m 2 G 2 .
By Eq. 1, the iariance of G 1 will be a 2 /n while that of G 2 will be a 2 /n 2 .
We assume that the mass fractions in the sample can be measured with errors
that are neoligible in comparison with the variations in the G , a very
sound assumption with modern weighing equipment of industrial grade or
finer. Since the grades of all the particles are independent of each other,
we can apply the principle of additivity of variance again, just as we did
in Eq. 1. The result is
a 2 m a 2 + m 2 = cr 2 [ (ni In 1 ) = (m /n 2 )] (10)
1 2
By Eq. 3b, n. M /fpd m M/fpd . Substituting into Eq. 10, we get
( 2 f /M){m 1 d + m 2 d ] (11)
By the same line of reasoning, the sampling variance in G when there are k
discrete size classes will be
k
a 2 = (a 2 fp/M) m d (12)
i=1
Since density varies along with other properties of oil shale, we could make
this equation a little more precise by subscripting density and summing the
product .m.s . However, we are merely trying to arrive at a satisfactory
sample skz 1 so a single highest value of density, whose percentagewise
variation is rather small anyway, will give a sample size only a little
larger than the necessary minimum.
317
-------
d, MEAN SIZE, cm
icr1 i io
10* ic
M, MINIMUM SAMPLE MASS, g
Figure 2. Concurrency diagram that solves Equation 5, giving sample
size required for tests on oil shale particles. Enter at
top with particle size or mean size for fraction or dis-
tribution, drop down to r, number of replicate measurements
to be wade of property, then move horizontally to appro-
priate value of 0/0 , then downward again to read mass of
sample. Example (dffshed lines) for d = 0.36 cm, r = 2 and
o/o = 38 gives M = 135 grams.
m
318
-------
If particle size is continuously variable with a mass-based probability
density m(d), the analogous equation to Eq. 12 is
d
= (a 2 fp/M) .fdmax d 3 m(d) dd (13)
mi n
We have found that size distributions for crushed oil shale are approx-
imately lognormal, as is the only well-documented size distribution for mine
run shale that 1 know of. 9 This property is easliy tested by plotting (when
such data are available) the cumulative weight percent passing a series of
graded screens versus the screen size on lognormal probability graph paper.
If the distribution is lognormal the plot will be a straight line. If rn(d)
in Eq. 13 is the lognormal density the integral of Eq. 13 can be evaluated
with the aid of tables of the cumulative normal distribution. On the other
hand, if the screening data is available, we can work with Eq. 12, letting
d 1 for each fraction equal the arithmetic mean of its two screen sizes.
Let us now define the mean size for a distributed sample as that single
size which, taking the sample as a wnole, makes the sampling variance a =
a 2 /n. By Eq. 3b, n M/fp(d) 3 , where d is the required average diamet r.
Thus, a (cr 2 fp/M)(d) 3 . Comparing this with Eq. 12, it is clear that the
required’ average diameter must be
d (1m d )h/’3 (14)
It is worth reflecting on the structure of this average for a moment. In
crushed shale, the larger size particles tend to be present in larger
amounts, so the average diameter for sampling-variance purposes will tend to
be close to the high end of the size range. If that range is considerable,
say 10 to 1 or more, the smaller sizes will have almost no influence on_the
average size. If no screening data is available from which to compute d, a
safe size is the largest size visible in the mass being sampled.
Let’s recap the sample-sizing procedure. First, one uses the known or
estimated standard deviation of the property to be tested, together with the
measurement error and the number of repeat determinations to set the number
of particles needed. Then, using the appropriate mean particle size, Eq. 4
for narrow size fractions, Eq. 14 for multiple discrete sizes or wider size
distribution, and taking the fp product = 1, the required mass of sample is
found from Eqs. 3 (or 5), or Figure 2.
A CASE OF TOO-PLENTEOUS YIELD
The core of our oil shale program at LLL is the experimental simulation
of modified ir situ retorting with two specially constructed aboveground
retorts. Each of these retorts is equipped with an elaborate, multistage
collection system that recovers all the oil that leaves the retort as
liquid, mist and vapor. For each run, the yield, expressed as a percentage
of Fischer assay, is found from the ec uation
319
-------
V = 100R/MG
R = the gallons of oil recovered,
M = the mass of oil shale loaded into the retort,
G = the grade of the shale, as determined by two Fischer
assay samples of the st ale loaded.
In a dozen or so combustion runs made in our smaller retort over the past
two years we have obtained yields averaging 91.5%. Some oil is lost during
retorting by combustion, coking and cracking that is not lost during the
Fischer assay. The quantities R and M are measured with sufficient accuracy
that their errors are negligible in comparison with that of the assay. If
we take for that error the value given earlier, 0.371 gal/ton, the mean of
two determinations will have a standard error of 0.371AJ2 0.262, so a 95%
confidence interval for the true graoe should be that mean ± 0.53 gal/ton,
approximately. Since this amounted to 2.2% of the assay itself, we had been
allowing in our thinking for about tnis much error in the yield. We were
therefore surprised when our yield fcr Run S-18 of the small retort turned
out to be over 103%.
Well, as you no doubt have noticed by now, we failed to allow for the
introduction of sampling error. We had been careful to assure that our
sample was representative of the load and did not realize that, because of
the relatively large size of the particules that make up most of the sample
mass, our sample--38.2 lb--was too small.
When I began to suspect this cause, I looked for some data from which I
could estimate the standard deviation of grade in our shale pile. In the
summer of 1977 we loaded our large retort with a matrix of shale crushed to
less than 3 inches in size in which were embedded 68 larger blocks that were
selected from our pile. We began by picking blocks in a narrow size class,
8 to 12 inches, from all over the pile until we had 105 blocks. Except for
their size, they were a random sample of our stock. Each of these was
weighed in the air and weighed again under water to determine its density,
then the block densities were adjusted slightly to correct for small amounts
of included voids. From the corrected density, the average grade of each
block was estimated using a slightly revised equation based on the data of
Smith. 4 The mean and standard deviation were 25.3 and 10.05 gal/ton; our
shale pile is a little richer and is not quite as variable as the specimens
of Smith.
The material loaded in Run S-18 was obtained representatively from our
entire shale pile by a large scale crushing and sampling procedure, which we
call “donkey-walking.” This procedure, whose details I’ll skip, apportions
the material being crushed evenly, a little at a time, to a circle of many
55-gallon drums. Subsequent examination of such drums has shown that they
are very closely alike in their size distributions and average chemical
properties. The 5-18 load came from one such drum, which was first similar-
ly redistributed, now on a much smaller scale, to 5-gallon buckets. The
buckets were loaded completely into the retort until it was full and all the
leftover material was screened, then repeatedly recrushed and redistributed
320
-------
TABLE 1.
Screen Fractio
Inches or Mesh
n,
nbr
Mass, lb.
Ant
Mean
hmetic
Size, cm
Estimated Number
of Particles
-3, + 2 in
11.8
6.35
33
-2, ÷ 1
10.6
3.81
139
-1, + 1/2
5.7
1.90
600
-1/2, + 1/4
4.0
0.96
3300
-1/4, + 6 mesh
1.7
0.488
10600
-6, + 12
1.5
0.252
68000
-12, + 20
1.1
0.126
360000
-20
1.8
< 0.05
> iO
0.05 0.1
0.2
0.5
1.0
5 tO
Particle size distribution for 38.2 lb. sample left over
from load for Run S-18. While lower region is fairly
straight, sharp upward curve at right end indicates severe
truncation of theoretically long right tail of lognormal
probability distribution.
I! ‘‘I I I ii
7.6
99
95
90
70
50
• MASS
- PERCENT
- BELOW
• GIVEN
SIZE
MAX
30•
I0
5
2
(SCREEN
Figure 3.
d, SIZE cm
I I I I I I II1 j I I I liii
2
OPENING),
321
-------
into smaller quantities to get the 100-gram, finely-divided samples required
for the Fischer assay. The size distribution of the leftover 38.2 pounds of
material is given in Table 1, along with the number of particles in each
screan fraction as estimated from Eq. 3b with f 0.284 and p = 2.2 g/cm 3 .
The cumulative mass-based size distribution is also plotted in Figure 3 on
lognormal probability paper.
From the table it is clear that., although there are plenty of particles
in the size classes below 0.5 inch, there are probably too few in the three
upper classes that constitute 73.6% of the sample mass, with the top two
sizes being very severely underrepresented. The average size for this
sample, calculated by Eq. 14, is 4.65 cm; by Eq. 5, the sample mass required
to at least equalize sampling and measurement contributions to the error of
the estimated grade is M = 1 x 2 (10.05/0.371)2 (4.65) 148000 g 325 lb.
With only 38.2 ib, our sampling error drowns the measurement error and makes
the combined error of the estimated grade equal to 0.66 gal/ton. This
figure corresponds to a 95% confidence band for yield in Run S-18 of more
than ± 5%, so our true yield in that run was probably what it should have
been, something under 100 percent.
CONCLUSION
I have provided equations for choosing a minimum adequate sample size
from a reservoir of oil shale rubble, based on the fact that properties of
oil shale are continuously and intimately, rather than discretely, distrib-
uted throughout all the particles. The guiding principle is that the con-
tributions of sampling and measurement errors to the total error of estima-
tion shall be equal. Where accurate costs of sampling and measurement are
known, that principle may be discardec and a minimum-cost sample size can be
determined for any desired estimation error. The sample size is proportion-
al to the third power of the particle size; when the reservoir contains
distributed sizes, the mean sample size to be used in determining sample
mass is dominat.ed by the larger sizes present. Neglecting the contribution
of error due to sample size, even when procedures are followed to make the
sample representative, can cause serious underestimation of errors in esti-
mated properties of quantities derived from them.
REFERENCES
1. Miller, S., “Federal Environmental Monitoring: Will the Bubble
Burst?”, Env. Sd. & Tech. 12 (12) 1264 (Nov. 1978).
2. Aplan, F.F., Ch. 27 of “Handbook of Mineral Dressing,” A.F. Taggart,
Ed., J. Wiley & Sons, New York (1945).
3. Harris, W.E., and Kratochvil, B., “Sampling Variance in Analysis for
Tract Components in Solids,” Anal. Chem. 46 , 313 (Feb. 1974). See,
too, “Sampling, Manipulative, Observational, and Evaluative Errors,” by
W.E. Harris, Amer. Lab. 10 (1) (Jan. 1978).
322
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4. Smith, J.W. , “Specific Gravity Oil Yield Relationships of Two Colorado
Oil Shale Cores,” md. Eng. Ctiem. 48 (3), 461 (Mar. 1956).
5. “1972 Book of Annual Standards, Part 19,” 02234-72, “Standard Methods
for Collection of a Gross Sample of Coal,” p. 355, Amer. Soc. Testing
Matls., Philadelphia (1972).
6. Mensik, J.D. , private communication, Dec. 1977. Dr. Mensik chairs an
ASTM committee that is developing a standard for determination of grade
in oil shale.
7. Smith, J.W. , “Conversion Constants for Mahogany Zone Oil Shale, “ Amer.
Ass’n. of Petroleum Geologists Bull. 50 , 167 (1966).
8. Heistand, R.N. , “The Fischer Assay: Standard for the Oil Shale
Industry,” Energy Sources 2 (4) (1976). See Table 2.
9. Matzick, A., Dannenberg, R.O. and Guthrie, B. , “Experiments in Crushing
Green River Oil Shale,” Bur. Mines R .I. 5563, p. 13 (1960). The essen-
tial plot is reproduced in both editions of the widely available “Syn-
thetic Fuels Data Handbook,” G.L. Baughman, Ed., Cameron Engineers
(1975, 1978).
ACKNOWLEDGEMENTS
I am grateful to my project leaders, Arthur Lewis and Albert Rothman,
for encouraging my interest in matters statistical and to William Miller,
our staunchly supporting mechanical engineer, for our discussions of shale
sampling at the Laboratory.
323
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APPENDIX
Suppose it. is desired to estimate some property, x, of a reservoir of
rubbled oil shale and that information is available on the costs (1) of
collecting and preparing the sample for the measurement and (2) of making
the measurement on the sample. Also known are the variances a 2 and a 2 of
the property in the reservoir and the measurement, respectvely. It is not
unreasonable to assume the cost of sampling to be proportional to the sample
size (either M or n, since, by Eq. 3b they are proportional) and the cost of
the determiantion to be proportional to the number of replicates run. Then
we can write
C = c 5 n + c 1 r (Al)
How should n a d r be chosen so as to achieve a certain estimation error,
a-, at the lowest cost?
x
Equation 1 gives the variance of the estimate.
a 2 = a 2 /n + o 2 /r (1)
x m
r = a /(c - a 2 /n) (2)
Substituting into Equation Al,
Czcsn+CmcJ (cr _a21n)l (A2)
= c + da 2 (-l)(cr - a 2 in) 2 (-)a 2 (-)(1/n 2 )
+ cma a 2 I(na - a 2 ) 2 (A3)
Set dC/dn 0, solve for n.
n = (o 2 /c ) [ /Ea Ic 5 a + iJ (A4)
Now taking the second derivative,
= 2a 2 a 2 a 2 c /(na 2 - a 2 ) 3 (A5)
dn 2 xm m x
Since a 2 - > a 2 /n by its definition, (na - o2)3 will be positive for
all n > 1. Tt*erefore, d 2 C/dn 2 is positive for n as given by Eq. A4, so that
a does define the minimum for C.
324
-------
If that n is now substituted into Eq. lb. one obtains after some manip-
ulation
r (o I o ) [ (1 + ..Jcrncr / c cJ2) / ’ / c 5 c 2 ] (A6)
Dividing this into A4, we find that the ratio of n to r is given by
nlr = ,Jc i 2 /cc (A7)
Example of use:
Suppose for estimation of grade you wanted aG = 0.5 gal/ton. Sampling
and preparation (repeated grinding, splitting, screening) costs for the
environment involved are proportional to the mass of sample taken, about $2
per pound for samples_of 20 lb and more. Mean size is about 5 cm. Taking
fp = 1, c 5 $2 x fp(d) 3 /(454 g/ib) 56 /particle. c $10 per assay, cr 2
102, çy 2 0.3712 0.1376. Equation A4 gives n = 4 3 pieces or 127 ib,
and r ismfound to be 4 from Eq. A6 or A7.
325
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SAMPLING AND HANDLING OF
OIL SHALE SOLIDS AND LIQUIDS
Thomas R. Wildeman
Department of Chemistry and Geochemistry
Colorado School of Mines
Golden, Colorado 80401
I NTRO [ )UCT ION
This paper is a practical review of what various groups have learned
while sampling, handling, and preserving materials from oil shale retorting
operations. The experience of this group has been in the preparation of raw
a;id retorted shale from the TOSCO II process,’ the Fiscfler assay ot a stan-
dard shale, 2 3 and the sampling and analysis of raw and retorted shales,
oils and waters from the Paraho process. 4 6 Most of these studies concern
surface rather than in situ retorts. However, the observations of other
research groups which have been sampling oil shale materials related to in
situ retorting have been considered in the writing of this paper. The
experiences of the Berkeley group 7 8 and the Battelle group 9 have been
especially useful. The methods of analysis that have been used in this
study are not discussed in this paper but can be found in other reports. 3
6 10 11 There has been much written on the sampling, handling, and storage
of environmental materials; obviously the methods suggested here have been
based on that accumulated wisdom. In this regard, a recent review by
Malenthal and Becker 12 provides a good summary of the procedures used for
sampling and handling environmental materials. Most of the various proce-
dures used here are mentioned in that review. 12
Regarding the Quality Assurance Program of the Environmental Protection
Agency, this paper will be more applicable to the questions of siting cri-
teria, field methods, sampling frequency, and preservation of samples.’ 3
The questions of data acquisition, standard methods of analysis, equivalent
methods of analysis, reference samples and monitoring capabilities are not
discussed here.
The paper is divided into two main sections: SAMPLING and HANDLING AND
PRESERVATION. Within each section there are subsections on solids and
liquids, and each subsection is divided into conclusions, discussion, and
recommended procedures.
326
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SAMPLING
Solid Materials
Conclusions -
1. Samplina raw and retorted shale every half hour using the
sampling systems on the Paraho retort places a burden on the
sampling system, the retort personnel, and the laboratory
personnel. Fortunately, conclusion 2 states that sampling
this often is not necessary.
2. The .‘esults of the sampling program for the Paraho retort
suggest that taking a weekly composite sample is sufficient
for monitoring the inorganic constituents in raw and retorted
shale. The organic constituents can be monitored by taking a
daily composite sample.
Discussion on the Conclusions -
The conclusions on sampling are primarily the result of the sampling
program undertaken at Paraho in the summer of 1977. This was one of the
more extensive sampling studies ever attempted on an oil shale retort. The
details of the program and the initial results have been published in other
reports. 4 5 6 A summary of the research follows.
The Paraho retort is an aboveground, vertical kiln which processes oil
shale as a physically heterogeneous leedstock. The shale is crushed to -3
inch to +1/2 inch normal size. A study was made to determine how the trace
elements varied in the retort. A nested sampling scheme was devised which
went for 30 days to test the variatic.n in the daily, the 8-hour, the 1-hour
and the analysis levels. A diagram of the sampling design is shown in
Figure 1. The sampling of the other materials at Paraho was fit to the
sampling of the raw shale. For the retorted shale, the same design (Figure
1) as for the raw shale was used.
One other goal of the Paraho orogram is to try to uncover element
relations between the feedstock and the products. Thus, the timing of the
collection of the various materials has to be considered. It takes about 4
hours for feedstock to pass through the retort and exit as retorted shale.
Paraho measures the amount of oil collected in a day from 0000 to 2400
hours. Thus the retort day starts at 2200 of the previous day for the raw
shale, at 0000 for the product oil nad water and at 0200 for the retorted
shale. On this time scheme, the raw shale, retorted shale, oil and water
are related to one another. Figure 2 is a diagram of when samples were
collected on a typical retort day.
The system used for sampling raw and retorted shale at the Paraho
retort has been previously described. 4 However, there are some important
features of the system, that require mention in this paper. At preset
thtervals of usually 30-60 minutes, a motorized gate diverts the complete
327
-------
24Hr
8Hr
lHr
8/23
1111.1111
30 RETORT
DAYS
(i.)
9/20
6
30
III Ii F1 1
FIGURE
1. TIME NESTED PARAHO
SAMPLING DESIGN.
-------
RETORT
DAY
3 SAMPLING
TIMES
RETORT.
GAS FINES OIL
SHALE SHALE
RECYCLE & &
LINE DUST WATER
z
0
0
1
w
-j
0
,)
L
>-
w
II
2
—3
—3
C
z
0
0
U i
0
-J
FIGURE 2.
‘3
N.)
—oooo
0800
—1600
— 2400
1
2
1
-------
flow on the cor,veyor belt to the sampler. This amounts to around 200 pounds
of material in a single cut. The sample passes through a series of crush-
ers and splitters until a representative two pound sample is retained. The
hourly samples can be taken or they can be accumulated to make an 8-hour or
24-hour composite sample.
The results of the analyses on the raw shale are shown in Table 1. The
oil, water and gas analysis is by Fischer assay’ 4 and the elemental analyses
are by energy dispersive x-ray fluoi-escence ana]y5js . S The analysis of
variance study on the raw shale concentration results show that for the oil
yield 63% of the variation was on the daily level and 37% on the hourly
level. This implies that a single sample cannot be representative of the
oil yield over the 30-day period. However, since the amount of variance on
the 1-hour and 8-hour levels is low, a composite sample need be taken only
once a day to determine the organic characteristics of the feedstock and
retorted shale for that day’s operations. A scan of the results for the 17
elements analyzed shows that over 60% of the variance for all these elements
lies on the 1-hour or analysis level. None of the elements show daily
variances similar to the Fischer assays. Thus, on a production level none
of the elements analyzed till now vary the same way as the organic content
of the oil shale. However, the analysis of variance results show that a
representative sample for the inorganic and trace elements can be deter-
mined. Thus, the grand mean for the retort month is a reasonable average
for that month. Also, the relative standard deviations for the analysis of
the elements is about 10%. This amount of analytical error is tolerable for
most environmental studies. Yet, this amount of imprecision often contribu-
ted to the majority of the variance in the average. This means that for
these elements, oil shale feedstock is quite uniform and homogeneous.
The same analyses have been made on the retorted shale and the same
conclusions made for the raw shale apply to the retorted shale. In fact,
the retorted shale is even more uniform than the feedstock. Fox and cowork-
ers at the Lawrence Berkeley Laboratories have recently completed an analy-
sis program of a core taken from the U.S. Naval Oil Shale Reserve No. 1.26
It is from the same stratigraphic section as the shale mined at Anvil
Points. The range in concentration of the elements in the formation is
significantly greater than what is shown in Table 1. This implies that
mining, hauling and crushing blends the feedstock. It also implies that the
raw shale contained in an in situ retort may not be as uniform as the feed-
stock for a surface retort. The best method for sampling an in situ retort
is still to be defined because the proper studies have yet to be made.
An important distinction occurs between taking just one sample daily or
weekly and preparing a composite sample from the hourly cuts. In the case
of one sample, the uncertainties exhibited on the 1- and 8-hour levels are
compounded into a total variance which can be quite large. If samples are
taken hourly and then combined into a composite, then the variances on the
lower levels are eliminated and the uncertainty should be similar to what is
seen on the daily level in Table 1. Consequently, it is quite important to
take hourly cuts but then combine the cuts into a reasonable composite
sample.
330
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TABLE 1. CONCENTRATION PARAMETERS OF PARAHO OIL SHALE
The Sampling Period is from August 23, 1977 through September 20, 1977
Conc.
Grand
Grand
Grand
Rel.
Range
Avg.
Analysis
% of
Variance
Std.
Std. 0ev.
Rel.
24 hr
8 hr
1 hr
Analysis
Substance
Unit
Mean
0ev.
%
0ev. %
Level
Level
Level
Level
Oil
gpt
27.0
3.2
11.9
22.0-39.0
2.0
63.0
0.0
37.0
--
Water
gpt
4.4
1.7
39.0
1.6-11.4
15.0
0.0
93.0
7.0
--
Gas & Loss
gpt
2.2
0.6
28.0
1.0-3.7
20.0
0.0
26.0
74.0
--
Ca
%
12.3
1.5
12.0
7.2-15.0
13.0
3.0
0.0
0.0
97.0
Mn
ppm
313.0
34.0
11.0
191.0-380.0
10.0
12.0
0.0
0.0
88.0
Fe
%
2.13
0.21
10.0
1.2-2.5
9.2
6.0
0.0
0.0
94.0
Ni
ppm
32.0
3.0
9.5
18.0-36.0
6.6
0.0
0.0
55.0
45.0
Cu
ppm
35.0
4.0
10.1
20.0-40.0
6.6
8.0
22.0
2.0
68.0
Zn
ppm
83.0
24.0
29.0
40.0-180.0
9.8
14.0
0.0
68.0
18.0
Ga
ppm
7.0
0.8
12.0
4.0-8.0
7.6
18.0
0.0
32.0
50.0
As
ppm
44.0
5.6
13.0
26.0-58.0
8.7
25.0
0.0
23.0
52.0
Se
ppm
1.5
0.26
17.0
1.0-2.3
15.0
7.0
0.0
0.0
93.0
Rb
ppm
80.0
6.0
7.4
45.0-85.0
1.7
24.0
0.0
73.0
3.0
Sr
ppm
770.0
66.0
8.6
410.0-830.0
2.2
32.0
0.0
62.0
6.0
V
ppm
12.0
1.2
10.6
6.2-14.0
4.9
8.0
0.0
49.0
43.0
Zr
ppm
56.0
14.0
25.0
26.0-120.0
8.3
10.0
0.0
80.0
10.0
Nb
ppm
5.7
0.5
9.7
3.3-6.6
2.4
19.0
0.0
72.0
9.0
Mo
ppm
23.0
2.5
10.8
13.0-28.0
2.3
0.0
0.0
89.0
11.0
Ba
ppm
480.0
67.0
14.0
240.0-670.0
1.7
14.0
0.0
84.0
2.0
Pb
ppm
24.0
3.1
13.0
15.0-35.0
3.2
0.0
26.0
65.0
9.0
-------
Sampling Proced..jres for Surface Retorts -
Organic constituents--A sampling system for raw and retorted shale
similar in design to the one used at Paraho 4 14 should be available on all
surface retorts so that samples for quality control and environmental
studies can be secured. If this is the case, the daily average sample
delivered by that sampling system should be a composite of 24 one-hour
samples. This daily composite sample should be suitable for organic studies
made on raw and retorted shale. A daily sample is what is normally used for
most quality control experiments and this sampling period should be reason-
able for most environmental tests on the organic constituents.
In organic constituents--A weekly composite sample of raw and retorted
shale should be sufficient for most studies that need to be performed on the
inorganic constituents in these materials. This holds for quality control
and for environmental studies. The best way to secure the sample is to
split 100 grams from the composite daily sample. These splits can be com-
bined and blended on a weekly basis.
Liquid Materials
Conclusions -
The following conclusions are again based on the experiences encounter-
ed during the Paraho sampling program. Consequently, they apply primarily
to surface retorts.
1. Concerning the liquid products, sampling these streams can
only be done every 8 hours. The reason is that the water has
to be separated from the oil by settling and the amount that
settles out is about 3% or less of the oil. So accumulating
1 gallon of water often requires the settling of 500 gallons,
which is about 8 hours of retorting.
2. Also, at Anvil Points, sampling oil and water at this inter-
val requires the manipulation of numerous valves. This makes
the procedure prone to human error.
Discussion on the Conclusions -
There is great difference between how much is known about the solid
materials and the liquids. This difference is partly due to the difficulty
in sampling the liquid products on a surface retort. This difficulty is
reflected in the conclusions. All those for the liquids are based on prac-
tical limitations whereas the sampling for the solids is built on a scien-
tific basis.
The conclusions on the liquid handling system at Paraho are partly
based on hours of confusion and frustration. There is no continuous sampl-
ing system for the liquids as there is for the solids. Even if such were
available, the small amount of water produced makes it even more difficult
332
-------
to secure a s . mple of that material. During the Paraho study, securing a
sample of the liquid product water from one shift was so difficult that in
some instances it was not obvious that the proper tank was being sampled.
Another situation also dictates against sampling the liquids more often than
every shift. As is discussed in the HANDLING AND PRESERVATION section, the
water and oil require much handling immediately after sampling. The water
has to be separated from the oil, splits have to be prepared, and some
splits bubbled with N 2 . All this typically takes about two or three hours.
So processing the liquids at more frequent intervals, becomes difficult
unless sufficient personnel are available.
Sampling water from in situ retorts may not present the difficulties
that it does on surface retorts. This is because the amount of water pro-
duced is about equal to the amount of oil produced. Consequently, one is
not faced with the problem of separating a small amount of water from the
oil.
Sampling Procedures for Surface Retorts -
The following procedures are based on practical considerations and not
on a chemical study of the liquid samples. Consequently, it is better to
consider the following as opinions and not firm recommendations.
Oils--A continuous sampling system should be built into the product oil
handling system of a surface retort. Special attention should be given to
the materials used for construction of the system. Stainless steel and
glass may be reasonable for organic constituents but reactions and adsorp-
tion may occur between these materials and inorganic constituents. Handling
of the sample may make collection on an hourly basis impractical, however
the oil sampliig system should be cdpable of delivering 8-hour and daily
samples.
Product Waters--On a surface retort, the small amount of water associ-
ated with the product oil makes the sampling of the water on a continuous
basis impractical. Furthermore, sampling of water on an 8-hour basis is
also difficult. In this case, a compromise will probably have to be made
between securing a definite sample by a convenient method and a questionable
sample by a difficult method. A reasonable procedure would be to deliver
oil to 1-day holding tanks that would have sampling taps built into them at
the floor. A sampling system should be designed so that a sample of product
water could be secured from the tap with little pumping or diversion of the
liquids in the tank. Each daily holding tank should have its own sampling
system.
HANDLING AND PRESERVATION
Solid Materials
33’)
-------
Conclusions -
1. Concerning the crushing and grinding of oil shale, it is
difficult to pulverize the rock to less than 100 mesh, and
this step can add contamination to the sample.
2. The crushing of surface retorted shales poses no problems
because the solids have lost much of their mechanical
strength. Preparation of retorted shale samples does raise
considerable dust so handling procedures should be designed
so that dust fractions of the retorted shale are not lost.
3. Blending of raw and retorted shales is easily achieved during
preparation. No unusual precautions have to be taken to
insure a homogeneous sample. In fact, these samples are
among the most homogeneous of geologic specimens.
4. Surface retorted shale is more homogeneous than raw shale.
The retorting evidently homogenizes the rock.
5. Many of the conclusions stated for surface retorted shale may
not hold for in situ retorted shale because the retorting
conditions are not uniform in the latter case. Also, temper-
atures in an in situ retort may reach the fusion temperature
of the rock. The retorted shale in this case will become
like basaltic cinders and rinding may become quite diff i-
cult.
Discussion on the Conclusions -
Conclusion 1 on crushing requires the attention of research groups.
When being pulverized, if too much time is spent in the grinder, oil shale
can become heated and partially retorted. Disk puverizers will especially
cause fusion and partial retorting. Currently, this project uses a SPEX
shatter box for pulverizing. If the grinding chamber is to be used for over
5 minutes, it is first cooled in a freezer. Concerning contamination, the
abundances of trace elements in oil shale are low enough that contamination
is possible from grinding and sieving equipment. Table 2 compares the
concentrations of elements in our standard oil shale (OS-i) with estimates
of the concentrations that can occur in the worst cases of contamination
from grinding in a shatter box.’ 6 17 No sieves should be used; instead,
grinding tests should be made on spare amounts of raw shale. Alumina is not
dense enough to crush small shale particles; tungsten carbide is expensive.
Thus hardened steel seems a reasonable grinding surface. In the worst case,
it could add 10% to the concentration of Ni and Mn, and 5% to Fe, Cu, and
Mo. 1
Fox and ccworkers have recently reported the results of using a plane-
tary ball mill with sintered corundum grinding surfaces for pulverizing oil
shale. 26 They found that a sample could be ground to between 100 and 200
334
-------
TABLE 2. CONCENTRATIONS OF ELEMENTS IN OIL SHALE OS-i COMPARED WITH
ESTIMATES OF CONTAMINATION FROM GRINDING AND SIEVING MATERIALS
All Concentrations are in ppm
Rock
Oil
Shale
Con
taminants
Sieve
Stainless
Grinding Materials
Tungsten
Hardened
Element
OS-i
Steel
Carbide
Alumina
Steel
Al
34000
60
5000
Ti
1400
30
V
130
6
2
Cr
40
20
Mn
270
3
50
Fe
19000
20
300
60
500
Co
12
150
60
Ni
30
3
20
Cu
50
5
10
Ga
10
80
Mo
30
5
TABLE 3. RESULTS OF HOMOGENEITY TESTS FOR Rb AND Sr ON THE
STANDARD SHALE AND A SPENT SHALE
Raw
Shale
Spent
Shale
Target Size
0.500
g
300.0
pg
0.500
g
300.0
pg
Number of Samples
8.0
6.0
8.0
6.0
Average for Sr (ppm)
Std. 0ev. for Sr (%)
584.0
24.0
584.0
90.0
771.0
17.0
735.0
23.0
Re]. Std. Dev. for Sr
(%)
4.1
15.0
2.2
3.1
Average for Rb (ppm)
Std. Dev. for Rb (ppm)
60.1
2.7
63.3
1.9
80.7
1.5
93.4
4.4
Rel. Std. Dev. for Rb
CX)
4.5
3.0
1.9
4.8
335
-------
mesh in 1 hour. The amount of contamination produced was negligible. The
only contaminant was a slight amount cf aluminum.
Conclusion 2 on the dust raised from retorted shale appears at first to
be a nuisance. However, recent analy5es on the retorted shale baghouse dust
collected at the Paraho retort have ;hown that Ni, Cu, As, Mo, and Pb are
definitely higher in Paraho retorted shale dust than in the bulk retorted
shale. 6 If this is because certain phases are more prone to dusting, then
segregation can occur by raising considerable dust while handling retorted
shale.
Conclusions 3 and 4 concern the homogeneity of oil shale. It is
remarkable how uniform it is when pulverized to -200 mesh. Table 3 contains
the results of analyses for Rb and Sr in raw shale (OS-i) and retorted shale
(SS-2) by x-ray fluorescence. 3 In both cases the samples were pulverized to
-200 mesh. The remarkable feature in the numbers is that the method which
uses only 300 pg of sample gives results which have uncertainties similar to
those of the method which uses 0.500 g. 3 Most analytical chemists would
refuse to accept a 300 pg split as a representative portion of the sample.
In the case of oil shale it appears that it is representative. Note that in
the relative st.andard deviations there is a hint that retorted shale gives
more certain concentration values than raw shale. This indication of
retorted shale being more homogeneous than raw shale was confirmed when the
concentrations for Prarho raw and retcrted shale show that the concentration
ranges and relative standard deviations for most elements in retorted shale
are lower than in raw shale. 6 Surface retorts homogenize the solid
materials.
Conclusion 5 on the retorted shale from in situ operations is exempli-
fied in the problems that Fox and coworkers had in processing spent shale
from the simulated in situ retorts. 7 In their studies and in most in situ
retorts the process is a batch operation rather than a continuous process so
little mixing of solid materials occurs. Securing a representative sample
of the retorted shale was a definite problem in their study. Also, they
suggest that PD, and possibly Zn and Cu, were added to the retorted shale
during the pulverizing and sieving operations.
The question of how to store the solid samples should be considered.
For inorganic analyses, polyethylene containers that have been washed with
811 HNO 3 would be reasonable. 12 However, plastic bottles may contribute some
organic contaminants and furthermore they do breathe so that volatile
materials can be lost or gained. If c.rganics or mercury are to be analyzed,
then perhaps glass containers would te more suitable. In addition, samples
collected for the analysis of volatile constituents should not be subjected
to wide temperature fluctuations so storage of the sealed samples in a
regrigerator is recommended.
Handling Procedures for Solid Materials -
The whole sample should be crushed to -10 mesh using jaw crushers and
roller crusherE. At this point, a suitable sized sample of not less than
336
-------
25 g should be split for further handling. This split is further pulverized
in a hardened steel shatter box to a grain size suitable for analysis.
Typically the pulverized sample has a -200 mesh grain size. No sieves
should be used; this is especially important for the retorted shale.
Instead, pulverizing tests should run and the shatter box should be operated
for an appropriate amount of time. If the time spent in pulverizing the
sample extends beyond 5 minutes. then consideration should be given to
cooling the pulverizer in a freezer beforehand. Storage should be in glass
or conventional polyetheylene bottles that have been rinsed in 8M HNO 3 at
least overnight. If the analysis includes volatile constituents, storage
should be in a refrigerator.
Liquid Materials
Conclusions -
1. Concerning the liquid products, there is a distillation of
the oils and waters from solids in surface retorts so that
minimal particulate matter cccurs in the liquids. This makes
filtration ot the samples unnecessary. This is not the case
for in situ retorts.
2. Handling of the oils and waters cannot be done in a uniform
fashion for all studies. Some studies require the liquids to
be bubbled with N 2 , while others don’t. Some require freez-
ing, others refrigeration. Some require storage in glass,
others in plastic.
3. The waters cannot be acidified since they contain appreciable
thiosulfate and this decomposes at low pHs resulting in the
formation of free sulfur.
4. Water samples can be held under refrigeration and freezing
for over a year without decomposition. However, once opened
the water does start changing within a month.
Discussion on the Conclusions -
The EPA Methods Manual on Water Analysis states that complete and
unequivocal preservation of water samples is an impossibility. 18 Complete
stability can never be obtained, and preservation techniques only retard the
chemical and biological changes that continue after the sample is taken. To
the EPA statement should be added that a universal water sample is an irnpos-
sibility. A number of samples have to be taken and handling and preserva-
tion is dictated by the analytical objective. Both of these ideas are
especially relevant to oil shale retort waters. In addition, there is a
definite difference in the character of retort liquids from a surface retort
and an in situ retort which is the b isis for Conclusion 1. With regard to
in situ retort waters, the program developed by Farrier and others on the
Omega-9 retort water provides the primary basis for any analytical program
on these waters. 19 20 21
337
-------
Conclusion 1 on the distillation is an important advantage in sampling
surface retort liquids. Filtering the water using methods such as pressure
filtration through Millipore filters can cause precipitation of some
constituents. 27 Filtering oil typically requires vacuum or pressure methods
and this may cause the loss of volatile constituents. When the Fischer
assay oil was filtered, there was no residue in 600 ml of oil. The same w s
true of the water. These observations held true for the Paraho liquids.
Concerning the separation of the two liquids, the oil predominates over the
water by a ratio of 10 to 1 and the separation is clean. A good oil sample
is easily procured. The water usually has oil in it. This can be stripped
from the water by cooling the mixture to just above freezing and filtering
through cotton or glass wool. The solid globs of oil are easily trapped.
For in situ retort liquids, separation of particulates is necessary.
For water this is usually done by filtration through 0.45 pm Millipore
filters. 7 8 19 In this case, the particulates have to be saved because
they have been found to absorb trace metals. 7 8 This should be done on warm
oil so that the temperature does not fall below the pour point.
Conclusion 2 on handling and storage has to be addressed in any analy-
sis program. In the Paraho project, four handling and preservation tech-
niques were used: refrigerated, frozen, N 2 bubbled and regrigerated, and N 2
bubbled and frozen. At the time it was thought that that would be suffi-
cient. However, some analysts felt that some splits should have been stored
in glass for the analysis of organics and mercury in the liquids. Dr.
Denney of the University of Colorado, who analyzes organic constituents in
water, reports that specially cleaned glassware with special caps are abso-
lutely essential for these analyses. The review of handling environmental
samples also stresses this point. 12 For the analysis of Hg, some groups
insist on glass, 9 while others find polyethylene acceptable. 8 The obvious
conclusion is that the type of analysis dictates the handling and preserva-
tion methods to be used. Careful planning and recording of the actual
procedures that are used are essential to the interpretation of the subse-
quent results.
Concerning Conclusion 3, this was observed by many people over the
years; ho ever, Dr. Leenheer’s research group 23 was the first to confirm
that S 2 0 3 was a major constituent in the water and that it would dispropor-
tionate with elemental sulfur precipitating when the water was acidified.
In the Paraho.program, a number of product and process waters were collected
and the S 2 0 3 concentration ranged from 0.1 to 26 mg/ml as elemental sul-
fur. 6 A concentration of 26 mg/mi is a 0.41 molar solution of S 2 0 3 . In
addition to S 2 0 3 , acidifying may cause carboxylic acids to precipitate. At
first, the restriction of not acidifying is disconcerting because this is a
standard method of assuring that trace metals are stabilized in solution. 24
However, the dissolved solids level in these waters is high, the pH is
stabilized by NH 4 HCO 3 in solution, and the oxidizing capacity appears to be
stabilized by the various sulfur species. Consequently, little aging occurs
as long as the sample is refrigerated.
338
-------
The bases for Conclusion 4 are the results on the Paraho waters. 6 It
appears that freezing preserves the pH and Eh values better than refrigera-
tion. The deterioration of the waters after opening can be observed through
the consistent rise in the specific conductance after one month of being
opened. In this case, the first set of analyses were performed and the
waters were returned to the refrigerator, then the analyses were performed
and the waters were returned to the refrigerator, then the analyses were
repeated about 30 days later.
Handling Procedures for Liquid Materials -
Special Procedures, Restrictions, and Comments -
1. The biases of the analyst come into play on the selection of
containers. My preference is for conventional virgin poly-
ethylene bottles (Nalgene type 2003). Other than FEP Teflon,
these appear to be least contaminated with trace metals and
least prone to transpiration of liquids and vapors through
the walls.’ 2 25 Oils and other organic material will attach
polyethylene, but this attack is very slow if the samples are
kept refrigerated or frozen.
2. The procedures described below are not suitable for the
analysis of trace organics. In this case special procedures
dictated by the analyst should be employed. 12 Also, all
plastic materials should be considered at contaminants.
Special consideration should be given to the type of cap used
on the container.
3. In any program, more than one method of handling and preser-
vation should be employed. The concentration values obtained
by analysis apply only to that liquid at that time of anal-
ysis. Analysis of samples prepared by a number of methods
helps to spotlight abberant results. This practice also may
yield clues on how the sample has changed from the time of
collection.
4. The procedures used below should be reasonable for the anal-
ysis of mercury. Fox and others 8 report that no loss of
mercury was found for samples that were stored in airtight,
acidwashed, polyethylene bottles at 4°C. However, in this
case it is best to consult the analyst before collection of
the sample.
Procedure for Oils--If the sample is taken from a continuous sampler,
centrifuging ‘is necessary to separate water and particulates. If the sample
is taken from a holding tank, centrifugation may not be necessary. The
sample is poured into at least 16 one-ounce polyethylene bottles that have
been washed in 8M HNO 3 , rinsed with deionized distilled water, dried, and
tightly capped.’ 2 Equal numbers of the one-ounce bottles are prepared and
stored in the following four manners: refrigerated, frozen, bubbled with N
339
-------
3nd refrigerated, bubbled with N 2 and frozen. Upon analysis, the one-once
bottles are used. Once opened, these samples will age, so the analyses
should be performed within one month or a new one-ounce bottle should be
used.
Procedure for Waters--If the sample is from an in situ retorting
process, then it should be filtered tI rough a 0.45 um Millipore filter using
a vacuum or N 2 -gas pressure. The filter with the particulates should be
saved for possible future analyses. If the micropore filtration is not
needed, then cool the water and filter the sample through cotton or glass
wool to remove insoluble organic material. The sample is poured into at
least 16 one-ounce polyethylene bottles that have been washed in 8M HNO 3 ,
rinsed with deionized distilled water, dried, and tightly capped. 12 Equal
numbers of the one-ounce bottles are prepared and stored in the following
four manners: refrigerated, frozen, bubbled with N 2 and refrigerated,
bubbled with N and frozen. None of the samples should be acidified. A
portion of the unfiltered water should be poured into polyethylene bottles
and refrigerated and frozen for future possible analyses. Upon analysis,
the one-ounce bottles are used. Once opened, these samples will age, so the
analyses should be performed within one month or a new one-ounce bottle
should be used.
ACKNOWLEDGEMENTS
This study was performed with financial support from DOE under Grant
No. COO-4O17-1 and is part of the Environmental Trace Substances Research
Program of Colorado. Discussions with Phyllis Fox, Jon Fruchter, Robert
Meglen, and Don Denney have been especially helpful in establishing the
procedures suggested in this paper.
REFERENCES
1. Wildeman, T.R., Preparation and 1 nalysis of Standard Oil Shale Samples
OS-i, SS-1, and SS-2. Nat. Bureau of Standards, Special Report, In
press.
2. Wildeman, T.R., Preparation of Fischer Assay of a Standard Oil Shale
Sample. Preprints, Div. of Petrol. Chem. , ACS, 22 (2): 760-764, 1977.
3. Wildeman, T.R., and R.R. Meglen. The Analysis of Oil Shale Materials
for Element Balance Studies, In: Analytical Chemistry of Oil Shale and
Tar Sands, Advan. in Chemistry Series, No. 170, 1978, pp. 195-212.
4. Wildeman, T.R. , and R.N. Heistanci. Trace Element Variations in an Oil
Shale Retorting Operation. Preprints, Fuel Division ACS, 24: 271-280,
1979.
5. Colorado ETSRP. Progress Report on Trace Elements in Oil Shale. DOE
Project EY-76-S-02-4017, 1978. p. 137.
340
-------
5. Colorado ETSRP. Progress Report on Trace Elements in Oil Shale. DOE
Project EY-77-S-02 -4017, 1979. In press.
7. Fox, J.P., McLaughlin, R.D., Thomas, J.F., and R.E. Poulson. The
Partitioning of As, Cd, Cu, Hg, Pb, and Zn During Simulated In Situ Oil
Shale Retorting. In: 10th Oil Shale Symposium Proceedings, Colorado
School of Mines Press, Golden. Colorado. 1977. pp• 223-237.
8. Fox, J.P. , et al. Mercury Emissions from a Simulated In Situ Oil Shale
Retort. In: Proceedings of the 11th Oil Shale Symposium, Colorado
School of Mines Press, Golden, Colorado, 1978. pp. 55-75.
9. Fruchter, J..S., Laul, J.C., Peterson, M.R., Ryan, P.W., and M.E.
Turner. high Precision Trace Eli ment and Organic Constituent Analysis
of Oil Shale and Solvent Refined Coal Materials. In: Analytical
Chemistry of Oil Shale and Tar Sands, Adv. in Chemistry Series, No.
170, 1978. pp. 255-281.
10. Fox, J.P. , Fruchter, J.S. , and T.R. Wildeman. Interlaboratory Study of
Elemental Abundances in Raw and Spent Oil Shales. Presented at the EPA
Symposium on Oil Shale Sampling, Handling and Quality Assurance, March
1979, Denver, Colorado, pp. 1-23.
11. Kubo, H., Bernthal, R. and T.R. Wildeman. Energy Dispersive X-ray
Fluorescence Analysis of Trace Elements in Oil Samples. Anal. Chem.
50: 899-903.
12. Maienthal, E.J. and D.A. Becker. A Survey on Current Literature on
Sampling, Sample Handling, for Environmental Materials and Long Term
Storage. Interface, 5(4): 49-62 (1976).
13. QA Report. Federal Environmental Monitoring: Will the Bubble Burst?
Environ. Science Technol., 12: 1264-1269, 1978.
14. Heistand, R.N. The Fischer Assay: Standard for the Oil Shale
Industry. Energy Sources, 2: 397-405, 1976.
15. Aifrey, A.C., Nunnelley, L.L. and W.R. Smyth. Medical Application of a
Small Sample X-ray Fluorescence System, Mv. in X-ray Analysis, 19:
497-509, 1976.
16. Meyers, A.T., and P.R. Burnett. Contamination of Rock Samples During
Grinding as Determined Spectrographically. Amer. Jour. Science, 251:
814-820, 1953.
17. Thompson, G., and D.C. Bankston. Sample Contamination from Grinding
and Sieving Determined by Emission Spectrometry. Appi. Spectroscopy,
24: 210-219, 1970.
341
-------
18. u.s. Environmental Protection Agency. Methods for Chemical Analysis of
Water and Wastes. Methods Development and Quality Assurance Center,
Cincinnati, Ohio, 1974. 298 pp.
19. Farrier, U.S., Virona, F.E., Phjllips, J.E., and R.E. Poulson.
Environmental Research for an In Situ Oil Shale Processing. In:
Proceedings of the 11th Oil Shale Symposium, Colorado School of Mines
Press, Golden Colorado, 1978. pp. 81-99.
20. Farrier, (LA., Poulson, R.E., Skinner, Q.D., Adams, J.C., and J.P.
Bower. Acquisition Processing and Storage for Environmental Research
of Aqueou5 Effluents Derived from In Situ Oil Shale Processing. In:
Proceedings 2nd Pacific Chem. Engin. Congress, v. 1, 1977. pp.
1031-1035.
21. Fox, J.P., Farrier, D.S., and R.E. Poulson. Chemical Characterization
and Analytical Considerations for an In Situ Oil Shale Process Water.
LETC/RI-78/7. Dept. of Energy, Laramie Energy Technology Center,
Wyoming, 1978. 45 pp.
22. Goodfellow, 1., and M.T. Atwood. Fischer Assay of Oil Shale Procedures
of the Oil Shale Corporation. Ir s: Proceeding of the Seventh Oil Shale
Symposium, Quart. Colorado School of Mines, 69(2): 205—219, 1974.
23. Stuber, H.A., and J.A. Leenheer. Fractionation of Organic Solutes in
Oil Shale Retort Waters for Sorption Studies on Processes Shale.
Preprints, Div. Fuel, Chemistry, ACS, 23(2): 165-174, 1978.
24. Brown, E., Skougstad, M.W., and M.J. Fishman. Methods for Collection
and Analysis of Water Samples for Dissolved Minerals and Gases. U.S.
Geological Survey, Techniques of Water Resources Investigation, Book 5,
Chap. A-i, 1970. 160 pp.
25. U.S. National Bureau of Standards. Accuracy in Trace Analysis:
Sampling, Sample Handling, Analysis. Nat. Bur. Standards Special
Publication 422, 1976. 1304 pp.
26. Branstetter, B. and Fox, J.P. Trace Element Analysis on the Naval Oil
Shale Reserve No. 1. Quarterly Report for July 1-Sept. 30, 1978, UCID
No. 8070. Lawrence Berkeley Laboratory, Berkeley, California. p.
1-10.
27. Fox, J.P. Retort Water Particulates, (Presented at the EPA Oil Shale
Sampling, Analysis and Quality Assurance Symposium) March 1979, Denver,
Colorado. p. 1-31.
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FACTORS TO CONSIDER IN THE DESIGN OF A
WATER QUALITY MONITORING NETWORK
Thomas G. Sanders
Assistant Professor of Civil Engineering
Colorado State University
Fort Collins, Colorado
Robert C. Ward
Associate Professor of Chemical and Agricultural Engineering
Colorado State University
Fort Collins, Colorado
INTRODUCTION
The assumption that a water quality monitoring network can detect
trends in water quality, check compliance with stream standards, and measure
ambient water quality, etc. , is incorporated into much of the enabling
legislation for water quality management in the United States. This legal
view of water quality monitoring envisions conclusive information being
generated to actively guide government’s water quality management efforts
and, at the same time, report to the legislative bodies and the public the
general water quality conditions and trends. When implemented, however,
water quality monitoring is viewed more from a technical feasibility stand-
point. That is, the problems involved in obtaining conclusive information
with the available monies force many compromises and half measures--the
consequences of which few fully understand.
Monitoring performed by an agency established by governmental action
is, in many cases, conducted over a large geographic area (defined by
political and not necessarily hydrologic boundaries) covering many miles of
streams. Simply collecting samples in such a situation often becomes a
major problem; so major, in fact, that it becomes an end in itself. In many
cases, little thought is given to the representativeness of the water
samples or types of data analysis techniques to be used or even the ultimate
use of the data. Consequently, the majority of the resources are devoted to
collecting data as it is the most immediate problem.
By using the majority of the monitoring resources to physically collect
water samples, little monitoring resources are left to consider the repre-
sentativeness of the sample in time and space, data analysis or data use.
If a balanced (collection versus use) monitoring system were to be
developed, the entire monitoring system must be examined and designed
simultaneously, hence a systems approach.
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The purpose of this paper is to review the monitoring system and then
delineate the impacts that such a systems approach of monitoring will have
on network design by considering the water quality variables to be moni-
tored, the sampling location and sampling frequency.
MONITORING SYSTEM FRAMEWORK
The actual operation of a monitoring system can be categorized into
five major functions:
1. Sample Collection
2. Laboratory Analysis
3. Data Handling
4. Data Analysis
5. Information Utilization
These five functions serve as the feedback loop from in-stream water quality
conditions to water quality management decision making. A management agency
is constantly making decisions (e.g. , relative to site approvals, regula-
tions, pollution abatement, etc.) that affect water quality. Without a
monitoring feedback loop accurately documenting the effects of those
decisions, the management’s past success and future direction are uncertain.
Monitoring Network Design--is an overriding activity (covering the five
operational functions listed above) that should carefully integrate sample
collection (e.g., location and frequency) to the type of data analysis used
to obtain the information required and actually utilized in decision making.
Thus, the design of water quality monitoring networks must take into account
the ultimate use of the data collected and the type and level of statistical
analysis applied to the data.
FACTORS IN NETWORK DESIGN
Monitoring network design, as a planning/design type function which
guides monitoring operations, can itself be broken down into three major
components:
1. Selection of Water Quality Variables* to Monitor
2. Sampling Station Location
3. Sampling Frequency
*The term water quality variable is used instead of water quality param-
eter because water quality is a random variable and can be defined by
statistical parameters such as the mean and standard deviation. In
addition, the term parameter is most often used to define constants of
deterministic equations or models and can lead to confusion by identi-
fying it as a random variable.
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Each of these factors in network design affects all the monitoring system’s
operational functions listed previously and vice versa. The degree of
impact, however, depends upon the purpose and goals of the monitoring
system.
SELECTION OF WATER QUALITY VARIABLES TO MEASURE
The selection of the water quality variable to be sampled will depend
to a large extent on the objectives of the sampling network and the back-
ground or frame of reference of the individuals responsible for developing
the objectives of the monitoring network. When a sampling network has its
primary objective to monitor compliance with stream standards, the variables
sampled are the ones specified in the legislation, for example, dissolved
oxygen (DO). DO is sampled because stream standards specify a minimum level
which should not be violated. Dissolved oxygen and other variables deemed
most important and included in stream standard legislation were those
related to water supply, coliform bacteria, biochemical oxygen demand (BOD),
temperature, turbidity, and suspended and dissolved solids, because most
individuals entering the field of water quality management during the last
few decades have a background in sanitary engineering.
Now that more individuals in professions besides sanitary (environ-
mental) engineering are interested in water quality, the number of water
quality variables which should be sampled routinely have increased. In
fact, it appears that a month does not pass that yet another water quality
variable must be sampled and included in a permanent sampling program. This
variable-a-month syndrome cannot and should not be the major variable
selection mode for a permanent, routine sampling program, but instead can be
easily accommodated in the much discussed synoptic surveys.*
It can be said that both sampling location and sampling frequency can
be developed independently of the water quality variable to be analyzed, as
both location and frequency are specified for the collection of the water
sample--the analyses are made later. However, both criteria are affected by
the water quality variable being monitored. For example, sampling once a
week at a single point in a river may be more than adequate for monitoring
the relatively stable river temperature, but may be hardly adequate for
monitoring rapidly varying coliform bacteria toncentrations. Therefore,
before a water quality monitoring network can be designed in a systematic
fashion, the variables to be monitored should be specified so that their
natural and/or man-made variation in ‘ .ime and space can be considered when
*The increasing popularity of synoptic surveys with sampling agencies is
probably due to the result that the surveys are in fact an application
of a systems approach to water quality monitoring. Unlike the
permanent, routine sampling programs, the objectives and the use of the
data, the sampling locations, the sampling frequency, the variables to
be sampled as well as the data analysis procedures are developed
completely before the survey is undertaken.
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designing the n onitoring network. In addition to considering the water
quality variables of interest, their respective units should be delineated
as well. The network design varies tremendously if a daily mean (flow
weighted) concentration is needed versus an instantaneous grab sample
concentration, the former being a result of several samples with flow
measurements equally spaced during a 24-hour period, while the latter being
only a single sample (generally in the daytime between 8:00 a.m.-4:30 p.m.).
In reality, the specification of the water quality variable to be
monitored prior to initiating network design would be ideal. In practice,
however, an already designed network is given and then one must know or
determine what water quality variables can be accurately monitored with the
existing network.
SAMPLING STATION LOCATION
The location of a permanent sampling station in a water quality moni-
toring network is probably the most critical aspect of the network design,
but all too often never properly addressed. Expediency and cost compromises
lead in many cases to sampling from bridges or near existing river gaging
stations. Whether the single grab sample from the bridge or the gaging
station is truly representative of the water mass being sampled is not
known, but generally is assumed to be by both the collectors and users of
the water quality data. Using river stage for estimating discharge,
measurement anywhere in the lateral transect would indicate exactly the
river discharge. However, this does not necessarily follow when measuring
water quality variable concentrations. In fact, research indicates the
opposite, that rarely will a single sample be indicative of the average
water quality in a rivers’ cross section.
Sampling locations for a permanent water quality network can be classi-
fied into two levels of design: macrolocation and microlocation, the former
being a function of the specific objectives of the network and the latter
being independent of the objectives but a function of the representativeness
of the water sample to be collected.
The macrolocation within a river basin usually is determined by politi-
cal boundaries (state lines), areas of major pollution loads, population
centers, etc. Macrolocation can be specified, as well, according to percent
areal coverage using basin centroids.. 1 This methodology locates sampling
points in a systematic fashion maximizing information of the entire basin
with a few strategically located stations. Figure 1 is an example of
locating sampling stations using basin centroids and sub-basin centroids
with percent areal coverage as the criteria.
The procedure for locating sampling stations is derived by determining
the centroid of a river system. Each contributing exterior tributary (this
is a stream without defined tributaries) is given the magnitude of one; an
interior stream resulting from the intersection of two exterior tributaries
would have a magnitude equal to two. Continuing downstream in the same
manner, as streams intersect, the resultant downstream stretch of river
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I) Highest Order Stations
2) Second Order Stations
3) Third Order Station
Sampling Station Locations
Figure 1. Macrolocation of Sampling Stations Within a River Basin
Using the Percent Areal Coverage as the Criteria Specifying
Location.
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would have a magnitude equal to the sum of the magnitudes of the preceding
intersecting streams. Finally, at the mouth of the river, the magnitude of
the final river section will be equal to the number of contributing exterior
tributaries-—22 in Figure 1. Dividing the magnitude of the final stretch of
the river by two, the centroid of the basin, 11 is calculated. The section
of river having a magnitude equal to the centroid divides the basin into two
sections and is the location of the sampling station with highest order (the
assumption is made that there exists a sampling station at the mouth of the
river basin). In many cases, when applying this procedure to a river basin,
there usually is not a stream having a magnitude equal to the centroid.
When this occurs, the stream segment having a magnitude closest to the
centroid is chosen. The next order of sampling locations is determined by
finding the centroids of the two equal sections above and below the initial
river basin cer,troid. The procedure is continued finding the centroid of
the sections of the river separated by preceding centroids until a percent-
age of area] coverage is attained.
The percentage of area] coverage specified by the monitoring agency is
defined as the number of sampling stations divided by the magnitude of the
basin. Intrinsic to this objective procedure is the cpncept of a sampling
station hierarchy that orders the importance of each sampling station in the
basin. 2 This provides a realistic methodology in which a rational imple-
mentation program can proceed: the most important stations (highest order)
are built first and as the resources become available, additional stations
can be built. As each succeeding hierarchy of stations are established the
percentage of area] coverage is increased.
Having established the macrolocations within a river basin, the micro-
location is then determined. The macrolocation specifies the river reach to
be sampled while the microlocation specifies the point in the reach to
sampled. This point is the location of a zone in the river reach where
complete mixing exists and only one sample is required from the lateral
transect in order to obtain a representative (in space) sample. Being a
function of the distance downstream from the nearest outfall, the zone of
complete mixing can be estimated using various methodologies.
Given the assumptions that a point source pollutant distribution in a
stream approximates a Gaussian distribution, and that boundaries can be
modeled using image theory, the following equation can predict the distance
downstream in a straight, uniform channel from a point source pollutant to a
zone of complete mixing. 3
L = (1)
where Ly = mixing distance for complete lateral mixing,
= distance from point source to farthest lateral boundary,
u = mean stream velocity
348
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0 = lateral turbulent diffusion coefficient.
Unfortunately, there may exist in a given river reach no points of
complete mixing* due in part to the random nature of the aforementioned
mixing distance, inapplicability of the assumptions used in the determina-
tion of the mixing distance, or more often than not, not enough river length
or turbulence to assure complete mixing within the specified river reach.
If there is not a completely mixed zone in the river reach to be
sampled, there are three alternatives: (1) sample anyway at a single point
and assume it is representative (this is the general procedure being applied
today); (2) don’t sample the river reach at all, because the data which
would be obtained does not represent the existing river quality, but only
the quality of the sample volume collected--in other words, the data is
useless; (3) sample at several points in the lateral transect collecting a
composite mean, which would be representative of the water quality in the
river at that point in time and space.
If the sample is not representative of the water mass, the frequency of
sampling as well as the mode of data analysis, interpretation and presenta-
tion and the realistic use of the data for objective decision making becomes
inconsequential. In spite of this fact, criteria to establish station
locations for representative sampling has received relatively little atten-
tion from both state and federal ag?ncies responsible for water quality
monitori ng.
SAMPLING FREQUENCY
Once sampling stations have been located so that samples collected are
representative in space, sampling frequency should be specified so that the
samples are representative in time.
Sampling frequency at each permanent sampling station within a river
basin is a very important parameter which must be considered in the design
of a water quality monitoring network. A large portion of the costs of
operating a monitoring network is directly related to the frequency of
sampling. However, the reliability and utility of water quality data
derived from a monitoring network is likewise related to the frequency of
sampling. Addressing this anomaly Quimpo 4 summarized the significance of
sampling frequency and stated that:
On the one hand, by sampling too often, the information obtained
is redundant and thus expensive, and on the other hand, sampling
too infrequently bypasses some information necessitating an
extended period of observation.
*J should be noted that field verification of a completely mixed zone
prior to locating a permanent sampling station can be easily done by
collecting multiple samples in the cross section and analyzing the data
using a well-known one- or two-way analysis of variance techniques.
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Significant as sampling frequency is to detecting stream standards viola-
tion, maintaining effluent standards, and estimating temporal changes in
ambient water quality, very little quantitative criteria which designates
appropriate sampling frequencies have been applied to the design of water
quality monitoring networks. In many cases, professional judgment and cost
constraints provide the basis for sampling frequencies. All too often.
frequencies are the same at each station and based upon routing capabili-
ties, once-a-month, once-a-week, etc. and although possibly the only
practical means to implement a sampling program considering the statistical
background of data collectors, there do exist many quantitative, statisti-
cally meaningful procedures to specify sampling frequencies at each
station. 5 ’ 6 The methods include specifying frequencies as functions of the
cyclic variations of the water quality variable (Nyquist frequency), the
drainage basin area and the ratio of maximum to minimum f low, 7 the conf i-
dence interval of the annual mean, 8 ’ 9 the number of data per year for
hypotheses,’° and the power of a test measuring water quality interven-
tion.
All of the aforementioned procedures can be applied to the design of a
water quality monitoring network with each requiring a different level or
statistical sophistication insofar as data requirements as well as assump-
tions applying.
One of the simplest approaches is to assume that the water quality
variable concentrations are random, independent and identically distributed
(lid) and determine the number of samples per year as a function of an
allowable (specified) confidence interval of the mean annual concentration
(this is analogous to the procedure for determining how many analyses of a
water sample should be made to determine a reasonable estimate of the mean
water quality variable concentration). 5
[ t 12 s]2
where n = Number of equally spaced samples collected per year
‘2 = Constant which is a function of the level of significance
a, and the number of samples
S = Standard deviation of the water quality concentrations
R = Specified half-width of the confidence interval of the
annual mean.
Using the same assumption, that the water quality variable is lid, the
number of samples per year can be specified as a function of the data
analysis procedure as well .’° For example, if annual means were to be
tested for significant changes using the difference in means, then to detect
an assumed level of change, the number of samples can be specified.
A much more sophisticated procedure, representing a higher level of
statistical analysis, is to recognize that water quality variables may not
be lid, but highly dependent, not identically distributed, having seasonal
350
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variation, and iietermjne sampling frequency as a function of the variability
of the water quality variable time series after trend and periodic compo-
nents have been removed. Unfortunately, other than mean daily discharge,
data bases of water quality variable of sufficient number, reliability and
length are generally not available for application of this procedure.
Once a uniform sampling frequency criterion is selected it can be
utilized to objectively distribute sampling frequencies within a water
quality monitoring network. For example, the expected half-width of the
confidence interval of the annual mean (for specifying sampling frequencies)
approach can be applied basin-wide in a consistent fashion by specifying
equality of these expected half-widths at each sampling station. Thus,
stations where water quality varies tremendously will be sampled more
frequently, than stations where the water quality varies little. With
reference to Figure 2 which is a plot of the expected half-width of the
confidence interval of mean log river flow versus the number of samples per
year, the number of samples collected at each station within the river basin
for a given R are determined by drawing a horizontal line through R and
reading the number of samples on the abscissa axis below the intersections
on the horizontal line with each curve. Figure 2 may also be used in an
iterative fashion to specify sampling frequencies at each station when a
total number of samples from the basin is specified. For example, if only N
samples per year would be collected and analyzed, a value of R is assumed
and a line is drawn horizontally; the number of samples specified by the
intersection of the curves are summed and compared to N. If the sum were
not equal to N then another estimate of R would be made until the sum of all
the samples is equal to N.
It should be noted that the expected half-width of the annual mean is
not the only statistic that can be used to specify sampling frequencies; the
expected half-width divided by the mean is a measure of relative error and
may be more appropriate when assigning sampling frequencies in a basin where
water quality varies tremendously from river to river.
When developing sampling frequencies, one must keep in mind two very
important cycles which can have immense impact on water quality concen-
trations--the diurnal cycle and the weekly cycle. The effect of the diurnal
cycle (which is a function of the rotation of the earth) can be eliminated
by sampling in equal time intervals for a 24-hour period and the effect of
the weekly cycle (which is a function of mans’ activity) can be eliminated
by specifying that sampling intervals for a network cannot be multiples of
seven--occasional sampling on weekends would be necessary.
Perhaps, the major impact between network design in terms of variables
to be monitored, sampling location, and sampling frequency and the opera-
tional monitoring functions is in the area of data analysis and, conse-
quently, ultimate value of the monitoring network information. Any sampling
program that is to generate conclusive results from observing a stochastic
time series (water quality concentrations) must be well planned and statis-
tically designed. Statistically designed implies that the sampling is
planned (in proper locations and numbers) so that the statistical analysis
351
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0.9
0,8
O.7
0.6
0.5
0.4
0.3
O.2
0,1
R vs. Number of Samples per Year
Ware
Conn, at Thompsonville
Deerfield
4 Conn, at Montague City
5 Millers
6 Conn, at Vernon
7 Westfield
8 Conn, ot Turners Foils
O
10
20
30
40
50
Number of Samples per Year
figure 2. A plot nunber of samples per year of the expected half-
width of the confidence Interval of mean log flow, R,
versus number of Samples for Several Rivers in the Connect-
icut River Basin.
352
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techniques chosen will be able to yield quantitative information. Thus, the
data analysis techniques (level and type of statistics) to be used must be
defined in order to know how to compute proper sampling frequencies,
locations, etc.
SUMMARY AND CONCLUSIONS
The previous discussion has pointed out many problems associated with
not designing a monitoring system in a systems context. Perhaps the major
concern is that all aspects of a monitoring program should match in terms of
accuracy. For example, it would not be wise to use time series analysis on
nonrepresentative, grab sample data--the system would be providing excessive
accuracy in one segment compared to the accuracy in another segment.
In a similar manner, it may be unrealistic to encourage use of more
sophisticated sample collection and laboratory analysis techniques if the
data is not to receive a thorough statistical analysis.
We cannot continue to test hypotheses, make decisions, justify addi-
tiortal billions of dollars to be spent on pollution control, etc. using
water quality data which are collected, only in the daytime, not flow
weighted, several times a year, from locations which are not completely
mixed and using lab analyses procedures which may have more variation in
their results when analyzing the same sample than the ambient variation of
the water quality variable in the river.
Perhaps an even larger concern to those in monitoring network design is
the use of water quality standards that generally ignore statistics. This
lowers the value of any information, from a compliance viewpoint, to that of
spot checks. Incorporating water quality means and variation into standards
would greatly facilitate incorporating more statistics into monitoring.
This would have the effect of tying network design to data use in a much
more concrete, statistical manner than is now possible. It would also
encourage use of the system approach to network design as there would be a
statistical thread moving through the entire monitoring operation.
ACKNOWLEDGMENTS
Financial support for the research was provided by the Office of Water
Resources Research, Department of the Interior WR-A041-Mass, WR-B059-Mass
and B-186-Colo.
REFERENCES
1. Sanders, T.G. Rational Design Criteria for a River Quality Monitoring
Network. Ph.D. Dissertation, Department of Civil Engineering,
University of Massachusetts, Amherst, Massachusetts, 1974.
2. Sharp, W.E. A Topologically Optimum River Sampling Plan for South
Carolina. Water Resources Research Institute Report No. 36, Clemson
University, Clemson, South Carolina, April 1973.
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3. Sanders, T.G., D.D. Adrian and J.M. Joyce. Mixing Length for
Representative Water Quality Sampling. Journal Water Pollution Control
Federation. 49:2467-2478, 1977.
4. Quimpo, R.G. Stochastic Analysis of Daily River Flows. Journal of the
Hydraulics, ASCE. 94(HY1):43-47, January 1968.
5. Sanders, T.G. and D.D. Adrian. Sampling Frequency for River Quality
Monitoring. Water Resources Research. 14(4):569-576, August 1978.
6. Loftis, J.C. Statistical and Economic Considerations for Improving
Regulatory Water Quality Monitoring Networks. Doctoral Dissertation
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in Agricultural Engineering, Colorado State
University, Fort Collins, Colorado, 1978.
7. Pomeroy, R.D. and G.T. Orlob. Problems of Setting Standards of
Surveillance for Water Quality Control. California State Water Quality
Control Board Publication No. 65, Sacramento, California, May 1967.
8. Ward, R.C.., K.S. Nielsen and M. Bundgaard-Nielsen. Design of Monitor-
ing Systems for Water Quality Management. Contribution for the Water
Quality Institute, Danish Academy of Technical Science, No. 3,
H rsholm, Denmark, December 1976.
9. Loftis, J.C. and R.C. Ward. Statistical Tradeoffs in Monitoring
Network Design, presented at AWRA Symposium “Establishment of Water
Quality Monitoring Programs,” San Francisco, California, June 12-14,
1978.
10. Sanders, T.G. and R.C. Ward. Relating Stream Standards to Regulatory
Water Quality Monitoring Practices. Presented at the AWRA Symposium
“Establishment of Water Quality Monitoring Programs,” San Francisco,
California, June 12-14, 1978.
11. Lettenmaier, D.P. Design of Monitoring Systems for Detection of Trends
in Stream Quality. Technical Report No. 39, Charles W. Harris
Hydraulics Laboratory, University of Washington, Seattle, August 1975.
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QUANTITATION OF INDIVIDUAL ORGANIC COMPOUNDS IN SHALE OIL
L.R. Hilpert, H.S. Hertz, W.E. May, S.N. Chesler, S.A. Wise,
F.R. Guenther, J.M. E.rown, and R.M. Parris
Organic Analytical Research Division
National Bureau of Standards
Washington, D.C. 20234
ABSTRACT
A serious and largely unknown complication of developing alternate
fuels such as shale oil is the potentially deleterious impact on the envi-
ronment. Identification and quantitation of toxic organic compounds in the
feedstock, process streams, and plant effluents will become increasingly
important as mutagenicity testing on chromatographic fractions generated
from various fuels and effluents exp2nds. In preparation for certifying a
Standard Reference Material for toxic, constituents in alternate fuels, our
laboratory has been investigating various techniques for quantitating indi-
vidual organic compounds in shale oil. Emphasis has focused on acid-base
extraction and high performance liquia chromatography as independent methods
of shale oil fractionation. Gas chromatographic, gas chromatographic mass
spectrometric, and high performance liquid chromatographic methods have been
used to quantitate several phenols, N-heterocyclics, and polynuclear
aromatic hydrocarbons in shale oil.
INTRODUCTION
In order to enhance the accuracy of environmental measurements asso-
ciated with the development of alternate fuels such as shale oil, the
Organic Analytical Research Division of the National Bureau of Standards is
developing the analytical expertise necessary to certify the concentrations
of several phe’iols, N-heterocyclics, and polynuclear aromatic hydrocarbons
in shale oil and to issue it as a Standard Reference Material (SRM) for
shale oil. The accurate quantitative analysis of individual toxic organic
compounds in alternate fuels will become increasingly important as muta-
genicity testing on chromatographic fractions generated from these fuels and
effluents expands. Without accurate measurement, scientists cannot correct-
ly relate health effects to levels of pollution, engineers cannot correctly
assess the effectiveness of various ccntrol technologies, and the government
cannot correctly make policy decisions which require compromises among
conflicting demands of environmental protection, energy conservation, and
public as well as economic health.
Numerous studies on the qualitative analysis of shale oil and coal
liquids appear in the recent literature. Unden et al.’ characterized th
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acidic and basic fractions of shale oil by GC-FTIR. Dark et al. 2 used HPLC
and LC-MS for the characterization of coal liquefaction products. Clark et
al. 3 used both solvent extraction a d chromatographic techniques for the
isolation of aikanes and polynuclear aromatic hydrocarbons from shale oil.
Jackson et al. 4 characterized hydroc:arbon types in shale oil distillates
with the use of a hydroboration-acid absorption technique. The major
emphasis of these studies has been qualitative, however, rather than accu-
rate quantitative analysis of individual compounds.
In prepartion for certifying a Standard Reference Material for toxic
constituents in alternate fuels, we have been investigating various tech-
niques focused on acid-base solvent extraction and high performance liquid
chromatography as independent methods of shale oil fractionation as a pre-
lude to quantitative determinations of individual compounds by various gas
chromatographic, gas chromatographic—mass spectrometric, and high perfor-
mance liquid chromatographic methods. A comparison of results obtained by
these various methods will be the subject of this paper.
EXPERIMENTAL
Shale Oil Sample
The shale oil analyzed in this work is from a 150-ton retort for in
situ simulated combustion operated by the Laramie Energy Research Center,
Laramie, Wyoming. The shale is from the Mahogany zone of the Colorado Green
River formation. An 8 L sample of this shale oil was obtained by NBS from
oak Ridge National Laboratory, Oak Ridge, Tennessee. The shale oil under-
went centrifugation at Oak Ridge to separate water (%40%) and sludge from
the oil. A subsample of 1 1 was removed from the 8 1 bulk sample. Aliquots
of ‘ 5 ml each were sealed in amber glass ampoules for subsequent analyses.
The samples were analyzed to measure the concentration (pglg) of pyrene,
fluoranthene, benzo(a)pyrene, phenol, o-cresol, 2, 4, 6-trimethylpyridine,
and acridine.
Extraction
Acid-base Extraction--The shale oil sample was separated into three
fractions (acids, bases, and neutrals) using an extraction procedure adapted
from Schmeltz. 5 For the determination of the PAHs, an additional liquid-
liquid partition step using dimethylformamide (DMF)/water and hexane was
utilized to remove the aliphatic hydrocarbons from the PAH neutrals. This
procedure for the isolation of PAHs in complex mixtures has been previously
reported by Bj$rseth. 6
HPLC Extraction--The shale oil simple was diluted ( 0.1 g/ml) prior to
fractionation on a preparative scale minosilane column (30 cm x 7 mm i.d.).
A sample containing from 10-15 mg of shale oil was injected onto the column
using a loop injector. A mobile phase flow rate of 5 ml/min was employed.
Standards of t ie compounds to be determined and the compounds utilized as
internal standards were injected to determine the appropriate elution
uolumes for fraction collection. After collection of the fractions in 15-
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cr 40-mi centrifuge tubes, the fractions were reduced to 50-500 p1 by pass-
ing N 2 over the sample.
Quantitative Analysis--Chromatographic conditions for the LC, GC, and
GC/MS quantitation of individual compounds are summarized in Table 1.
Details of these analyses are reported elsewhere. 7
TABLE 1. CHROMATOGRAPHIC CONDITIONS FOR QUANTITATION OF
INDIVIDUAL SHALE OIL CONSTITUENTS
Gas
Chromatography
Compound Class
Gas
Chromatography
Liquid
Chromatography
Mass
Spectrornetry
Polynuclear
30 m WCOT
Octadecylsilane
30 m SCOT
aromatic
Carbowax 20 M
70/30 CH 3 CN/H 9 0
SE-30 and 30 m
hydrocarbons
and 40-100%
linear gradient
WCOT SE-52
Phenols
30 m WCOT
SP-1000 and
Carbowax 20 M
capillary
Octadecylsilane
40/60 CH 3 CN/H 2 0
6 ft. 0.1%
SP-1000
packed column
N-heterocyclic
30 m WCOT
Octadecylsilane
30 m WCOT
compounds
SP-T000
0-50% linear
gradient CH 3 CN/H 2 0
SP-2100 and
17 m WCOT SE-52
RESULTS AND DISCUSSION
Quantitative determinations of individual components in a complex
matrix such as shale oil require that the sample be cleaned up prior to
analysis to remove nonanalyte interferences. This sample cleanup step has
traditionally involved a solvent extraction step such as that reported by
Schmeltz. 5 This extraction step is a laborious procedure which requires 1-2
man days to generate the acidic, basic, and neutral oil fractions. When a
standard addition technique involving three or four standard additions is
used for quantitation, the time spent on sample preparation becomes prohibi-
tive. Furthermore, once the extraction step has been completed, the sample
must be subjected to a high resolution chromatographic separation to allow
individual components to be quantified free from interferences.
The shale oil sample described above was analyzed for the following
compounds within each class (the specific compounds were arbitrarily select-
ed as being representative of the class of compounds and many are EPA
priority pollutants): Acids-phenol and o-cresol, bases--2, 4, 6-trimethyl-
357
-------
pyridine and acridine, and PAHs--fluoranthene, pyrene, and benzo(a)pyrene.
Quantitative determinations for the compounds in the acid/base solvent
extracted fractions were performed by gas chromatography or, where addition-
al specificity was required, by gas chromatography/mass spectrometry with
selected ion monitoring. The results of these determinations are presented
in Table 2, under the heading “Acid/Base Extraction.”
A novel, rapid method of preparing shale oil fractions for subsequent
quantitative determinations has been developed which involves a high per-
formance liquid chromatographic separation of the shale oil on a preparative
scale aminosilane (pbondapak NH 2 ) column. The compound(s) to be determined
can easily be eluted selectively by modifying the composition of the mobile
phase. By judiciously adjusting tt e mobile phase composition from 100
percent hexane to 100 percent CH 2 C1 2 it is possible to elute a wide range of
compounds from nonpolar PAHs to the more polar phenols and N-heterocyclics.
An example of the HPLC fractionation of shale oil to generate a PAH fraction
is ullustrated in Figure 1. StandarCs of the compound(sO to be determined
are run prior to fractionating the shale oil to determine the appropriate
elution volume for fraction collecti3n. This standard run is seen as the
lower chromatogram in the figure. Depending on the particular compound
being determined and its elution volume, the fraction can generally be
prepared in less than an hour. The shale oil fractions thus obtained were
analyzed for the individual compounds of interest by various high perform-
ance liquid chromatographic, gas chromatographic, and gas chromatographic/
mass spectrometric techniques (see Table 1). The results of these determi-
nations are shown in Table 2. As can be seen, the agreement among values
obtained by independent quantitative techniques is excellent at the 95
percent confidence level. A comparison of results from determinations on
fractions obtained by an acid/base extraction vs. those obtained from the
HPLC generated fractions are also in excellent agreement. Although the
“true” or “actual” concentrations cannot be verified with current state-of-
the-art methodology, the intralaboratory precision obtained using indepen-
dent techniques of shale oil fractionation and quantitation give us
confidence in the results obtained.
For many environmental analyses. there is little or no knowledge of
comparability of data from different laboratories and, in most cases,
probably little knowledge of intralaboratory precision. In order that the
data from different laboratories and methods be useful and reliable, there
must be a basis for intercomparability. Furthermore, unless the quantita-
tive data can be related from one laboratory to the next, environmental
standards can be neither set nor enforced. Research leading to the develop-
ment of Standard Reference Materials and the correct use of these SRMs are
one means for ensuring the comparability of these measurements.
Aliquots of the shale oil sample were sent to several laboratories
currently involved in characterizing alternate fuels. The laboratires were
requested to determine the concentrations of the phenols, N-heterocyclics,
and PAH5 menticned above. Preliminary results of this limited interlabora-
tory exercise are presented in Table 3. The scatter of the results indicate
the variability of state-of-the-art quantitative analyses for individual
358
-------
(n
TABLE 2. SHALE OIL ANALYSIS
(ppm, 95% confidence level)
Compound
LC
HPLC Extraction
GC
Acid/Base Extraction
Quantitation by
GC/MS
Quantitation by
GC GC/MS
Pyrene
108.0 ± 16.0
101.0 ± 4.0 102.0 ± 9.0
94.0 ± 10.0
--
fluoranthene
53.0 ± 6.0
55.0 ± 6.0 62.0 ± 5.0
75.0 ± 5.0
--
Benzo(a)pyrene
21.0 ± 2.8
-- 21.0 ± 50 a
24.0 ± 2.0
Benzo(e)pyrene
--
- - 20.0 ± 6.O
--
22.0 ± 5.0
Phenol
383.0 ± 50.0
387.0 ± 26.0 416.0 ± 28.0
--
334.0 ± 63.0
0-cresol
330.0 ± 34.0
334.0 ± 86.0 350.0 ± 16.0
--
322.0 ± 45.0
2, 4, 6-trimethyl
pyridine
--
912.0 ± 26.0 1214.0 ± 64.0
988.0 ± 56.0
--
Acridine
6.0 ± 2.4
-- 4.4 ± 0.3
alnternal standard of perylene used instead of standard addition.
-------
HPLC Fraetionation of Shale Oil
Figure 1. HPLC Fraetionation of Shale Oil--PAHs
360
-------
TABLE 3. INTERLABORATORY COMPARISON OF RESULTS OF SHALE OIL ANALYSIS
Compound
NBSa
2
3
4
5
6
7
Pyrene
107
155
360
150
168
212
141
Fluoranthene
61
102
220
80
108
104
112
Phenol
395
392
180
--
--
--
399
0-cresol
2, 4, 6-trimethylpyridine
338
1060
350
466
150
460
--
--
--
950
--
--
381
1092
aResults reported by NBS represent the mean of values obtained by GC, GC/MS,
and HPLC.
compounds in a complex matrix. It also stresses the need for a SRM, such as
the shale oil, which laboratories responsible for quantitative measurements
can use to gauge the accuracy of their analytical methods.
CONCLUSIONS
Quantitative determinations for individual organic constituents in a
shale oil have been accomplished using two independent fractionation tech-
niques and various quantitative methods. A novel HPLC technique for the
rapid separation of shale oil into fractions for the analysis of phenols,
N-heterocyclics, and polynuclear aromatic hydrocarbons has been presented.
Intralaboratory precision for the determination of single species concentra-
tions in a shale oil was excellent, ard is expected to result in the release
of a shale oil SRM in the near future, to be certified for several compounds
in each class. The results of a preliminary interlaboratory exercise on the
quantitative determinations of individual compounds in shale oil emphasize
the need for such a standard.
In order to specify procedures adequately, it has been necessary to
identify some commercial materials ii this report. In no case does such
identification imply recommendation or endorsement by the National Bureau of
Standards, nor does it imply that the material identified is necessarily the
best available for the purpose.
REFERENCES
1. Uden, P.C., Carpenter, A.P. , Jr., Hackett, H.M. , Henderson, D.E. , and
Siggie, S. , Quantitative Analysis of Shale Oil Acids and Bases by
Porous Layer Open Tubular Gas Chromatography and Interfaced Vapor Phase
Infrared SpectrophotometrY, Anal. Chem. , 51:38 -43 (1979).
361
-------
2. Dark, W.A., McFadden, W.H., and Bradford, D.L., Fractionation of Coal
Liquids by HPLC with Structural Characterization by LC-MS, J. Chrom.
Sci., 15:454-460 (1977).
3. Clark, B.R., Ho, C.-h., and Jones, A.R., Approaches to Chemical Class
Analyses of Fossil Derived Materials, ACS Division of Petroleum
Chemistry, March 1977.
4. Jackson, L.P., Alibright, C.S., and Jensen, H.B., Characteristics of
Synthetic Crude Oil Produced by In Situ Combustion Retorting, ACS
Preprints, Div. of Fuel Chem., 19(2):175-182 (1974).
5. Schmeltz, I., Phytochem., 6:33 (1967).
6. Bj rseth, A., Anal. Chem. Acta., 94:21 (1977).
7. Hertz, H.S., May, W.E., Hilpert, L.R., Chesler, S.N., Wise, S.A.,
Guenther, F.R., Brown, J.M., and Parris, R.M., manuscript in prepara-
tion.
362
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ISOLATION AND IDENTIFICATION OF ORGANIC RESIDUES
FROM PROCESSED OIL SHALE
D.L. Maase, V.D. Adams, D.B. Porcella, and D.L. Sorensen
Utah Water Research Laboratory
Utah State University
Logan, Utah 84322
ABST RACT
The purpose of this study was to identify characteristics of organic
residue from processed oil shale. Processed oil shale samples from the
Union B, Paraho and TQSCO II processes have been extracted by using organic
solvents in a soxhiet apparatus and by mixing shale samples with water.
Sample extraction residues were identified by gas chromatography coupled
with mass spectrometry (GC/MS).
I NTRODUCT ION
Western shale organic content estimates have been reported by USGS and
BIN since the turn of the century. More recently, because of increasing
attention to this fossil fuel reserve, methods and estimates of oil extract-
ability and characterizations of organic constituents have been reported.
In the past decade many investigations of oil shale kerogens and bitumens
have focused on macroconstituent determinations. Others report development
of laboratory regimes designed to isolate and identify the organic residue
from processed oil shale. More intensive investigations of the organic
matrix of products and wastes derived from shales, tar sand, coal, and high
boiling crude oil distillates are also reported. Isolation methods have
included liquid/liquid (L/L) extractions, thin layer chromatography (TLC),
and liquid column chromatography (LC). Extraction and elution solvents
employed have ranged from low polarity solvents (i.e., benzene, cyclohexane,
hexane(s), pentane) through polar solvents (i.e., acetone, ethanol, metha-
nol, CH 2 C1 2 , CHC1 3 ). Basic, neutral and acidic isolation conditions and
TLC/LC media have been used. Reported identification methods employed
include NMR, MS, IR, HPLC, GC, and GC/MS. A selection of these investiga-
tions are summarized in tables appended to this paper (Tables A1-A5 includ-
ing associated references).
METHODS AND PROCEDURES
pproach
Two approaches for extracting organics from spent shales were employed.
First, organic solvents following classical organic chemistry procedures
3&3
-------
were used to extract and concentrate the organic materials remaining in t ,e
processed shales. Second, water was used for extraction. The water
extracted organics were either sorbed by a resin and eluted with polar
solvents or partitioned to organic solvents by liquid/liquid extraction
prior to concentration and GC/MS identification of constituents.
A low level of light was maintained during these laboratory procedures.
All laboratory equipment utilized was made of glass, metal or Teflon.
Reusable glassware was washed in four organic solvents of decreasing polari-
ty, acid/base washed, distilled water rinsed and oven dried. Extraction and
concentration glassware was heated for one hour at 550°C.
Organic Extraction
As cited in the literature, soxhlet extraction solvents have included
redistilled (in glass) pentane, cyclohexane, benzene, and benzene:methanol
mixtures. The more polar concentrated methanol extracts have been used
directly in the Ames test for mutagenicity. (Presentation of this research
is included in the symposium paper entitled “Detection of Chemical Mutaqens
in Spent Oil Shale Using the Ames Test [ Dickson et al.].)
The soxhlet extraction method was employed to develop extracts for
GC/GC-MS investigations. 400 grams of processed oil shale samples were.
charged to each soxhlet and 1.2 .Q. leaching solvent was used. Sample
characterization, leaching solvent, and extraction conditions utilized are
summarized in Table 1.
Each soxhlet extraction sample was divided in half. One part was then
concentrated by flash evaporation (Buchier Instruments) and the other part
concentrated by Kuderna Danish heat evaporation (500 ml Kontes with a 10 ml
concentration tube and macro Snyder condenser). The concentrated solutions
( 1O ml) were then further concentrated to 4 ml at room temperature using a
gentle stream of nitrogen.
Thin layer chromatography (TLC) was utilized to fractionate some of the
concentrated soxhiet extracts. Concentrated extracts equivalent to a
soxhiet charge of 1.0 kg were applieo to silica gel plates (EM Laboratories
Inc., 20 cm x 20 cm x 2 mm, PLC 60F-254 Plates); and developed in benzene:
cyclohexane (3:2). Separated compounds were visualized by means of an
ultraviolet hand lamp. As observed in ultraviolet light (“.254 hv), the ten
compounds included in a standard PAll mixture resolved into five fractions.
Based on the TLC resolution of the PAt-f standard mixture, the resolved sample
TLC plates were fractionated as <0.1, 0.1-0.25, 0.25-0.6, 0.6-0.8 and >0.8
of the developed solvent front (TLC R values). The silica gel as fraction-
ated was scraped from the TLC plat s and the associated resolved sample
components were eluted in methanol. To ensure a maximum recovery of the
sample components from the scraped silica gel, fractions were sonicated
(Bronwill/VWR Scientific) to homogenize the methanol silica gel mixtures.
The silica gel was removed from these emulsions by filtration at “ .0.5 atmos-
pheres (GFC filters). The filtered sample fractions were concentrated in a
364
-------
TABLE 1. CHARACTERIZATION OF EXAMPLE SAMPLES AND SOXHLET EXTRACTION OPERATING CONDITIONS
Shale
Sample Extracted
Soxhlet Operation
Time
Sample &
%
Benzene
(Days)
Treatment
Nomatica
Sieve
Fraction
%
b Vola—
I2O tilesC
Leachab es
(ppm)
Leach
Solver t
@ 3 min/
cycle
GCIGC Trace/Spectrum
Figure(s) and/or Comments
Parahoe <114” 0.5 14 49,600 Pentane 3 TAR Problem
D5ARe
TOSCO <1 mm 1.9 9 1,900 Methanol 3 Mutagenicity of this extract
D3BKd after reported by Dickson et al.
Benzene this symposium
TOSCO <1 mm 1.9 9 1,900 Benzene 3 Figure 1 Chromatography
DcAR
Paraho <1 mm 1.5 7 150 Berizene 4 Figure 2 Chromatograph of
Dkd 60-80 TLC TLC fraction 0.6-0.8 Rf
Methanol
Union <1 mm 1.3 15 77,300 Benzene These extracts could not be
D l i crushed and concentrated to less 10 m2.
Benzene 3 due to TAR matrix
Methanol
aThe shale samples are identified by process source but are representative only of early surface
retorting investigations and should not be considered reflective of commercial scale processed
dshales.
Determined by further drying of ground air dried samples at 103°C until approximately constant
weight (total samples = 30, maximum CV = 4%).
Determined by heating dried samples (from b above) at 550°C until approximately constant weight
d(total samples 35, maximum CV 7%). Includes ammonia, C0 2 , etc. as well as organics.
ppm; parts dried benzene leachables per million parts leached shale (by weight). Data is comparable
et0 Schmidt-Collerus (1974) procedure.
Paraho samples from different intermediate sources.
-------
nitrogen atmosphere. The separated TLC silica gel showed no fluorescence
under the UV hand lamp.
Water Extractions
A 55 gallon Teflon lined drum was used as a mixing chamber for the
water extractions. Mixing run compositions varied from 5 t.o 50 kilograms
dry spent shale (<1 mm sieve) and from 10 to 100 liters of water. After
mixing (from 2 to 12 hours) and investigation of post mixing physical settl-
ing characteristics ( ‘.90 mm), 20 liters of near surface water was drawn off
and filtered (Whatman Qualitative #2).
Extracted organics in these filtered water samples were sorbed with
nonionic sorption resin (XAD-2). A CH 2 C1 2 liquid/liquid extraction routine
was used as a comparison extraction method (EPA, 1977). Preliminary inves-
tigations using distilled water and known concentrations of PAH indicate
that XAD sorption and Lu extraction transfer efficiencies were comparable.
Webb (1975) reported that the differences between XAO-2 and CHC1 3 extraction
efficiencies were less than 10 percent for the low molecular weight PAH
compounds that he studied.
For preparation of the XAD-2 resin, 3 soxhiet extractions of 8 hours
each were required. The XAD-2 resin was placed in a soxhiet and extracted
first with acetonitrile, then ether, and finally with methanol. The resin
was stored under methanol as required.
A glass chromatography column (10 x 1 cm id) with a 20 liter delivery
tank was used for the XAD-2 resin. Silanized glass wool plugs were used on
each side of the XAD-2 column resin pack. After activation of the resin
with 40 ml deionized water (‘Milli Q System”), 20 liter sample water
extracts were passed through the column at 30 mi/mm. Stepan and Smith
(1977) report that higher PAH sorption efficiencies are possible with lower
column flow rates than used here. The column resin was eluted with 30 ml of
ether. MgSO 4 was used to remove water from the ether elutants. The water
free ether elutions were then concentrated to 0.1 ml in a gentle stream of
nitrogen. A detailed description of XAD preparation and column operations
is presented in Junk et al. (1974).
GC/MS and GC Identification
Organics in the extracted and concentrated samples were identified with
a Hewlett-Packard gas chromatograph-mass spectrometer (HP 5985 GC/MS
System). A 10 meter glass capillary column coated with 5P2100 was tempera-
ture programmed from 90°C to 250°C at. 5°/mm to resolve sample components.
The mass spectrometer ionization voltage was maintained at 70 ev. Injection
port and transfer line temperatures were 250° and 275° respectively.
A HP-5750 gas chromatograph fitted with a 180 cm x 0.3 cm stainless
steel column packed with 10 percent SP2100 on 80/100 mesh Supelcoport was
used for sampie screening work. This column performed adequately yet at
higher operating temperatures (>250°) column bleed was excessive. A liquid
366
-------
crystal packed column was also used to resolve a standard PAH mixture for
comparison witt the above study columns. The liquid crystal column yielded
excellent resolution, even of the l rger molecular weight isomers. GC!MS
library identifications were possible with injections equivalent to less
than 1 ng for each PAH in the standard mixture. However, according to the
literature review, liquid crystal columns have not been used to resolve
complex environmental samples (see appendix).
RESULTS AND DISCUSSION
The higher organic content shale samples investigated in this study
produced a tarry matrix during extractions with benzene, cyclohexane, pen-
tane or with benzene:methanol mixtures. The celulose soxhiet thimbles can
have “tar” plugging problems. Concentration of these tarry extracts often
led to the development of an asphaltic-like tar complex. Similar tar
extraction and concentration problems have been reported by researchers
working with raw oil shales, bitumens, kerogens and related substitute crude
oils. Various laboratory methods have been devised to investigate tar
matrices (see appendix tables). Consequently, the isolation and identifica-
tion of organic residue from processed oil shale reported in this paper does
not include samples with tar matrices capable of limiting concentration of
extracts to <4 ml. The samples are also reflective of the lower organic
content processed shales.
Kuderna-Danish heat evaporation and vacuum evaporation allow quantif i-
able organic separations. Comparison of GC traces of split extracts concen-
trated by these methods show slight differences in relative peak responses.
Junk and coworkers (1974) have noted differential concentration efficiencies
of low molecular weight PAH when comparing differing Kuderna-Danish designs.
CC/MS of Soxhiet Extracts
A sample of benzene soxhiet extract has been resolved into more than
120 peaks as shown in Figure 1. The GC trace was obtained from the equiva-
lent to the benzene leachables from about 50 mg of a shale sample (see Table
1 for description of sample characteristics and soxhiet extraction condi-
tions). Alkanes from C 11 to C 30 were identified in the benzene leachables
by interpretation of mass spectra data (Table 2). Also the following aro-
matics were identified. Peaks 1, 2, 3 are alkyl substituted benzenes; peaks
22 to 27 and 38, 39, and 42 and 53 have been identified as alkyl substituted
naphthacenes. Alkyl substituted pher.anthrenes (peaks 73, 74) and pyrenes
(peaks 85, 86) were identified. Peak 67 has been tentatively identified as
elemental sulfur.
CC/MS of TLC Fractions
A chromatographic trace of a standard PAH mixture and an example of a
0.6 to 0.8 Rf TLC fraction concentrate GC/MS injection are compared in
Figure 2. This injection is equivalent to the benzene leachables from about
2.5 g of shale developed as described in Figure 3. Single ion mass spectrum
reconstructions indicate the presence of three, four, and five ring aromat c
367
-------
riewlect—t-acKarcl gas chromatograph—mass spectrometer (HP 5985 CC/MS system)
10 meter glass capillary column coated with SP2100
Temperature programmed from 9Q0 to 2500 centigrade at 5°/mm
(See Table 1 for sample and workup characterizations)
4
58
41
29
(A)
OD
52
71
7?
84 89
94
97 101
104 106
(mm)
113 120
I I I I I I I I 1 1 I I 1 1 I I 2% I I I 3b I I I
Figure 1. Gas Chromatography of Benzene So;hlet Leachables.
-------
Table 2. IDENTIFIED BENZENE LEACHABLES
1 C 10 H 14 134
2 C 10 H 14 134
3 C 11 11 22 154
and C 10 H 14 134
4 C 11 11 24 156
5 C 10 H 12 132
7 C 11 H 16 148
8 C 10 H 8 128
9 C 11 H 14 146
10 C 12 H 24 168
11 C 12 H 26 170
12 C 13 H 28 184
16 C 11 H 10 142
17 C 11 H 10 142
18 C 13 11 26 182
20 C 13 H 28 184
22 C 13 11 18 174
23 C 12 1 1 10 154
25 C 12 1 1 12
26 C 12 H 12 156
27 C 12 11 12 156
28 C 14 H 28 196
29 C 14 H 30 198
30 C 12 H 12 156
36 ? ?
37 ? ?
38 C 13 H 14 170
Peak Molecular
Formula Compound
Number Weight
Benzene, di— or tri—alkyl substituted
Benzene, di— or tri—alkyl substituted
1 —undecene
Benzene, di— or tri—alkyl substituted
Und ec ane
Benzene, di—alkyl-alkenyl--substituted
Benzene, alkyl substituted
Naphthalene or azulene
1 —dodecene
Dodecane
Undecane, dimethyl
Naphthalene, methyl
Naphthalene, methyl
1—tridecene
Tridecane
Naphthacene, 1,2,3,4—tetra hydro—
tri—alkyl substituted
1,1’B,phenyl or acenaphthycene,
1, 2—dihydro
Naphthacene, mono— or di—alkyl
substituted
Naphthacene, mono— or di—alkyl
substituted
Naphthacene, mono— or di—alkyl
substituted
I —tetradecene
Te tradecane
Naphthacene, mono- or di—alkyl substituted
Alkane, substituted
Alkane, substituted
Naphthacene, trimethyl substituted
156
369
-------
Table 2. CONTINUED
Peak Molecular
Formula Compound
Number Weight
39 C 13 H 14 170 Naphthacene, trimethyl substituted
40 C 15 H 30 210 1—pentadecene
41 C 15 H 32 212 Pentadecane
42 C 13 H 14 170 Naphthacene, trimethyl substituted
43 C 13 H 14 170 Naphthacene, trimethyl substituted
44 C 13 H 14 170 Naphthacene, trimethyl substituted
46 C 14 H 16 184 Naphthacene, alkyl substituted
47 ? Alkane, substituted
48 ? ? Alkane, substituted
49 ? 182
51 C 16 H 32 224 1—hexadecene
52 C 16 H 34 226 Hexadecane
53 C 14 H 16 184 Naphthacene, alkyl substituted
55 Alkane, substituted
56 196
57 C 17 H 34 238 1—heptadecene
58 C 17 H 36 240 Heptadecane
59 C 19 H 40 268 Alkane, substituted
60 ? ? Alkane, substituted
61 ? ? Alkane, substituted
63 C 13 H 10 S 198 Dibenzothiophene, methyl substituted
64 C 18 H 36 252 1—octadecene
65 C 18 11 40 254 Octadecane
66 ? ? Alkane, substituted
and 192 Phenanthrene or anthracene, methyl
substituted
67 S 8 Sulfur ?
70 C 19 H 38 266 1—nonadecene
71 C 19 H 40 268 Nonadecane
73 C 16 H 14 206 Phenanthrene, dimethyl substituted
74 C 16 H 14 206 Phenanthrene, dimethyl substituted
370
-------
Table 2. CONTINUED
Peak Molecular
Formula Compound
Number Weight
76 C 20 H 40 280 1—eicosene
77 C 20 H 42 282 Eicosane
83 C 21 H 42 294 1—heneicosene
84 C 21 11 44 296 Heneicosane
85 C 17 H 12 216 Pyrene, methyl substituted or
I 1H—benzo [ a]fluorene
86 C 17 H 12 216 Pyrene, methyl substituted
88 C 22 H 44 308 1—docosene
89 C 22 H 46 310 Docosane
93 C 23 H 46 322 1—tricosene
94 C 23 H 48 324 Tricosane
96 C 24 11 48 336 1—tetracosene
97 C 24 H 50 338 Tetracosane
100 C 25 H 50 350 1—pentacosene
101 C 25 H 52 352 Pentacosane
103 C 26 H 52 364 1—hexcosene
104 C 26 H 54 366 Hexcosane
105 C 27 H 54 378 1—heptacosene
106 C 27 H 56 380 Heptacosane
107 253
108 217
109 C 28 H 56 392 1—octacosene
110 C 28 H 58 394 Octacosane
112 ? 217 ?
113 C 29 H 60 408 Nonacosane
114 217 ?
115 217 ?
117 ? 217 ?
119 C 30 H 62 422 ?
371
-------
INJ
TLC 0.6 - 0.8 Rf Fraction
4 ring PAH O 228
mw) knowns
Triphenylene
Benz(a)anthracene
Chrysene
3 ring PAH ("'••' 178 mw) knowns
Phenanthrcne
Anthracene
(min)
Figure 2. Gas chromatography comparison of a TLC 0.6-0.8 fraction with known PAH.
-------
Known PAH
mixture
Desorbed in
methanol concen—
trated to 0.14 ml.
2.1 il injected to
GC/MS. CC on Figure
3.
Benzene extract from
200 g sample concen-
trated to 4 ml. 1.
.il to GC/MS. CC on
Figure 1.
Table 3. SUMMARY OF PAils IDENTIFIED IN TLC 0.6 to 0.8 Rf FRACTION
Number
Aromatic
Rings
GC/MS
Identif led
Compound
Molecular
wt.
Formula
Retention
Time
(mm)
3
anthracene
178.1
C 14 H 10
10.2
3
phenanthrene
178.1
C 14 H 10
10.4
4
pyrene
202.1
C 16 H 10
15.6
4
fluoranthene
202.1
C 16 H 10
16.3
4
benzo(a)anthracene
chrysene
triphenylene
benzo(c)phenanthrene
228.1
C 18 H 12
22.0
Solvent front
Resolved PAIl
Constituents
TLC Plate
x b1 j hclou J
x
x
x
x
o Concentrated Sample
(Rf)
1.0
0.8
0.6
0.25
0.1
0
Solvent Development
Benzene : Cyclohexane
(3:2)
4
TLC of e tracts from 1 kg processed shale
Kuderna Concentration Vacuum
Danish Methods Evaporation
Soxhlet Extractions
Solvent and Operation Conditions Described in Table 1
Figure 3. Summary of organic extraction regime.
373
-------
252.1 iiiw
228.1 mw
4 ring PAH (‘\‘ 202 mw) knowns
F! uoranthene
Pyrene
4 ring PAH ( 228 mw) knowris
Triphenylene
Benz (a)an thracene
Chrysene
-11 . 1 1 9 2 2’3 2’4 2 9
202.1 mw
(A)
Shale/Water Mix XAD—2 Developed GC
5 ring PAH (r , 252
mw) knowns
Benzo (e)pyrene
Perylene
B nzo (a) pyrene
Figure 4.
Gas chromatography comparison of a XAD-2 developed water extract with known PAM.
-------
hydrocarbons. From mass spectrum information the ring compounds are further
identified as shown in Table 3. The four ring 228 mw PAH, benz(a)anthra-
cene, chrysene and triphenylene, could not be separately identified by the
GD/MS computer library. The mass spectrum of benzo(c)phenanthrene was
identified. Five ring aromatics (benz a and e pyrenes and perylene) were
indicated by GC retention time comparison with gas chromatography of the
standard PAR mixture. However, insufficient concentrations of the five ring
aromatics were present above background to allow identification of mass
spectra.
GC/MS of Water Extracts
A portion of a GC trace of a XAD-2 developed processed shale water mix
is compared to the PAH standard in Figure 4. Three ring 178 mw and four
ring 202 molecular weight PAR were indicated by retention time and identi-
fied by comparison of GC/MS library mass spectra. Only a weak presence of
four ring 228 mw PAH is indicated by single ion reconstruction GC/MS traces.
The five ring 252 mw benzo(e)pyrene, perylene and benzo(a)pyrene seem to be
extractable by the shale/water mixing technique. However, the concentration
above background of the four and five ring aromatics were not high enough to
allow comparison of mass spectra. The larger peaks shown on this GC trace
are believed to be phthalic esters.
REFERENCES
Acheson, M.A. , R.M. Harrison, R. Perry, and R.A. Wellings. 1976. Factors
affecting the extraction and analysis of polynuclear aromatic hydrocar-
bons in water. Water Research Vol. 10, pp. 207-212.
Adams, J. , K. t4enzies, and P. Levins. 1977. Selection and evaluation of
sorbent resins for the collection of organic compounds. EPA-600/7-77-
004. April.
Alben, K.T. 1979. GC-MS analysis of potable water for evidence of contam-
ination by coal tar compounds used in storage tank coatings. ACS
Division cf Environmental Chemistry. Preprints of papers presented at
the 177th National Meeting, Vol. 19, No. 1. April.
Brown, W.D., L.S. Ramos, and W.D. MacLood, Jr. 1978. Comparison of extrac-
tion methods for hydrocarbons in marine sediment. AIChE Division of
Petroleum Chemistry, Vol. 23, No. 3. August.
Bunger, J.W. 1977. Techniques of analysis of tar sand bitumens. Symposium
of Analytical Chemistry of Tar Sands and Oil Shale Presented Before the
Division of Petroleum Chemistry, American Chemical Society, New
Orleans, Vol. 22, No. 2. March 20-25.
C.-h. Ho, B.R. Clark, M.R. Guerin, C.Y. Ma, and T.K. Rao. 1979. Aromatic
nitrogen compounds in fossil fuels--a potential hazard? ACS Division
of Environ iental Chemistry. Preprints 177th National Meeting, Vol. 19,
No. 1. April.
375
-------
Callen, R.B. , C.A. Simpson, and J.G. £;endoraitis. 1977. Analytical charac-
terization of solvent refined coal comparison with petroleum
residue. Symposium on Analytical Chemistry of Tar Sands and Oil
Shale Presented Before the Division of Petroleum Chemistry, Inc.
American Chemical Society, New Orleans, Vol. 22, No. 2. March
20-25.
Cautreels, W. , and K.V. Cauwenberghe. 1977. Fast quantitative analysis of
organic compounds in airborne particulate matter by gas chromatography
with selective mass spectrometric detection. Journal of Chromatogra-
phy, Vol. 131, p. 253.
Chriswell, C.D., R.L. Ericson, G.A. Junk, K.W. Lee, J.S. Fritz, and H.J.
Svec. 1977. Comparison of macroreticular resin and activated
carbon as sorbents. JAWWA 69(12):669-674. December.
Clark, B.R., Ho C.-h., and A.R. Jones. 1977. Approaches to chemical class
analyses of fossil derived minerals. Symposium on Analytical Chemistry
of Tar Sands and Oil Shale Presented Before the Division of Petroleum
Chemistry, Inc., American Chemical Society, New Orleans, Vol. 22, No.
2. March 20-25.
Coomes, M.R. 1978. Carcinogenic aspects of oil shale. Presented at the
American Nuclear Society Environmental Aspects of Non-Conventional
Energy Resource 11. Topical Meeting. September 26-29.
Cotter, J.E., C.H. Prien, J.J. Schmidt-Collerus, D.J. Powell, R. Sung, C.
Habinicht, and R.E. Pressey. 1978. Samplings and analysis research
program at. the Paraho Shale Oil Demonstration Plant. USEPA-600/7-78-
065. April.
Cummins, J.J., and WE. Robinson. 1972. Thermal degradation of Green River
kerogen at 150°C to 350°C rate cif product formation--RI 7620. U.S.
Department of the Interior, Bureau of Mines, Washington, D.C. March.
Cunwnins, J.J. , F.G. Doolittle, and W.E. Robinson. 1974. Thermal degrada-
tion of Green River kerogen at 150°C to 350°C composition of products
--RI 7924. U.S. Department of the Interior, Bureau of Mines,
Washington, D.C.
Daisey, J.M., nd M.A. Leyko. 1979. Thin-layer gas chromatographic method
for the determination of polycyclic aromatic and aliphatic hydrocarbons
in airborne particulate matter. Analytical Chemistry, Vol. 51, No. 1.
January.
Dickson, J.D., V.D. Adams, D.B. Porcella, D.L. Sorensen, and J.H. Manwaring.
1979. Detection of chemical mutagens in spent oil shale using the Ames
test. Oil Shale Sampling Analysis and Quality Assurance Symposium,
Denver, Colorado. March 26-28.
376
-------
Dunlap, W.J., J.F. McNabb, M.R. Scaif, and R.L. Cosby. 1977. Sampling for
organic chemicals and microorganisms in the subsurface. EPA Robert S.
Kerr Environmental Research Laboratory, Ada, Oklahoma. August.
EPA. 1977. Sampling and analysis procedures for screening of industrial
effluents for priority pollutants. U.S.E.P.A., Environmental and
Monitoring Support Laboratory. Cincinnati. Ohio.
Farrington, J. 1978. An overview of the biochemistry of fossil fuel hydro-
carbons in marine/aquatic environment. Presented Before the Division
of Petroleum Chemistry, Inc. , American Chemical Society, Miami Beach.
September 10-15.
Fruchter, J.J., J.C. Laul, M.R. Peterson, and P.W. Ryan. 1977. High pre-
cision trace element and organic constituent analysis of oil shale
and 5 olvent refined coal minerals. Symposium on Analytical
Chemistry of Tar Sands and Oil Shale Presented Before the Division
of Petroleum Chemistry, Inc. , American Chemical Society, New
Orleans, Vol. 22, No. 2. March 20-25.
Gallegos, E.J. 1973. Identification of phenylcycloparaffin alkanes and
other monoaromatics in Green River shale by gas chromatography-
mass spectrometry. Anal. Cr em. Vol. 45, No. 6, p. 1399. July.
Giger, W. , and M. Blumer. 1974. Polycyclic aromatic hydrocarbons in the
environment isolation and characterization by chromatography, visible,
ultraviolet and mass spectrometry. Anal. Chem. Vol. 46, No. 12, p.
1662. October.
Greinke, R.A. , and I.C. Lewis. 1975. Development of a gas chromatographic-
ultraviolet absorption spectrometric method for monitoring petroleum
pitch volatiles in the environment. Anal. Chem. Vol. 47, No. 13.
November.
Grant, D.W., and R.B. Meiris. 1977. Application of thin-layer and high
performance liquid chromatography to the separation of polycyclic
aromatic hydrocarbons in bituminous materials. Journal of Chromatogra-
phy Vol. 142, p. 339.
Guerin, M.R. 1977. Energy sources of polycyclic aromatic hydrocarbons.
Oak Ridge National Laboratory.
Hill, H.H., K.W. Chan, Jr., and F.W. Korasek. 1977. Extraction of organic
compounds from airborne particulate matter for gas chrornatographic
analysis. Journal of Chromatography Vol. 131, p. 245.
Jacobson, l.A.., Jr.., AS. Decora, and G.L. Cook. 1974. Retorting indexes
for oil shale pyrolyses from ethylene-ethane ratio of product gases--RI
7921. U.S. Department of the Interior, Bureau of Mines, Washington,
D.C.
377
-------
John, E.D., and G. Nickles. 1977. Gas chromatograph-ic method for the
analysis of major polynuclear aromatics in particulate matter. Journal
of Chromatography Vol. 138, p. 399.
Jones, A.R., M.R. Guerin, and B.R. Clark. 1977. Preparative-scale liquid
chromatographic fractionation of crude oils derived from coal and
shale. Anal. Chem. Vol. 49, No. 12, p. 1766. October.
Jones, P.W., R.J. Jakobsen, P.E. Strup, and A.P. Graffeo. 1978. Chemical
characterization of shale oil and related fuels. AIChE Division of
Fuel Chemistry Symposium Oil Shale, Tar Sands and Related Material,
Vol. 21, No. 6. August.
Junk, G.A., J.J. Richard, M.D. Grieser, D. Witiak, J.L. Witiak, M.D.
Arguello, R. Vick, H.J. Svec, J.S. Fritz, and G.V. Calder. 1974. Use
of macroreticular resins in the analysis of water for traces of organic
contaminants. Journal of Chromatography Vol. 99, pp. 745—762.
Kwan, J.T. , J.I.S. Tang, W.H. Wong, and T.F. Yen. 1977. Application of
liquid chromatography to monitor biological treatment of oil shale
retort water. Symposium on Analytical Chemistry of Tar Sands and Oil
Shale Presented Before the Division of Petroleum Chemistry, Inc.,
American Chemical Society, New Orleans, Vol. 22, No. 2. March 20-25.
Lao, R.C., R.S. Thomas, and J.L. Monkman. 1975. Computerized gas chroma-
tographic-mass spectrometric analysis of polycyclic aromatic hydrocar-
bons in environmental samples. Journa1 of Chromatography Vol. 112, p.
681.
Lee, M.L., and M. Novotny. 1976. Gas chromatography/mass spectrometric and
nuclear magnetic resonance determination of polynuclear aromatic hydro-
carbons in airborne particulates. Anal. Chem. Vol. 48, No. 11, p.
1567. September.
Lee, M.L., and R.A. Hites. 1976. Cr aracterization of sulfur-containing
polycyclic aromatic compounds in carbon blacks. Anal. Chem. Vol. 48,
No. 13. November.
Leenheer, J.A. 1979. Study of sorption of complex organic solute mixtures
on sediment by dissolved-organic--carbon fractionation analysis. Divi-
sion of Environmental Chemistry, American Chemical Society, Preprints,
Presented at the 177th National Meeting. April.
May, W.E., S.P. Wasik, and D.H. Freeman. 1978. Determination of the
aqueous solubility of polynuclear aromatic hydrocarbons by a coupled
column liquid chromatographic technique. Anal. Chem. Vol. 50, No. 1.
January.
May, W., and S.P. Wasik. 1978. Determination of the solubility behavior of
some polycyclic aromatic hydrocarbons in water. Symposium on Analyti-
cal Chemistry of Petroleum Hydrocarbons in Marine/Aquatic Environment.
378
-------
Presented Before the Division of Petroleum Chemistry, Inc., American
Chemical Society, Miami Beach, Vol. 23, No. 3. September 10-15.
Natusch, F.S., and B.A. Tomkins. 1978. Isolation of polycyclic organic
compounds by solvent extraction with dimethyl sulfoxide. Anal. Chem.
Vol. 50, No. 11. September.
Pancirov, R.J., T.D. Sean, and R.A. t3rown. 1978. Methods of analysis for
polynuclear aromatic hydrocarbons in environmental samples. AIChE
Division of Petroleum Chemistry Vol. 23, No. 3. August.
Pellizzari, Edo D. 1978. Identification of components of energy-related
wastes and effluents. EPA-600/7-78-004. January.
Pierce, R.C., and M. Katz. 1975. Dependency of polynuclear aromatic hydro-
carbon cor.tent on size distribution of atmospheric aerosols. Environ-
mental Science and Technology Vol. 9, No. 4. April.
Pitts, J.N., Jr., R.A. Van Cauwenberghe, D. Crosjean, J.P. Schmidt, D.R.
Fitz, W.L. Belser, Jr., G.B. Kni dsen, and P.M. Hyuds. 1978. Atmos-
pheric reactions of polycyclic aromatic hydrocarbons: Facile formation
of mutagenic nitro derivatives. Science Vol. 202. November.
Robinson, W.E., and G.L. Cook. 1971. Compositional variations of organic
material cf Green River oil shale-Colorado No. 1 core--RI 7492. U.S.
Department of the Interior, Bureau of Mines, Washington, D.C. March.
Robinson, W.E., and G.L. Cook. 1973. Compositional variations of organic
material from Green River oil shale-Wyoming No. 1 core--RI 7820. U.S.
Department of the Interior, Bureau of Mines, Washington, D.C.
Rubin, I.B., M.R. Guenin, A.A. Hardigree, and J.L. Epler. 1976. Fractiona-
tion of synthetic crude oils from coal for biological testing. Envi-
ronmental Research Vol. 12, pp. 358-365.
Saxby, J.D. 1976. Chemical separation and characterization of kerogen from
oil shale. In: Oil Shale, Yen, 1976, Ch. 6.
Schmidt-Collerus, J.J. 1974. The disposal and environmental effects of
carbonaceous solid wastes from commercial oil shale operations.
N tional Science Foundation, WasI ington, D.C. January.
Schweighardt, F. K. , and B.M. Thomas. 1978. Solvent extraction of coal-
derived products. Anal. Chem. Vol. 50, No. 9. August.
Schaup, N. , and F. Van Wassenhoue. 1972. Determination of benzo(a)pyrene
in bitumen and plants. Journal of Chromatography Vol. 69, p. 421.
Schiller, J.E. , and D.R. Mathiason. 1977. Separation method for coal-
derived solids and heavy liquids. Anal. Chem. Vol. 49, No. 8. July.
379
-------
Selucky, M., T. Ruo, Y. Chu, and O.P. Strausz. 1977. Chromatographic
studies on oil shale bitumens. Symposium on Analytical Chemistry of
Tar Sands and Oil Shale Presented Before the Division of Petroleum
Chemistry, Inc., American Chemic3l Society, New Orleans Meeting, Vol.
22, No. 2. March 20-25.
Sharkey, A.G.. J.L. Schultz. C. White, and R LetL 1976. Analysis of
polycyclic organic material in coal, coal ash, fly ash and other fuel
and emission samples. EPA. Industrial Environmental Research Labora-
tory, Research Triangle Park, North Carolina. March.
Shuang-Ling, Chong, J.J. Cummins, and W.E. Robinson. 1976. Fractionation
of soluble extracts obtained from kerogen thermal degradation with CO
and H 2 O. 172nd National Meeting Div. Fuel Chemistry ACS Symposium on
Oil Shale, Tar Sands and Related Material, Vol. 21, No. 6. Fall.
Solash, 1., and R.F. Taylor. 1976. Characterization of aromatic fractions
from nonpetroleum derived JP-5 type fuels. 172nd National Meeting,
Division Fuel Chemistry ACS Symposium on Oil Shale, Tar Sands and
Related Material, Vol. 21, No. 6. Fall.
Spath, D.P. 1972. The chlorination of coal tar derivatives in water.
Dissertation. Department of Civil and Environmental Engineering,
University of Cincinnati.
Stepan, S.F., and J.F. Smith. 1977. Some conditions for use of macroretic-
ular resins in the quantitative analysis of organic pollutants in
water. Water Research Vol. 11, pp. 339-342.
Thomas, R.D., and P.B. Lorenz. 1970. Use of centrifugal separation to
investigate how kerogen is bound to the minerals in oil shale. Report
of Investigations, 7378. U.S. Department of the Interior, Bureau of
Mines, Wasnington, D.C. April.
tJden, P.C., AP. Carpenter, 1 -IM. Hackett, D.E. Henderson, and S. Siggia.
1979. Qualitative analysis of shale oil acids and bases by porous
layer open tubular gas chromatography and interfaced vapor phase infra-
red spectrophotometry. Anal. Chem. Vol. 51, No. 1. January.
Webb, R.G. 1975. Isolating organic water pollutants XAD resins, urethane
foams, solvent extraction. EPA-660/4-75-003. June.
Yen, T.F. 1976. Science and technology of oil shale. Ann Arbor Science
Publishers, 230 Collingwood, P.O. Box 1425, Ann Arbor, Michigan 48106.
Yen, T.F., and G.V. Chilingarian. 1976. Developments in petroleum science
#5 oil shale. Elsevier Scientific Publishing Co.
380
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ACKNOWLEDGEMENTS AND DISCLAIMER
We would like to acknowledge the Office of Water Research and Technolo-
gy (Project No. 8-154-UTAH; Contract No. 14-34-0001-8123), United States
Department of the Interior, Washinaton, D.C., which provided funds for
research and publication, as authorized by the Water Research and Develop-
ment Act of 1978.
Contents of this publication do not necessarily reflect the views and
policies of the Office of Water Research and Technology, U.S. Department of
the Interior, nor does mention of trace names or commercial products consti-
tute their endorsement or recommendation for use by the U.S. Government.
381
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Table Al. SUMMARY OF SELECTED OIL SHALE INVESTIGATIONS OF ORGANICS
50’ SE — 30
SCOT
100°—275° @
0
5 1mm
Cummins et al.
72 and 74
Gallegos
73
Raw shales
in situ
simulation
Co 1 utnns
50’ SE — 30
capillary column
4.7 kg stirred in 6 i benzene 1/2 days + 10 mm
ultrasonic -* organics extracted
LC alumina of pentane solubles
LC silical gel and 5A molecular sieve- alkanes
Jacobson et al.
74
Raw shales
Utah
Wyoming
Colorado
1/8” od 10’ SS
150/200 mesh
Poropak Q
He 50—180°C
Pyrolysis procedure, retorting study
Kwan et al.
78
Spent and raw
shales +
CC/MS and HPLC
Spent shale ground to <100 mesh soxhlet
extracted 48 hrs benzene, roto evaporation
LC—alumina benzene—methanol fractions
Maase and Adams Processed
79 and shales
Dickson and Porcella
79
10 m glass capil-
lary column with
SP2100
90—250°C @ 5°/mm
Soxhlet extraction 3 days with benzene then
methanol extraction - Ames test
Flash evaporation and Kuderna Danish
TLC
Chong et al.
76
Samples
Source
Analyzed
Identification Methods
and/or CC Columns Procedure Keywords and Other Comments
and Conditions
Oil shale
thermally
degraded with
CO—H 2 0
C.A
cc
Extracted with benzene and methanol
LC IRA—904 anion, A—is cation, FeC1 3 — clay
IRA—904 anion, silica gel, 5A molecular
sieve
Green River
200’ x
0.02”
Crushed 2—3 mm, soxhlet
extraction for one week
shale
Dexsil coated
capillary CC/MS
50:50
TIM ?
benzene:methanol
Chromatography
.
-------
Table Al. CONTINUED
Samples
Source Analyzed
Identification Methods
and/or CC Columns Procedure Keywords and Other Comments
and Conditions
Robinson and Cook Oil shale, raw IR Crushed, C 6 11 12 soxhiet 24 hrs L/L ultrasonic,
71 flash evaporation (10 torr) TAR Problem
molecular sieves other work with pentane
Robinson and Cook Raw shale Not reported <8 mesh - <100 mesh 24 hr. soxhiet with
73 Wyoming cyclohexane, elution chromatography and
molecular sieves +++
Saxby in Shale kerogen Not reported Soxhiet extraction benzene:methanol
( Yen 76b Acid/Basic fractionation
Chapter 6 TAR and moisture problems
Identification C 40 H 80
Schmidt—Collerus Spent shale 50’ Corasil SCOT Benzene, 6 day soxhiet extraction
74 100°—300°C Flash evaporation/Kuderna Danish
TLC identification/separation
Show PA l- I mobility with polar solvents
Thomas and Lorenz Raw shales Not reported Centrifical separation macro characteristics
70 C, N, S, Fe, H with/without TAR problem
Yen Shale bitumen Not reported Benzene soxhiet extraction, fluorescence, GC,
76 kerogen HPLC, UV macro element characterization
kerogen model, geological origin ? , much more
-------
TableA2. SELECTED INVESTIGATIONS OF PRODUCTS AND WASTES DERIVED FROM SHALES, TAR SAND, COAL, AND HIGH
BOILING CRUDE OIL DISTILLATES
Samples
Source Analyzed
Identification Methods
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
NMR, MS, IR Review of methods of analysis! Problems with
f lash evaporation macro characterization C,
H, N, S inorganics!
Callen et al.
77
Oils (related)
NMR, IR
LC: n—Pentane, benzene, THF
Pyridine, macro characterization 0, N, S, C
Heteroatom concentrations
C.—h. Ho et al.
79
Shale oil
150 ton
Laramie Retort
1/8” x 20’
3% Dexsil 400
on 100/120 mesh
Chromosorb 750
Centrifuge separation water/oil/emulsion
Acid/Base LL extractions
LC Sephadex LH—20, Silicic Acid, Basic Alumina
Neutral Aza—arenes suggested
Clark et al.
77
Shale oil
COED
Syn crude
3% Dexsil packed
400 column
Benzene -3 Pyridine extraction
Flash evaporation problems LC Florisil,
Alumina, Sephadex DMSO, Cyclohexane, 1120
fractions
Fruchter et al.
77
Shale oil
solvent refined
coal materials
6’ 3% SP2100
120° — 250°C
Isooctane/HC1/NaOH DMSO fractionations
Irradiation of samples for standard also
inorganics
Greinke and
Lewis
75
Guerin
77
Jones et al.
77
Coal and
petroleum
pitch
volatiles
Crude oils
Coal oils
Shale oils
Oils from coal
and shale
GC, MS, UV
1/8” x 10’ SS
3% Dexsil 300
on Chromosorb C
USBM—AP I
procedure
CC/MS 3% Dexsil 400
70—320°C @ 4°/mm
Distillation of petroleum pitch
collected volatiles soxhiet extracted
with cyclohexane
3—5 ring PAll content estimated
Comparison of PA l-I from energy sources,
combustion products, conversion products and
other processing wastes +
LC Sephadex L1I—20 elution of PAl-I with 2 2. THF
and 2 2. ethanol
Bunger
77
Tar Sand
Bitumens
-------
Table A2. CONTINUED
Samples
Source
Analyzed
Identification Methods
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
Jones in
Coo me s
78
Pellizzari
78
Shale oil and
related fuels
Energy related
wastes and
effluents
6’ 1% OV—1O1
100_3400 @ 4°/mm
100 m glass SCOT
OV—1O1 20—240°C
0
@ 4 1mm; 50 m
glass SCOT, others
LC Silica Gel
Petroleum ether and CH 2 C1 2 methanol fractions
Liquid and solid effluents from oil shale,
coal gasification and coal liquefaction
processes, coal fired power plants and oil
refineries -H-!
ce
( 7
Rubin et al.
76
Schweighardt
and Thomas 78
Crude oils
from coal
Coal derived
products
Fractionation
for biological
testing
L/L Acid/Base extractions
LC florisil of neutral fraction
eluted with hexane, benzene, ether, methanol
Liquid nitrogen saponification
N—Pent ane/benzene/ THF
LC
N—Pentane/Benzene molecular sieve (5A)
thiourea
Sharkey et al.
76
Coal, ashes
Shale oils
Ferroalloy
“high resolution
MS ”
300°C lOu torr
Analysis of PAR in coal, coal ash, fly ash,
and other fuel and emission samples
Schiller and
Mathiason
77
Oils, Tar
Sand and Coal
derived
1/8” x 10’ SS
5% SE — 30 on
Chromosorb W
Mixed tar with 2—3g A1 2 0 3 (N)LC Alumina CC1 3 FI,
tetrahydrafuran/hexane/ toluenelchloroform
THF/ethanol fractions
Uden et al.
79
TOSCO
Shale
Acids
II
Oil
and Bases
CC / IR
100’ x 0.03”
SCOT FFAP
on Chrornosorb R
L/L Acid Base extractions
substituted pyridines and quinolines
and phenols identified
Selucky et al. Bitumen
77
HPLC
-------
TableA3. SELECTED INVESTIGATIONS OF AIR CONCENTRATIONS OF PAIl
Samples
Source
Analyzed
Identification Methods
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
Cautreels and Aerosol 3 m Soxhlet extraction 4 hrs benzene, 4 hrs
Cauwenberghe extracts 4% Dexsil—300 methanol redissolved in ether, washed in water
77 120—280°C @ 4°/mm Acid/Base fractions
Daisey and Leyko Air filters 3.18 mum x 3.66 m Soxhiet extraction with cyclohexane
79 SS with 6% Dexsil TLC acetylated cellulose
300 on 80/100 propanol—acetone—water (2:1:1)
Chromosorb W (HP)
Hill et al. Air filters 10’ x 2 mm glass Soxhlet extraction with methanol redissolved
77 packing coated with in cyclohexane
Carbowax 20
0 100—240 @ 4°/mm
Lee and Novotny Air filters 19 m x 0.26 mm Column study
76 glass capillary 180 cmx O. 32 cm odstainless steel column
SE—52 3% Dexsil 300 on 80/100 mesh Chromosorb W
Natusch and Air filters 1/4” od x 6’ glass Dimethyl sulfoxide soxhiet extraction
Tomkins 1.5—3% SP2100 n—pentane, n—heptane isooctane, n—hexane
78 80/100 mesh + fractions
others
Pierce and Katz Aerosols Not reported Benzene soxhlet extraction TLC preseparation
75 polycyclic Quinones identified
Pitts et al. Air filters TLC solvent toluene:
78 CH2C12:Methanol 25:1:1
Silica gel plates deadsorption in
methanol + Ames test
-------
Table A4. SELECTED INVESTIGATIONS CONCERNING PAll WATER CONCENTRATIONS
Samples
Identification Methods
Source Analyzed
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
Acheson et al. Synthetic and TLC GLC Ultra—Turrax resin -* CH 2 C1 2
76 river water extraction efficiency study
variables suspended solids, initial concentra—
t ion
Adams et al. Water with Chromosorb 101 Comparison of sorbent resins properties and
77 kriowns and 102 efficiencies f or C 6 — C 13 alkanes and 1 to
XAD2and4 4ringPAB
Tenex — CC and
Poropak
co
—A
Chriswell et al. Water with CC 3 mm x 1.8 in Study of XAD—2 and carbon adsorption recovery
77 known PAM SS column packed of trace organic from water
with 5% OV-1 on
100/120 mesh
Chromosorb WAW
75—250°C @ 8°/mitt
Dunlap et al. Groundwater XAD—2 resin Sampling and extraction study of groundwaters
77 extraction
EPA Industrial L/L extraction Sampling and analysis procedures for screening
77 wastewater CH 2 C1 2 li/HO of industrial for priority pollutants
Junk et al. Water XAD—2 resin Study of removal of trace organics from water
74 extraction
-------
TableA4. CONTINUED
Samples
Identification Methods
Source Analyzed
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
Kwan et al. Shale retort IR, UV, GC L/L extractions with ether for 2 weeks
77 water HPLC Acid/Neutral/Basic fractions
May et al. Water with XAD—2 resin Study of PAIL water solubilities
78 PAH stan— extraction Naphthalene - Chrysene
dards
Spath Ohio River CC 1/8” x 6’ SS Background study and results of chlorination
72 water 10% silicone of PAHs Naphthalene—Pyrene
grease on Gas
Chrom Q
Stepan and Water with 2 mm x 3.5 m XAD—2 and 7 extraction efficiency study
Smith knowus glass column variable flowrate, pH, temperature
77 3% SE—30
Webb Water with XAD resins Comparison of isolation methods for C 6
75 knowns urethane foam alkanes and 1 to 4 ring PAIL
solvent extrac—
t ion
-------
TableA5. SELECTED INVESTIGATIONS OF PAM CONTENT IN OTHER ENVIRONMENTAL SAMPLES
Samples
Source
Analyzed
Identification Methods
and/or GC Columns Procedure Keywords and Other Comments
and Conditions
Brown et al. Marine sediments 30 m x 0.25 mm Benzene/methanol soxhiet extractions; LC on
78 Glass capillary silica gel
SE 54 WCOT col.
Farrington Near offshore Glass capillary “EPA mussel watch” pyrene and chrysene
78 sediments identified
Giger and Blumer Sediments UV, MS, GC Soxhiet extractions methanol and benzene
74 LC—Sephadex LH—20, Silica gel, Alumina
removal of H 2 0, S; UV estimation of PAM
contents through coronene
John and Nickless Sediments 4 mm x 3’ with 5% Na 2 SO 4 water removal, soxhiet extraction
77 Dexsil 300 on 60/80 CH 2 C1 2 LC x 2 then TLC
mesh chromosorb W
Lao et al. Sludges, tar, 0.125” x 12’ SS Cyclohexane soxhlet extraction 24 hrs;
75 soot, air packed with 6% or separatory funnel 24 hrs CH 2 C1 2
Dexsil 300, 400, or
410 on 80/100 mesh
Chromosorb W HP
Lee and Mites Carbon black 11 m x 0.26 mm Soxhiet extraction CH 2 C1 2 for 18 hrs
76 Glass capillary
SE—52
Pancirov et al. Wastewater 1/8” x 10’ 2% SE 30 C’ 4 as a standard CH 2 C1 2 soxhiet extraction
78 refinery on Chromosorb C LC on alumina elutions isooctane to DMSO
sediments He 40 mi/mm
175_3000 @ 4°/mm
-------
POLAR CONSTITUENTS OF A SHALE OIL: COMPARATIVE
COMPOSITION WITH OTHER FOSSIL-DERIVED LIQUIDS*
I.B. Rubin, NA. Goeckner , and B.R. Clark
Analytical Chemistry Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37830
Historically, polar compounds in petroleum crudes and products have
been of interest to chemists and engineers because some categories of these
compounds have a commercial value if they can be separated, while others
have detrimental effects on refinery processes and end uses of the product. 1
Acidic and basic components can provide either larger benefits or worse
problems in fuels derived from coal or shale, because they are generally
present in higher quantities than in petroleum crudes. In natural crudes,
acids and bases comprise usually not more than 1% to 2% each,’ while in the
synthetic crudes they may total as much as 10%, as will be seen below. In
recent years, environmental concerns have added another dimension to the
analysis of fossil fuels. These concerns regarding both the production and
end uses of fossil fuels have required increasing efforts to examine all
aspects of fossil fuel technology for their potential effects on living
systems. For many years this effort was devoted mainly to the study of the
polycyclic aromatic hydrocarbon fraction, constituents of which are known to
have tumorigenic effects, but recent studies have shown that, as measured by
the Ames test for mutagenicity, some of the polar classes of compounds have
as large, if not a larger, biological effect. 2
The use of strong acids and bases for the separation of bases and acids
respectively from crudes and refinery products has been accepted practice
virtually since the beginnings of the modern petroleum industry. 1 Although
other separations methods, such as the Bureau of Mines-API procedure in
which ion exchange resins are used, have been developed, 3 acid/base extrac-
tions seem to be generally adaptable to a wide variety of materials. 4 8
Another recent separations procedure uses Sephadex LH-20 gel as a support
for the partitioning of polar and nonpolar compounds into a hexane/aqueous
methanol solvent system. 9 10 The polar fraction can then be subfractionated
into its acidic and basic components.
In our work we have chosen to follow both the acid/base and the LH-20
procedures as well as combinations of the two. This acid/base fractionation
kResearch sponsored jointly by the Environmental Protection Agency and the
U.S. Department of Energy under contract W-7405-eng-26 with Union Carbide.
Western Illinois University, Chemistry Department, Macomb, Illinois.
390
-------
scheme7 has been adapted from that described by Swain et al.6 Modifications
of the early acid/base separations methods have proven applicable to a range
of materials from Cincinnati tap water4 to fungicides for rubber trees in
Malaya,5 and including shale oil.8 The method involves extractions from
ether solutions of the sample initially with 1 N hydrochloric acid and 1 N
sodium hydroxide followed by extractions with ether at pH 6, 1 and 11.
Water soluble acids and bases are recovered as are the various insoluble
materials. A comparison of the quantities of acids and bases recovered from
several materials is shown in Table 1, and one can see that they vary great-
ly from material to material. Reproducibility of the fractionation is good
as is shown in Table 2 for three of the polar fractions in shale oil. These
data are in chronological order and cover a period of about two and a half
years. It can be seen that except for the weak acids, there is no differ-
ence between the early and later results. Recent data from samples run
simultaneously in triplicate show the same order of precision. Distribution
can be checked by using radioactive tracers if one desires. We have done
independent studies with an aqueous solution of standard compounds and found
that distribution among the polar fractions, plus the neutral, is as we had
expected. For quality assurance, we periodically fractionate one shale oil
of which we have an ample quantity and which apparently has not changed much
in chemical class composition in three years. This oil has been selected as
a standard oil by the National Bureau of Standards.
The full fractionation scheme using Sephadex LH-20 has been described
by Jones et al.10 The polar constitutents are found mainly in the hydro-
philic and H-bonding fractions. Once the lipophilic fraction has been
completely eluted with hexane, the hydrophilic can be eluted with the
aqueous methanol solution by reversing the flow. Since this is a lengthy
procedure, we prefer to elute the hydrophilic with acetone, although this
has the disadvantage of shrinking the gel and necessitating repacking the
column. The gravimetric yields for the hydrophilic and H-bonding fractions
of several materials is shown in Table 3, and again one can see the varia-
bility among materials. The distribution between lipophilic and hydrophilic
fractions is very reproducible as shown by the data in Table 4. Recovery of
the hydrophilic fraction is very nearly the same regardless of the scale of
the operation, as is the precision. The difference in total recovery was
caused by the fact that in the large scale work, the volatiles and insolu-
bles were removed prior to chromatography, while they were not in the small
scale work. About two years elapsed between these two studies. As with the
acid/base procedure, quality assurance can be maintained by periodically
chromatographing radio-tracers, a synthetic mixture or a control oil.
Fractions are defined by the operation used for their isolation, rather
than strictly by composition because of the solubility and other problems
that cause cross contamination among the various fractions. As we see
below, the hydrophilic fraction contains not only acid and base but also
neutral components. The neutral fraction of shale oil contains about 5%
hydrophilic material when it is chrcmatographed on LH-20, while the lipo-
philic fraction was found to contain about 7% ether soluble base fraction
when it was subjected to an acid/base extraction. This is the same propor-
tion as found in the whole oil.
391
-------
TABLE 1. COMPARATIVE ACID AND BASE COMPOSITION
OF FOSSIL FUEL PRODUCT OILS
Weight Percent of Original Sample
Shale Coal Coal
Mixed
Fraction
Oil Oil A Oil B
Crude Oils
Weak Acids, Ether Sol.
1.1 1.6 4.7
0.3
Strong Acids, Ether Sol
0.3 1.3 0.7
0.4
Acid, Water So].
0.6 1.0 0.4
0.1
Bases, Ether So].
7.0 2.2 2.0
0.2
Bases, Water So].
0.4 0.4 0.3
0.5
TOTAL
9.4 6.5 8.1
1.5
TABLE 2.
REPRODUCIBILiTY OF FRACTIONATION FOR
ACID
AND BASE FRA(:TIONS OF SHALE OIL
Date
Weight Percent of Original
Sample
Weak Acid Strong Acid
Base
2/20/76
1.33 0.45
6.87
3/15/76
1.23 0.26
7.11
7/12/76
1.33 0.20
7.01
10/25/76
0.74 0.25
6.86
10/17/78
0.88 0.42
7.22
10/17/78
0.67 0.25
7.18
Ave.
1.09 0.31
7.04
RSD, %
29.0 34.0
2.0
Wt. range, g
5-100
392
-------
TABLE 3. COMPARISON OF POLAR FRACTIONS OF FOSSIL FUEL
OILS AFTER GEL PARTITION CHROMATOGRAPHY
Sampi
Weight Percent of 0ri inal San!ple
e Hydrophilic H-Bond
Shale
Oil 7.0
5.2
Coal
Oil A 3•4
8.2
Coal
Oil B 10.9
5.7
Coal
Oil C 18.5
0.0
Crude
Oils 0.8
14.9
TABLE 4. REPRODUCIBILIT’i OF LIPOPHILIC/HYDROPHILIC
DISTRIBUTION BY GEL P RTITI0N CHROMATOGRAPHY
A.
Small scale procedure
Wt. Range, g: 0.05-1.0
Column Vol., ml: 11
n: 24
Recovery, %
Lipophilic Hydrophilic
Total
94.2±
3%
87.6±4% 6.8±21%
B.
Large scale procedure*
Wt. Range, g: 17-300
Column Vol., ml: 2000
S
Recovery, %
Lipophilic Hydrophilic
Total
99.4±
1.5%
93.4±1.5% 6.0±17%
*Calculated from data in Reference 10.
393
-------
The hydrophilic fraction is a very complex mixture, so it was subse-
quently subfractionated by an acid/base procedure. The flow diagram of this
separation is shown in Figure 1. It is essentially the same as described
previously, 7 except that methylene chloride was used instead of ether and
potassium rather than sodium hydroxide. Distribution of the subfractions
for several fuel oils is shown in Table 5. The shale oil fraction can be
seen to contain more base and strong acid materials than those from the
coal-derived products, which in turn, contain more phenolic matter. A
considerable portion of these hydrophilic fractions appeared to be nonpolar
in nature when confronted with strong acid and base. This neutraV’ portion
was then subfractionated by sbsorption chromotography on alumina. Distribu-
tion of the neutral subfractions are shown in Table 6. Very little, if any,
material is eluted with cyclohexane and benzene, so there is an almost
complete lack of saturates and aromatics. Since the bulk of the material is
eluted with methanol, indications are that these compounds have strong
absorptive capabilities.
These methanol eluted subfractions have been examined by elemental
analysis, nuclear magnetic resonance spectroscopy and infrared absorption
spectrophotometry. These materials are essentially nonvolatile. Gas chrom-
atography of these and similar samples on Dexsil 400 packed columns at
temperatures as high as 320°C reveal no significant peaks, so GC-mass spec-
troscopy could not be used for characterization studies. Direct probe
studies of similar subfractions produced such complex mass spectra that no
useful information could be acquired.
Results of the elemental analyses are presented in Table 7. Carbon and
hydrogen were determined by standard combustion train analysis, nitrogen by
the Kjeldahl procedure, sulfur by the Leco sulfur analyzer and oxygen by
difference. Ratios of the other elements to carbon are shown in Table 8, as
are postulated empirical formulae of the average compounds normalized to one
nitrogen atom per molecule and molecular weights based on these formulae.
The low hydrogen to carbon ratios indicate that the carbon residues are more
aromatic than aliphatic in nature. It must be recognized that even though
these subfractions comprise only a small portion of the original sample,
they are still complex mixtures. The shale oil subfraction is about 4% of
the original.
The proton NMR spectrum of the shale oil subfraction is shown in Figure
2, and for a coal product in Figure 3. Hydrogen data are summarized in
Table 9. Values in the first colunm are methyl group absorptions y and
further from the aromatic ring. These values are about the same for each
sample. Values in the 1.0 to 1.9 6 range correspond to CH 2 and CH protons
and further from the aromatic ring or activating group. Since these values
are larger than those in the 0.5 to 1.0 6 range, they indicate average
chains of medium length. The 1.90 to 3.50 6 region indicate hydrogen a to
an aromatic ring, and the range in values show variations of substituents on
the aromatic ring. Values in the last two columns are for noncondensed ring
aromatics and condensed ring aromatics, respectively, while the 3.50 to 4.50
6 and 4.50 to 6.0 6 values are characertistic of protons influence by func-
tional groups, in the former range by groups that may contain oxygen and/cr
394
-------
ORNL—DWG 78-6038
AD
ORG
N
A 1
B
ALUMINA I
ADSORPTION
CHROMATOGRAPHY
(GI
A 2
N 2
CH 3 OH
N 3
N 4
Figure 1. Flow Diagram for Subfractionation of Gel
Partition Chromatography Fractions.
395
-------
TABLE 5. DISTRIBUTION OF SLJBFRACTIONS OF THE HYDROPHILIC
PORTION OF FOSSIL FUEL OILS
Sample
Weight Percent of Hydrophilic Fraction
Weak Acid Strong Acid Base Neutral
Total
Shale Oil
11.2 1.7 10.8 64.0
87.7
Coal Oil A
20.2 0.4 2.0 57.7
80.3
Coal Oil B
36.7 0.5 3.4 52.4
93.0
Coal Oil C
44.6 0.0 1.9 37.8
84.3
Crude Oils
3.2 0.7 4.2 79.9
88.0
TABLE
6. DISTRIBUTION OF THE NEUTRAL SLJBFRACTION OF
THE HYDROPHILIC PORTION OF FOSSIL FUEL OILS
Sample
Weight Percent of Neutral Subfraction
C 6 H 12 C 6 HE. CH 2 C1 2 CH 3 OH
Total
Shale Oil
0.3 1.0 0.8 85.1
87.2
Coal Oil A
0.0 1.4 3.4 71.9
76.7
Coal Oil B
1.1 0.9 1.1 81.3
84.4
Coal Oil C
0.6 1.2 0.0 98.2
100.0
Crude Oils
0.9 0.9 3.5 71.2
76.5
TABLE 7.
ELEMENTAL ANALYSIS CF FOSSIL FUEL OILS SUBFRACTION
Sample
Weight Percent of Methanol Subfraction
C H N S
0*
Shale Oil
75.3 7.76 5.18 1.42
10.34
Coal Oil A
81.3 8.00 3.1 .0 0.17
4.60
Coal Oil B
81.7 7.78 1.86 0.61
8.05
Coal Oil C
79.5 7.70 1.86 0.21
10.73
Crude Oils
75.1 8.32 1.99 5.21
9.38
*By difference.
396
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TABLE 8. ATOMTC RATIO OF OThER ELEMENTS TO
CARBON IN METHANOL SUBFRACTION
Sample
Ratio H/c: N/C S/C 0/C
Shale Oil
1.23 0.059 0.007 0.103
Coal Oil A
1.17 0.033 0.001 0.042
Coal Oil B
1.13 0.020 0.003 0.074
Coal Oil C
1.15 0.020 0.001 0.101
Crude Oils
1.32 0.023 0.026 0.094
Shale Oil
Coal Oil A
Postulated Empirical Formula
C 17 H 21 N 1 0 2 MW = 271
C. 1 H 36 N 1 0 1 438
Coal Oil B
Coal Oil C
C , 1 H 59 N 1 O 4 749
CC . 0 H 58 N 1 0 5 752
Crude Oils
C 44 H 58 N 1 0 4 664
TABLE 9.
DISTRIBUTION OF HYDROGEN TYPES BY NMR IN METHANOL SUBRFACTION
Chemical Shift
0.5- 1.0- 1.90— 3.50- 4.50- 6.0- 7.15-
1.Oo 1.906 3.506 4.506 6.06 7.156 8.256
Sample
Percent of Hydrogen
Shale Oil
13 37 37 - - 5 8
Coal Oil A
10 26 31 1 7 17 8
Coal Oil B
11 26 26 - 5 21 11
Coal Oil C
11 32 19 4 3 16 16
Crude Oils
14 40 31 - - 8 8
397
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Figure 2. Proton NMR Spectrum of Methane! Subtraction of Shale Oil
t 0
IQ9STS5432
Figure 3. Proton NMR Spectrum of Methanol Subtraction of Coal Oil C.
398
-------
WAVELENGTH IN *
Figure 4. Infrared Spectrum of Methanol Subfraction of Shale Oil
Figure 5. Infrared Spectrum of Methanol Subfraction of Coal Oil C.
399
-------
r itrogen, and in the latter range by a variety of vinylic protons. Iti
samples such as these, it is difficult to assign functional groupings.
The infrared spectrum of the shale oil subfraction is shown in Figure
4, and that of a coal product in Figure 5. As with the NMR data, because of
the complexity of the samples, it is impossible to make specific assign-
ments. The aliphatic bands are clearly seen in the 1.3R5, 1455 and 2800 t.o
3000 wavenumber regions. The shale oil sample has virtually no aromatic
absorption whfle the coal product does, shown at 1600 and 3000 cm . The
shale oil has a much stronger carbonyl absorption band than does the coal
product (1600_to 1700 cm ‘), while both show possible amide or amine absorp-
tion (3400 cm J). There is very little absorption by the shale oil sample
in the “fingerprint” region, 750 to 1350 cm 1, while the coal oil has a
number of absorption bands in that area.
We have attempted to describe similarities and differences in the polar
portions of a variety of types of fossil fuel oils including oil from shale,
from several coal liquefaction processes and from a mixture of natural
petroleum crudes. Samples have been fractionated by acid/base distribution
as well as by gel partition chromatography which was then followed by acid!
base distribution and adsorption chromatography. One subfraction of partic-
ular interest was that obtained from the hydrophilic fraction after gel
partition chromatography, extracted into a neutral subfraction, and then
eluted from an alumina column by methanol. This subfraction was not gas
chromatographable, and was partially characterized by elemental analysis,
NMR spectroscopy and infrared spectrophotometry.
REFERENCES
1. Lochte, H.L. and ER. Littmann. The Petroleum Acids and Bases. New
York, Chemical Publishing Co., Ir c., 1955.
2. Epler, J.L., J.A. Young, A.A. Hardigree, T.K., Rao, M.R. Guerin, LB.
Rubin, C.-h. Ho, and B.R. Clark. Analytical and Biological Analyses of
Test Materials from the Synthetic Fuel Technologies. I. Mutagenicity
of Crude Oils Determined by the Salmonella typhimurium/Microsomal
Activation System. Mutat Res. 57:265-276, 1978.
3. Jewell, D.M., J.H. Weber, J.W. unger, H. Plancher and D.R. Latham.
Ion-Exchange, Coordination, and !kdsorption Chromatographic Separation
of Heavy-End Petroleum Distillates. Anal. Chem. 44:1391-1395, 1972.
4. Braus, H., F.M. Middleton and G. Walton. Organic Chemical Compounds in
Raw and Filtered Surface Waters. Anal. Chem. 23:1160-1164, 1951.
5. Coles, GV. The Analysis of Coal Tar Fungicides. J Sci Food Agric.
7:11—17, 1956.
6. Swain, A.P., J.E. Cooper and R.L. Stedman. Large Scale Fractionation
of Cigarette Smoke Condensate for Chemical and Biologic Investigations.
Cancer Res. 29:579-583, 1969.
400
-------
7. Rubin, LB., M.R. Guerin, J.L. Epler and A.A. Hardigree. Fractionation
of Synthetic Crude Oils from Coal for Biological Testing. Environ Res.
12:358-365, 1976.
8. Uden, P.C., A.P. Carpenter, Jr., H.M. Hackett, D.E. Henderson and S.
Siggia. Qualitative Analysis of Shale Oil Acids and Bases by Porous
Layer Open Tubular Gas Chromatography and Interfaced Vaper Phase Infra-
red Spectorphotometry. Anal Chem. 51:38-43, 1979.
9. Klimisch, H.J. and L. Stadler. Gel—verteilungschromatographisches
Verfahren zur praparativen Abtrennung polarer Substanzen von
polyzyklischen aromatischen Kohlenwasserstoffen. J. Chrornatog.
67:175-178, 1972.
10. Jones, A.R., M.R. Guerin and B.R. Clark. Preparative-Scale Liquid
Chromatographic Fractionation of Crude Oils Derived from Coal and
Shale. Anal. Cern. 49”1766-1771, 1977.
401
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PROTON AND CARBON-13 NMR STUDIES ON NAPHTHA AND LIGHT DISTILLATE
HYDROCARBON FRACTIONS OBTAINED FROM IN SITU SHALE OIL
D.A. Netzel, D.R. McKay, R.A. Heppner, F.D. Guffey,
S.D. Cooke, and D.L. Vane
U.S. Department of Energy
Laramie Energy Technology Center
P.O. Box 3395, University Station
Laramie, Wyoming 82071
ABSTRACT
The proton and carbon-13 NMR studies were made on the saturates,
olef ins, and aromatic fractions obtained from the naphtha and light distil-
late cuts of in situ shale oil. Carbon-13 NMR area measurements were used
to compute the C IC 1 ratio and, thus, the average paraffinic chain length
for the saturate ractions. Identification and the relative concentrations
of the various olefins were determined from carbon-13 NMR data. The NMR
results are compared with the mass spectroanalysis data on the same frac-
tions. The percent of carbon types in the aromatic fractions were also
determined by both NMR and mass spectral data. Qualitative agreement
between the two techniques was evident. However, some definite disagreement
also exists.
INTRODUCTION
Numerous research and technological programs are being conducted by
both governmental and industrial laboratories to economically recover oil
from the large deposits of oil shale in the Western States of Colorado,
Utah, and Wyoming. To aid in monitoring and assessing the various recovery
processes being used it is necessary that new analytical methods be devel-
oped which are rapid and can qualitatively and quantitatively characterize
the various oil fractions obtained from crude shale oil. These fractions
are extremely complex mixtures and while, in principle, individual identi-
fication of the components is conceptually possible the task would be
exceedingly time consuming and of limited value. Characterization in terms
of average properties of the sample is an alternate approach. This analyt-
ical approach can provide significant quantitative data on the oil samples
in a relatively short time. Of the many analytical spectros opic tech-
niques, NMR spectroscopy is most suitable for obtaining average properties
of hydrocarbon fractions.
*presented in part as a Poster Session, ENC Conf., Asilomar CA, 2/19-23,
1979.
402
-------
Although it is not as sensitive as other instrumental methods, the
advantages of NMR spectroscopy are the nondestructive aspects as related to
the sample and the relative ease in which the spectroscopic data can be
reduced to give the desired information.
Carbon-13 and proton NMR spectroscopy have been used _extensively in
characterizing oil fractions obtained from petroleum crudes.’ At present,
little work using this technique has been published on the chemical composi-
tion of shale oil. NMR spectroscopy has been used, however, to study oil
shale in the solid state. 7 8 9
It is the purpose of this paper to present preliminary NMR data on the
average composition of the hydrocarbons of the naphtha and light distillate
fractions obtained from thermal fractionation of crude shale oil. Both the
naphtha and light distillate fractions were subdivided into saturates,
olefins, and aromatics. The average carbon chain-length was determined for
the saturate fractions. The double—bond position and the relative percent
of the various olefin types were determined for the olef in fractions. The
aromatic fractions were characterized in terms of average molecular parame-
ters which have been developed for petroleum crude and coal liquid analysis.
The data obtained on the saturates, olefins and aromatic fractions by NMR
are compared with the mass spectral analysis of the same samples.
EXPERIMENTAL
Shale Oil Fractions
The oil fractions used in this study were obtained from crude shale oil
produced by the in situ retort experiment at Site 4, Rock Springs, Wyoming.
The crude shale oil was thermally fractionated using the Hempel technique
into naphtha, light distillate, heavy distillate, and residue fractions.
The naphtha and light distillate fractions were treated with 10% NaOH and
10% H 2 S0 4 solutions to remove most of the tar acids and bases,” respective-
ly. The neutral fractions were then separated into saturates, olef ins, and
aromatics using silica gel chromatography. 12 The eluent was monitored by
noting changes in the refractive index due to the different hydrocarbon
types.
Carbon and Hydrogen Determination
The weight percents of carbon and hydrogen for the shale oil fractions
were determined by the standard combustion technique.
Molecular Weight Determination
The molecular weights were determined by vapor phase osmometry. Benzil
in benzene was used as a calibrant for VPO molecular weight determinations.
+Reference to specific manufacturer does not imply endorsement by the United
States Department of Energy.
403
-------
Nuclear Magnetic Resonance
Carbon-13 NMR--A varian CFT-20 NMR* spectrometer was used to obtain the
gated proton decoupled spectra. A 5 mm probe insert was used along with a
pulse width of 12 psec (14 psec = 9Q0) and a pulse delay of 9 sec. The
number of pulses used was varied to assure good signal-to-noise ratio.
Grated decoupling was used to suppress the nuclear Overhauser effect (NOE)
and, thereby, assuring quantitative results of the integration of carbon
atoms’ presence in the samples. Samples were dissolved in CDC1 3 and refer-
enced to internal TMS. Spectra were obtained at an ambient temperature of
about 38°C.
Proton NMR--Proton spectra were also obtained on the CFT-20 NMR spec-
trometer with a 5 mm probe insert. A 900 pulse width of 40 psec was used.
In most cases only a single transient was used to record the proton spectra.
CDC1 3 was used as the solvent for the samples and TMS the internal refer-
ence.
Gas Chromatography/Mass Spectrometry
The mass spectral analysis was performed on an AEI-MS12 mass spectrom-
eter and a HP-5700 chromatograph. Data reduction was performed on a
Finnigan INCOS 2300 computer system. The mass spectra were recorded at an
accelerating voltage of 8KV, an ionizing voltage of 70V, and a filament
current of 100 pA. The source temperature was 210°C.
A SCOT column (50’ x 0.02”) packed with Dexsil 300 was used in the GC.
The oven was programmed from 50 to 250°C at 2°/mm. Both the transfer line
and injection port temperatures were 250°C. A helium flow rate of approxi-
mately 4 cc/mm was used.
Computer Programs
The equations for computing average molecular parameters from NMR data
were obtained from the article by Cantor.’ 3 The input data for the computa-
tion are the normalized integrated areas from both carbon-13 and proton NMR
spectra, weight fraction of carbon and hydrogen from elemental analysis and
the average molecular weight from VPO.
The method of Robinson and Cook’ 4 was used to reduce the mass spectral
data into compound types in the naphtha and light distillate aromatic frac-
tions.
RESULTS AND DISCUSSION
A number of investigations have been conducted to determine the actual
molecular species of alkanes, olefins, and aromatics in naphtha and light
distillate fractions of shale oil.”’ 15...21 These studies used techniques
other than NMR spectroscopy to determine the hydrocarbon carbon chain length
of the most dominant and lesser abundance components. However, such studies
were very time consuming.
404
-------
Table 1 lists the chemical and physical properties of the shale oil
fractions obtained from the Hempel distillation of the crude shale oil.
Figures 1 and 2 show both the proton and carbon-13 NMR spectra for the
saturates, olefins, and aromatics obtained from the naphtha and light
distillate fractions, respectively. It is from these spectra that informa-
tion about the average paraffinic carbon chain-length, olefinic double bond
position and average aromatic molecular parameters can be obtained.
Saturates
The proton spectra of the paraffinic carbons of the naphtha and light
distillate fractions (Figures 1 and 2, respectively) show little detailed
information other than that the ratio of CH 2 /CH 3 is greater for the light
distillate than the naphtha fraction. Under the experimental conditions
used, no evidence for aromatic or olefinic hydrogens can be found indicating
that the silica gel separation method is quite satisfactory for saturates.
The carbon-13 spectra for these two fractions shows more detail of the
types of carbons present. Again no evidence for aromatic or olefinic com-
pounds can be found. The aliphatic region shows that the saturates are
composed essentially of normal alkanes (5 intense resonances) with smaller
amount of branched and/or cyclic alkanes. The intensity ratio (as well as
the area ratio) for the five most intense lines differ for the naphtha and
light distillate fraction. This difference is due to the average carbon
chain-length of the n-alkanes present in each fraction. The average carbon
chain-length in the saturates can be estimated from NMR area ratios of known
n-alkanes. For example, Figure 3 shows the carbon—13 spectra of two
n-alkanes-nonane and hexadecane. The methyl carbon resonance is at 14.2 ppm
relative to TMS. Carbons 2, 3, 4, and 5 resonate at 23.0, 32.3, 29.7, and
30.0 ppm, respectively. The area of the resonance at 14.2 ppm represents
two methyl carbons and is sufficiently upfield to be integrated without
difficulties even in complex systems. Carbon 4 and higher have similar
chemical shifts ( 3O.0 ppm) and, thus, must be integrated together to obtain
the total area. In complex systems these carbons may not always be
resolved.
The area ratio of C (n = 4,5,6 . . . ) to C 1 was obtained for a series
of n-alkanes and this ra io was plotted against the number of carbons in the
molecule (see Figure 4). The solid line in Figure 4 represents the theoret-
ical values obtained for the hydrocarbons investigated. Since the experi-
mental values obtained fall on the line or nearly so indicates the spectrom-
eter conditions are such that quantitative results can be obtained for
carbon-13 studies.
The area ratios of C to C 1 for the saturates of the naphtha and light
distillate fraction were %und to be 2.07 and 3.67, respectively, corre-
sponding to an average carbon chain-length of C 10 and C 13 14’ respectively.
The mass spectral analysis of the saturates obtained from the naphtha and
light distillate fraction is given in Table 2.
405
-------
Table 1. CHEMICAL AND PHYSICAL PROPERTIES OF SHALE OIL FRACTIONS
Dry
Light
Heavy
shale oil Naphtha distillate
distillate Residue
Boiling range
Fraction yield, vol
I BP—1400°F
12.2
400_6000 F
39.8
600-800°F
31.7
800+°F
12.6
Paraffins and cyclo-
paraffins, vol
Olefins and cyclo-
olefins, vol
Aromatics, vol %
(includes sulfur,
nitrogen and oxy-
gen compounds)
Carbon, wt %
Hydrogen, wt
Nitrogen, wt
Sulfur, wt
Oxygen, wt *
VPO molecular weight
(aye.)
Tar acids, vol *
Tar bases, vol *
814.96
11 .97
1 .50
0.95
11.1
23.7
8 4.69
12.914
1 .014
0.69
3.6
6.6
85.35
12.142
1.16
0.144
1.6
9.9
85. 13
11 .89
1 .59
0.52
82.81
10.56
2.20
0.914
0
65.2
60.5
11.1
214.8
199
204
290
-------
1H NMR SPECTRUM
SHALE OIL
LIGHT DISTILLATE FRACTION
SATURATE CUT
13C NMR SPECTRUM
SHALE OIL
LIGHT DISTILLATE FRACTION
SATURATE CUT
f 1H NMR SPECTRUM
* SHALE OIL
LIGHT DISTILLATE FRACTION
OLEFIN CUT
OlEFINIC HYDROGENS
13C NMR SPECTRUM
SHALE OIL
LIGHT DISTILLATE FRACTION
OLEFIN CUT TMS
M^yWv^wv^^^
OlEFINIC CARBONS
lid
I PP"
KJ
|0 PPM
1.27 O.It
29.96 22.90 14.14
'H NMR SPECTRUM
SHALE OIL
LIGHT DISTILLATE FRACTION
AROMATIC CUT
13C NMR SPECTRUM
SHALE OIL
LIGHT DISTILLATE FRACTION
AROMATIC CUT
A
2.25 1.26 0.90
128.23
14.14
Figure 1. Proton and Carbon-13 NMR Spectra of the
Naphtha Saturate, Olefinic, and Aromatic Fractions.
407
-------
1H NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION
SATURATE CUT
13C NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION
SATURATE CUT
127 O.BI
29 9g 2295 14.17
1H NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION
OLEFIN CUT
— •"
1
5 3S
1
(\
i
n\ \
_^ v
| JO ppm
1.26 0.17
13C NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION
OLEFIN CUT
32.4 23.) <4.2
'H NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION
AROMATIC CUT
13C NMR SPECTRUM
SHALE OIL
NAPHTHA FRACTION/
AROMATIC CUT
a.21
144.1 136.2 127.9 COCI3
20.5 0 «»«>
figure 2, Proton and Carbon-13 NMR Spectra of the
Light Distillate Saturate, Olefinic, and Aromatic Fractions.
408
-------
13C NMR SPECTRA
NONANE: CH3CH2CH2CH2CH2CH2CH2CH2CH3
(1) (2) (3) (4) (5) (4) (3) (2) (1)
32.23
29,68
29.93
22.95
14.19 ppm
f• HEXADECANE: CH3CH2CH2CH2(CH2)8CH2CH2CH2CH3
(1) (2) (3) (4) Cn (4) (3) (2) (1) "
i
Cn
30.22
23.09
14.22 ppm
Fiqure 3. Carbon-13 NHR Spectra of Nonane and Hexadecane.
409
-------
<
QC
<
IU
Of.
u
m
^^
u
c
u
NAPHTHA &
2 -
4 8 12 16 20 24 28 32
NUMBER OF CARBONS IN STRAIGHT CHAIN PARAFFIN
13C Area Ratio with
Figure 4. Correlation of the C
the Number of Carbons in Normal Alkanes.
From the mass spectral data the saturates are composed essentially of
n-alkanes. The dominant n-alkanes for the saturates in the naphtha fraction
are n-C and n-C and for the liht distillate fractions
n-C14.
length as determined by NMR.
n-C
n-C
and
10 and n-Ctl and for the light stae racons n-12, n-13t
These values are in good agreement with the average carbon chain-
Olefins
large
shows
chemical
The proton spectra of the olefins obtained from the naphtha and light
distillate fractions (Figures 1 and 2) show the presence of olefinic hydro-
gens at about 5.38 ppm relative to TMS. The aliphatic region shows
anounts of methyl and methylene hydrogens. The carbon-13 spectra
well-defined olefinic carbons in the region of 114 to 138 ppm.
shifts of the long chain saturate carbons in 1-alkene are similar to carbons
in long chain saturate n-alkanes. The saturate carbon region
intense resonances at 14.24, 23.08, 29.63, 29.86, and 32.41 ppm
fraction and 14.16, 22.90, 29.64, 29.96, and 32.20 ppm for the light
fraction. The observed intense resonances in the alkane region
shows fine
for the
naphtha
distillate
410
-------
TABLE 2. MASS SPECTRAL ANALYSIS OF SATURATES
Naphtha Fraction Light Distillate
Area % Fraction, Area %
Branched C 9 1.8
rrC 9 1.8
n-C 10 26.5
Branched C, 1 4.0
n-C 11 39.7 2.6
n-C 12 13.1 14.5
Branched C, 3 1.4
n-C 13 2.4 16.7
n-C 14 11.0
Branched C 14 5.4
n-C 15 7.9
Branched C 15 5.1
n-C 16 6.8
Branched C 16 6.9
n—C 17 8.2
Branched C 17 2.7
n-C 18 4.6
Total Straight Chain 83.5 72.4
Total Branched 7.2 20.2
Unidentified 9.3 7.4
of the olefins in most cases differ only 0.06 ppm from the intense reso-
nances observed in the corresponding saturate fractions. It would be
difficult to ascertain whether or not these resonances are associated with
olefins or saturates based on chemical shift data only. The intensities
observed for the CH 3 carbons do not correspond to the intensities of the
olefinic carbons. This suggests that the olefins were incompletely sepa-
rated from the saturates using the silica gel separation method and confirms
the findings of other investigators.’ 5 - 21
Since any contamination of the olefins by saturates will not interfere
with the olefinic carbon region, it is possible to identify the most domi-
Rant alkene double bond positions and determine their corresponding mole
ratios. Table 3 lists the normal alkenes identified in the olefin subfrac-
tions isolated from the naphtha and light distillate fractions. Also
included in the table are the observed chemical shifts of the alkene carbons
and the relative percent for each of the alkenes identified. The alkene
double bond positions were identified by comparing the observed chemical
shifts with those reported by Couperus et al. 22 Of the 70 compounds
investigated by Couperus et al., only four of the branched olefins listed
411
-------
t’ )
Table 3. 13 NMR CHEMICAL SHIFTS AND MOLE RATIOS OF NORMAL OLEFINS IN NAPHTHA AND
LIGHT DISTILLATE FRACTIONS OBTAINED FROM SHALE OIL
Designa-
tion in
Double
Naphtha fraction
Light
distillate
fraction
Rela-
Rela-
spectra
(Figs.
I & 2)
bond
position
n-ene
t
Chemical
shift (ppm)
tive
percent
Chemical
shift (ppm)
tive
C ,
C + i
percent
C
C , 41
—
A
1-ene
114.34
138.67
36
114.21
138.95
21
B
2-ene
124.44
131.68
11
124.46
131.73
18
C
3—ene
131.92
129.1+4
11
131.91
129.42
—-
D
E
1 + -ene
5—ene
130.16
130.40
13O.69
l3O.4O
>
42
130.12
130.1+1
l30.66
130.kl
61
-------
TABLE 4. POSSIBLE BRANCHED OLEFINS FROM NMR DATA
Alkene Carbons,
Chemical Shifts (ppm)
C C
n n+i
C 1 -C 2 C 3 C 4 C 5 C 7 13). 2 g 124.96
C ( 13158 )b (124.44)
C 1 -C 2 =C -C 4 -C 5 -C 6 124.41 129.67
F
C (124.44) (129.49)
C
C 1 -C 2 -C 3 =C 4 -C 5 -C 6 139.21 130.73
C (138.61) (130.69)
C C
I i
C 1 -C 2 -C 3 =C 4 -C 5 -C 6 -C 7 138.86 131. 79
I I
C C 8 C (138.67) (131.68)
Chemical shift values from Reference 22.
Observed chemical shift values.
have chemical shifts which correspond to the observed shifts measured within
±0.6 ppm. These branched olefjns are shown in Table 4. It is the position
of the double bond relative to the methyl substitution that is important
since the actual hydrocarbon chain may be longer for the olef ins in the
samples. A longer hydrocarbon chain would have little effect on the
olefinic carbons’ chemical shifts. The data in Table 3 indicates that the
light distillate fraction contains more symmetrical alkenes relative to
1-alkene than found in the naphtha fraction.
The mass spectral analysis (in area percent of the olefins obtained
from the naphtha fraction indicates that the most predominant alkenes are
the n-C 11 (24.8%) and branched C 11 (17.1%). The total amount of normal
olefins was found to be 36.4% and branched olefins 29.6%. The remaining
fraction was due to normal alkanes as suspected from NMR data. The distri-
bution of the chain-length of the alkenes were found to be almost identical
to the alkanes in the saturate fraction.
413
-------
The predominant alkenes found in the olefins obtained from the light
distillate fraction were C 12 r13’ and C 14 . The mass spectral analysis also
showed large amounts of alkahe of similar chain-length.
Aromati cs
The proton and carbon-13 NMR spectra of the aromatic components in
naphtha and light distillate fractions are shown in Figures 1 and 2, respec-
tively. The proton spectra of the aromatic naphtha and aromatic light
distillates shows considerable detail in the types of protons present. In
the naphtha fraction, there is evidence of proton resonance for small amount
of di-and triaromatic molecules. There appears to be no evidence of this
kind in the light distillate fraction at the recorded signal-to-noise level.
The relative amount of aromatic protons is also less in the light distillate
than in the naphtha fraction (8.7% and 23.8%, respectively). Another
significant difference between the fractions is the ratio of CH 3 and CH 2
hydrogens relative to the -CH 2 hydrogens. The naphtha fraction has more
cr-CH 2 (42%) than the light distillate fraction (26.6%). Thus, the proton
NMR data suggest the naphtha fraction contains mostly substituted monoaro-
matic compounds with apparently small amount of di- and triaromatic mole-
cules. The light distillate fraction is composed mostly of monoaromatics
which are less substituted, but the alkane substituent is of longer chain—
length.
The carbon-13 spectra of the aromatics for both the naphtha and light
distillate fraction (54% and 32%, respectively). The relative amounts of
alkane carbons were found to be 46% and 68% for the naphtha and light dis-
tillate fractions, respectively.
Average molecular parameters were calculated for both the naphtha and
light distillate fractions using the equations listed in the paper by
Cantor.’ 3 The mass spectral data were also obtained for the aromatic cuts
of the naphtha and light distillate fractions. The Robinson-Cook method’ 4
of treating mass spectral data of complex aromatic mixtures was used to
obtain information on the type and amount of aromatic molecules present.
The mass spectral data show that the aromatic fractions are free of any
cross-contamination due to saturate hydrocarbons. There is considerable
agreement between the NMR and mass spectral data. However, some definite
disagreement also exists and these differences are being studied in detail.
REFERENCES
1. Williams, R.B. , ASTM Special Technical Publication No. 224, 1958.
2. Knight, S.A. , Chemistry and Industry, 1920 (1967).
3. Hirsch, E. and Altgelt, K.H. , Anal. Chem. 42, 1330 (1970).
4. Clutter, D.R. , Petrakis, L. , Stenger, Jr., R.L. , and Jenson, R.K.
Anal. Chem. 44, 1395 (1972).
414
-------
5. lajek, M., Sklenar, V., Sebor, G., Lang, I., and Weisser, 0., Anal.
Chem. 50, 773 (1978).
6. Solash, J., Haz]ett, R.N., Hall, J.M., and Nowack, C.J., Fuel 57, 523
(1978).
7. Sydansk, R.D., Fuel. 57, 66 (1978).
8. Resing, H.A., Garroway, A.N., and Hazlett, R.N., Fuel 57, 450 (1978).
9. rlaciel, G.E., Bartuska, V.J. , and Miknis, F.P. , Fuel 57, 505 (1978)
10. Stevens, R.F., Dinneen, G.U., and Ball, J.S., BuMines RI 4898, August
1952, 20 pp.
11. Dinneen, G.U. , Van Meter, R.A. , Smith, J.R. , Bailey, C.W., Cook, G.L.
Alibright, C.S., and Ball, J.S., BuMines Bull. 593, 1961, 74 pp.
12. Dinneen, G.U. , Thompson, C.J. , Smith, J.R. , and Ball, J.S. , Anal. Chern.
22, 871 (1950).
13. Cantor, D.M., Anal. Chem. 50, 1185 (1978).
14. Robinson, C.J., and Cook, G.L., Anal. Chem. 41, 1548 (1969).
15. Jackson, L.P., Alibright, C.S., and Poulson, R.E., Analytical Chemistry
of Liquid Fuel Sources, Ed. Uden, Siggia, and Jensen, 1977, pp.
232- 242.
16. Jackson, L.P., Alibright, C.S., and Jensen, H.B., Anal. Chem. 46, 604
(1974).
17. Ball, J.S. , Dinneen, G.U. , Smith, J.R. , Bailey, C.W. , and Van Meter,
R., md. and Eng. Chem. 41, 581 (1949).
18. Robinson, W.E. , Cummings, .J.J. , and Dinneen, G.U. , Geochim. et
Cosmochim. Acta 29, 249 (1965).
19. Doolittle, E.G., Anders, D.E., and Robinson, W.E., abstract of presen-
tation at the Pittsburgh Conference on Analytical Chemistry and Applied
Spectroscopy, Inc. , Cleveland, Ohio, March 6-10, 1972, Paper No. 167,
p. 168.
20. Anders, D.E. , and Robinson, W.E., BuMines RI 7737, 1973, 22 pp.
21. Paulson, R.E. , Jensen, H.B. , Duval , J.J. , Harris, F.L. , and Morandi,
J.R., Proc. 18th Annual ISA Analysis Instrumentation Symposium, San
Francisco, California, Anal. Instrum. 10, 193 (1972).
22. Couperus, P.A., Calgue, A.D.H., and Van Dongen, J.P.C.M., Org. Magn.
Reson. 8, 426 (1976).
415
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A CONTINUOUS FLOW BIOASSAY TECHNIQUE FOR ASSESSING
THE TOXICITY OF OIL SHALE RELATED EFFLUENTS:
PRELIMINARY RESULTS WITH TWO SPECIES OF CADDISFLY LARVAE
Peter P. Russell
Lawrence Berkeley Laboratory
University of California
Berkeley, California 94720
Vincent H. Resh and Thomas S. Flynn
Division of Entomology and Parasitology
University of California
Berkeley, California 94720
INTRODUCTION
The following report describes research results of a study to develop
preliminary methods for determining the response of selected species of
aquatic insects to an effluent associated with activities of the oil shale
industry.
The wastewater considered is a byproduct of in situ retorting of oil
shale deposits and includes water of hydration, combustion water and intrud-
ing groundwater. These waters typically contain organic compounds in con-
centrations of up to 2% and inorganics to 5%, depending on the processing
parameters and the extent of groundwater intrusion. The principal inorganic
components of the wastewater are aminonium, sodium and bicarbonate with
lesser but significant amounts of thiosulfate, chloride, sulfate and carbon-
ate.
Large scale commercial shale oil production has yet to take place.
However, the effect on aquatic insects of wastewaters from conventional
petroleum production may give an indication of the responses that would
result from oil shale processing effluents. The impact of crude oil on
natural communities of benthic macroinvertebrates has been studied by
Rosenberg and Weins’ and Vascotto, 2 of heavy bunker oil by McCauley, 3 and of
oil field brines by Mathis and Dorris. 4 Larval insect populations occurring
in oil refinery effluent holding ponds were reported by Tubb and Oorris. 5
Although the responses were markedly species specific, in general Diptera
were the most tolerant of petroleum pollution while Trichoptera, Ephemerop-
tera and Plecoptera were the most sensitive.
The potential for use of stream macroinvertebrates as water quality
indicators stems from their relative ease of collection, wide range of
tolerance to pollution, inability to leave a polluted area rapidly, and
416
-------
often ready adaptability to laboratory study. 6 The caddif lies (Insecta:
Trichoptera) in particular are frequently used as indicator organisms for
freshwater lotic habitats because of their nearly ubiquitous occurrence,
their frequent dominance in both diversity and abundance, and the narrow
pollution tolerances of many species. 7 8
Apparati to simulate the lotic milieu of caddisfly larvae have been
designed and successfully _employed without resorting to elaborate or
expensive support systems. 9 1 Many utilize flumes with or without water
recirculation but simpler designs involving small, agitated basins are also
effective. The most important considerations pertaining are water circula-
tion, maintenance of a high dissolved oxygen concentration, and prevention
of temperature extremes.
Two experimental runs were performed in which caddisfly larvae were
exposed to various dilutions of oil shale related wastewaters in a model
stream setting. Another run used a synthetic wastewater compounded from
ammonium carbonate. In addition, one run tested the effect of the experi-
mental apparatus, with no wastewater load, on the caddisfly larvae. The
activity of the larvae was observed in terms of its motility, prepupation
behavior and timing, and the abandonment of larval cases.
METHODS
Two species were used in the bioassays, Dicosinoecus gilvipes (Hagen)
(Trichoptera: Limnephilidae) and Gumaga nigricula (McLaughlin) (Trichop-
tera: Sericostomatidae). The test organisms were collected from Big Sulfur
Creek at The Geysers, Sonoma County, California. Last instar Gumaga and
late instar Dicosmoecus were used.
The laboratory bioassays were conducted in four mutually isolated,
parallel model streams. Figure 1 shows a schematic of the bioassay appa-
ratus and support system with one stream illustrated. Each stream consisted
of a riffle reach 120 cm long bounded by a pool at each end. The upstream
and downstream pools were 17-cm and 27.5-cm long, respectively. The width
of the riffles and pools was 9.5 cm, yielding a total volume of 12 liters
per stream. Flow was produced in the streams by pumping water from the
lower pools to the respective upper pools. Temperature control was effected
by means of a cooling coil in the lower pool of each stream. An array of
fluorescent tubes suspended over the riffles provided illumination.
Although well lit, no lights were situated directly over the pools. Chemi-
cal constancy of the stream waters was maintained by metering makeup water
to the streams on a continuous basis. The makeup water source was Berkeley
(East Bay Municipal Utilities District) tap water, dechlorinated by passage
through a column of activated carbon. Since concurrent aufwuchs bioassays
were being conducted in the riffle reaches of the model streams, nutrient
salts were added to the makeup water to promote primary productivity. The
millimolar (mM) concentrations of the salts in the tap water and of the
salts added are given on Table 1. The resulting concentrations are similar
to those suggested by Guillard with reduced nitrate content and neither
vitamin nor buffer addition. 14 In addition, the wastewater load indicated
417
-------
PRIMARY
COOLING
CIRCUIT
AUFWUCHS
SEED
RESERVOIR
SLOPED FLUME
CHEMOSTATS
HEAT EXCHANGER
PUBLIC WATER SUPPLY
-*
ACTIVATED;
CARBON i
FLUORESCENT LIGHTS
(on timerl
SUBSTRATES
SeCONDAR
COOLING
CIRCUIT
FLOW
CONTROL
VALVE
METERING
PUMP
RECIRCULAT10N
PUMP
FEED
WATER
RESERVOIR
INSECT
LARVAE CAGES
METERING
PUMP
METERING PUMP
NUTRIENT
STOCK
TO DRAIN RESERVOIR
COOLING UNIT
WASTE WATER
RESERVOIR
XBL 779-1907
Figure 1. Model Stream Schematic.
418
-------
TABLE 1. IONIC COMPOSITION OF MODEL STREAM MAKEUP WATER
All Values in mM, TTrace
Constituents
Mill
In Tap
imolar Concentration
Added Total
Ca
Mg M 9 SO 4 .7H 2 0)a
0.45
0.123
0.0
0.027
0.45
0.150
HCO 3
PO 4 (as H 3 P0 4 )
NO 3 (as NaNO 3 )
S O 2
K (as KC1)a
EDJ (as Na 2 EOTA)a
FE, 1 (as FeCl 3 gH 2 O
Cu (as CuSO 4 7H 2 O)
Zn.H. (as ZnSO 4 •7H 2 0)
Co÷ 1 (as CoC1 2 •6H 2 0)
Mn,, , (as MflCl 2 .4H 2 0)a
Mo (as Na 2 MoD 4 .2H 2 0)a
Ma_
Cl
C
0.865
0.0002
0.0015
0.120
O. 15
-
0. 02
-
<0.01
<0.0001
0.0003
<0.01
0.3045
0.169
0.16
0.0
0.0485
0.0986
0.0
0.064
0.01
0.010
<0.01
<0.01
<0.0001
0.0006
<0.01
0. 1246
0.096
0.03
0.865
0.0487
0.1001
0.120
0.079
0.01
0.012
T
1
1
0.0009
1
0.4291
0.265
0.19
Compound was used to supply the ion.
Typical values for East Bay Municipal Utilities District, average of
analyses for December 1976.
dSOurCe from several compounds listed above.
Not reported.
419
-------
for each stream by the design of each experimental run was fed with the
makeup water. The metered makeup water displaced an equal volume of stream
water to waste which left the system via overflow ports in stilling wells
connected to each stream. The rate of makeup water feed was adjusted to
deliver six stream volumes per day for a mean residence time of 4 hours.
Ammonia levels in the model stream water were measured by the phenol-
hypochiorite method.’ 5 Aside from hydrogen sulfide, which was probably
absent from the well aerated stream waters, this colorimetric technique is
quite insensitive to interfering substances as well as other nitrogenous
compounds. Interference from color-producing compounds in the wastewater
was probably negligible because of the high dilutions employed.
The test larvae were confined in cages of PVC screen located in the
lower pool of each stream. A minimum of 16 cm 3 (1 in. 3 ) was provided for
each individual. In addition to PVC, other materials in contact with the
stream water were Teflon, nylon, white epoxy paint, polyethylene, plexiglass
and steel.
The effluent dilutions in each stream for the four experimental runs
are presented on Table 2 along with the mean temperature, pH, and ammonia
concentrations. For each run, a one-way analysis of variance was performed
on the temperature and pH values summarized on Table 2. Stream number was
used as the independent variable in the analyses. The variations in
temperature were not significantly influenced by stream number in any of the
runs. The measured pH values, however, did vary significantly (p <0.015)
with stream number in each run except Run 2, when it was not significantly
different between streams. The stream dependence of the measured pH values
is primarily a reflection of the salts in the effluents and of the impact of
the primary productivity in each of the streams on the carbonate buffer
system. The streams 1 pH values for each run were compared using Scheffe’s
test, at the 0.05 level of significance, as implemented by the system of
computer programs from the Statistical Package for the Social Sciences. 16
The streams in each run whose pH values were not different from each other
at this level of significance are joined by the vertical bars on Table 2.
Run 1, 1/7/78 to 1/19/78, was a demonstration of the viability of
Gumaga nigricula larvae in the model streams with no wastewater addition.
The duration of Run 1 was 12 days. Each of the other three runs lasted 9
days. The test specimens were collected on 1/4/78 and within 6 hours were
transported to the laboratory where they were introduced to an aerated basin
of model stream water. The stock basin was maintained near the temperature
of the model streams. Larval mortality in the stock basin prior to the
beginning of each run was negligible. The test larvae were randomly dis-
tributed to the cages in the lower pool of each stream.
The wastewater examined in Run 2 was a water sample obtained from the
U.S. Department of Energy/Laramie Energy Technology Center as produced
during the Rock Springs Site 9 experimental in situ oil shale processing
project near Rock Springs, Wyoming.’ 7 This effluent, called Omega-9 water,
was filtered to effect a nominal 0.4 pm exclusion of suspended matter prior
420
-------
Table 2: PHYSICAL/CHEMICAL MEASUREMENTS ON THE MODEL STREAMS FOR THE FOUR EXPERiMENTAL RUNS. TEMPERATURES AND pH
VALUES ARE MEANS OF DAILY IN SITU MEASUREMENTS TAKEN IN RIFFLE REACHES. THE VERTICAL BARS TO ThE RIGHT OF
THE pH COLUMN CONNECT STREAMS WHOSE pH VALUES ARE NOT SIGNIFICANTLY DIFFERENT AT ThE 0.05 LEVEL AS MEASURED
BY SCHEFFE’S TEST. MEASURED AMMONIA VALUES ARE MEANS OF TRIPLICATE SAMPLES TAKEN ON THE LAST DAY OF EACH
RUN. NO AMMONIA MEASUREMENTS WERE TAKEN FOR RUN I.
Run No. Effluent Dilution
NONE
Stream No.
Temperature (C)
Mean (Range)
pH
Mean (Range)
Aimnonia
Calc.
(mM)
Mean.
1
2 FILTERED
OMEGA— 9
WATER
3 UNFILTERED
OMEGA- 9
WATER
4 ANMONIUM
CARSONATE
0
1
24.3
(23.7 — 25.1)
8.8
(8.6 — 9.0)
0
0
2
24.4
(23.9 — 25.3)
9.1
(8.9 — 9.4)
0
0
3
24.6
(24.0 — 25.3)
9.0
(8.7 — 9.3)
0
0
4
24.5
(24.0 — 25.3)
9.1
(8.9 — 9.3)
0
0
4
21.7
(20.8 — 22.9)
7.9
(7.0 — 8.5)
0
0
0.27%
3
21.8
(20.8 — 23.3)
8.1
(7.6 — 8.4)
0.59
0.44
1.06%
2
22.0
(21.0 — 23.3)
8.2
(1.7 — 8.5)
2.36
2.02
2.12%
1
21.7
(20.7 — 23.0)
8.3
(7.8 — 8.4)
4.73
4.22
0
0.27%
0.53%
1.06%
1
3
2
4
24.0
24.4
24.4
24.5
(22.9— 24.9)
(23.4 - 25.1)
(23.2 - 25.1)
(23.4 — 25.2)
8.6
7.6
7.6
8.2
(7.7 — 8.9) 1— — ,
(6.7 - 8.2)
(7.0 - 8.1)
(8.0— 8.3)l_._I
0
0.59
1.18
2.36
0
0.08
0.47
2.37
0
0.56 mM
2.26mM
4.52 mM
NH 3
NIl 3
NH 3
3
4
1
2
23.3
23.7
23.2
23.4
(22.3 — 24.2)
(22.7 — 24.6)
(22.1 — 24.1)
(22.4 — 24.3)
8.5
7.1
6.9
7.2
(7.5 — 9.1)
(6.9 — 7.3)
(6.0 — 8.1)
(6.4 — 8.4)
0
0.56
2.26
4.52
0
0.04
0.51
1.25
-------
Carbon, Carbonate (as CO
3)
Carbon, Inorganic (as C)
Carbon, Organic (as C)
Chemical Oxygen Demand
Conductivity (pmholcm)
Cyanide (as CN_)
Total (as CaCO 3 )
Ammoniab (as NH 3 )
Ammonium (as NH+
4)
Nitrogen, Kjeldahl (as N)
Nitrogen, Nitrate (as NO 3 )
Nitrogen, Oranic (as N)
Oil and Grease
pH
Phenol s
Phosphorus, Orthophosphate (as POE
4)
Fixed (550 C)
Total (103-105 C)
Total Dissolved
Sulfate (as S0
4)
500.0
3340.0 ±
1003.0 ±
8100.0 ±
20,400.0 ±
0.42 ± 2.9
110.0
3795.0 ± 390.0
830.0
420.0
3470.0 ±
3420.0 ±
0.17
148.0 - 630.0
580. 0
8.65 ± 0.26
60.0 ± 30.0
0.08 - 24.6
13430.0 ± 415.0
14210.0 ± 120.0
14210.0 ± 193.0
1990.0 ± 250.0
0.0
<20.0
280.0
2740.0 ± 730.0
123.0 ± 18.0
TABLE 3. WATER QUALITY CHARACTERIZATION OF OMEGA-9 WATERa
Alkalinity (as CaCO 3 ) 16,200.0 ±480.0
Biochemical Oxygen Demand, 5-day 740.0
Carbon, Bicarbonate (as HCO-
3)
390.0
192.0
5700.0
3840.0
Hardness,
Nitrogen,
Nitrogen,
Solids,
Solids,
Solids,
Sulfur,
Sul fur,
Sulfur,
Sulfur,
Sulfide (as S)
Sulfite (as S)
Tetrathionate (as S40)
Sulfur, Thiosulfate (as S 2 O
3)
Sulfur, Thiocyanate (as SCN_)
A11 values are mg/l unless ot erwise noted. Iö
This is the sum of NH 3 and NH 4 .
422
-------
to its distribution to research laboratories. A description of the filtered
Omega-9 water in terms of its water quality parameters is given on Table 3.
Although the Omega-9 water sample was probably the best in situ retort water
currently available, it is not necessarily representai T ie of waters which
may be produced during full scale commercial in situ oil shale processing.
Consequently the results are strictly applicable to this water only, the
stream water effluent concentrations tested in Run 2 were 2.12%, 1.06% and
0.27% as well as a control stream with no Omega-9 water loading. In Run 2,
2/13/78 to 2/22/78, Gumaga nigricula larvae collected from Big Sulfur Creek
on 2/12/78 were used. Transportation to the laboratory was the same as with
the 1/4/78 collection. Unlike the earlier run, allochthonous leaf matter
from the collection site was included in the larvae cages of each stream.
On 3/13/78 Gumaga nigricula larvae for Run 3, 3/15/78 to 3/24/78, were
collected and transported to the laboratory as in the previous runs.
Allochthonous stream leaf matter was collected for introduction to the
insect cages in the model streams. Unfiltered Omega-9 water was used for
Run 3. The concentrations used were 1.06%, 0.53%, 0.27% and 0%.
Run 4 was designed to determine the effect of ammonium carbonate,
potentially a biolobically critical component of untreated oil shale related
effluents, on two species of caddisfly larvae. Gumaga nigricula and
Dicosmoecus gilvipes larve were collected on 4121/78 from Big Sulfur Creek
along with a supply of allochthonous leaf matter for the run, 4/22/78 to
5t1/78. The ammonium carbonate concentrations tested were 4.52 mM, 2.26 mM,
0.56 mM plus a control receiving no salt.
A period of at least one week was allowed to elapse between runs during
which the streams were flushed with makeup water containing no wastewater.
After each run the riffles and PVC insect cages of each stream were scrubbed
to remove any aufwuchs accumulation. The stream chosen as the control was
changed for each run.
RESULTS
Table 4 details the response of the caddisfly larvae for each of the
three runs in which wastewater was applied. The disposition of the initial
number of larvae in each stream is partitioned between the categories
“active,” “prepupae,” “pupae,” “dead or moribund” and “missing.” To warrant
11 active” status a larva must extend its legs beyond the case and crawl
around. This criterion was usually easily observed as the species studied
were quite motile in the stream cages. In some instances larvae had turned
around in their cases or begun pupating by sealing one end of their cases
and/or attaching their cases to the cage or the allochthonous leaf matter
with silk threads. These larvae were designated “active” as long as extend-
ed moving legs were visible. All larvae not active for longer than a day
were removed from the stream and preserved. At the end of each of the last
three runs all larvae were preserved for postmortem examination when the
inactive individuals were determined to be “prepupae” (in the sense of
Wiggins),’ 9 “pupae” or “dead or moribund.” Also designated “dead or mori-
bund” were larvae which abandoned their cases whether still motile or not.
423
-------
TABLE 4. INSECT LARVAE BIOASSAY RESULTS
Initial
Dead
or
No. Active
Prepupae Pupae moribund Missing
RUN 2
Gumaga nigricula 0.0% 17 16 0 1 0 0
0.27% 15 15 0 0 0 0
Filtered Omega-9 1.06% 15 14 1 0 0 0
Water 2.12% 16 14 2 0 0 0
RUN 3
Gumaga nigricula 0.0% 18 13 2 2 1 0
0.27% 14 9 2 3 0 0
Unfiltered 0.53% 18 12 3 2 1 0
Omega-9 Water 1.06% 18 13 2 0 3 0
RUN 4
Gumaga nigricula 0.0mM 9 5 2 1 0 1
0.56mM 11 8 3 0 0 0
Ammonium 2.26mM 10 5 3 1 1 0
Carbonate 4.52mM 9 4 3 1 1 0
RUN 4
Dicosmoecus 0.0mM 10 9 0 0 0 1
gilvipes
0.56mM 9 8 0 0 1 0
Ammonium 2.26mM 9 2 0 0 1 4
Carbonate 4.52mM 10 4 0 0 4 2
424
-------
The decision to classify these larvae as dead stems from the assumption that
the decased condition would be fatal within a short period of time. Mis-
sing” larvae were noted in Run 4 only.
The results of Run 1 are not shown on Table 4 because only one inactive
specimen occurred over the course of the 12 days. Of the ten Gumaga
nigricula larvae in each stream at the beginning of this run, one larva from
stream number 1 sealed the ends of its case and attached itself to the PVC
screen cage as though entering pupation. The specimen was unfortuantely not
preserved to later determine if the pupation process had begun. This larva
became inactive on the fourth day of the run. On the seventh day of Run 1,
one larva in stream number 2 became inactive and sealed the ends of its case
but by the next day it had unsealed the case and begun crawling around
again. It remained active through the end of the run.
DISCUSSION
The survival and activity data for two species of caddisflies deter-
mined from Runs 1-4 provide an example of the potential information that can
be obtained from bioassay analysis to assess the environmental effects of
oil shale related effluents. As discussed below, these results are intended
as a demonstration of this bioassay technique. Undoubtedly, if this
approach is expanded or modified to answer specific biological questions
regarding the effects of particular effluents, a useful and additional
dimension to water monitoring programs associated with the development of
the oil shale industry will be provided.
Run 1 demonstrates that Gumaga nigricula larvae remain “active” under
model stream conditions for at least 12 days. Since the duration of the
subsequent runs was only nine days each, it is expected that the larvae
could adequately accommodate any stream-induced stress for that period of
time. The survival of Gumaga nigricula as larvae in the control streams in
the following three runs was usually not as high as with Run 1. Most Gumaga
nigricula larvae remained “active” in the Run 2 control stream (94%); how-
ever, in Runs 3 and 4 only 72% and 56%, respectively, were “active: in the
control streams for nine days.
Perhaps the elevated temperature of the streams precipitated pupation
in the Gumaga nigricula which were obtained from a much cooler environment,
10°C to 12°C. Since specimen collection was never more than three days from
the beginning of each of the runs, stress from residence in the stock basin
was not likely to be responsible for the inactivity. Probably the condition
of the larvae in Big Sulfur Creek at the time of collection was the most
important factor influencing the number of inactive specimens in the control
streams. Although the streams were allowed time to flush out any effluent
residual from the previous run before commencing a new run, the possibility
exists that some toxic components may have adsorbed to the stream surfaces
and been slowly released to the stream waters of the subsequent run. No
analyses were performed to assess the significance of this mechanism. It is
likely that any chemical carryover was minor because all unscrubbed stream
surfaces were covered with a dense mat of aufwuchs that was continually
425
-------
sloughing cells, which were carried to waste via the overflow, and regener-
ating itself with new cell growth. Thus any toxins concentrating in the
aufwuchs would presumably be depleted during the interrun periods. Given
the heavy aufwuchs growth, probably negligible amounts of effluent constit-
uents were adsorbed to the underlying model stream surfaces. It should be
noted that the phenomenon of fewer control stream larvae remaining “active”
with each successive run could be attributed either to progressive maturity
of the last instar larvae collected from Big Sulfur Creek for each run, or
to residual toxicity buildup in the streams. Neither hypothesis can be
disproven by the data, but distinct differences in larval responses between
the proven by the data, but distinct differences in larval responses between
the control and test streams can be observed nevertheless.
Dicosmoecus gilvipes were not collected for 12 days of rearing in the
model streams as was done for Gumaga nigricula in Run 1. In Run 4, however,
9 of the 10 Dicosmoecus gilvipes larvae initially in the stream were
“active” after 9 days. Presumably there were no inherent stresses to the
Dicosmoecus gilvipes larvae in the other three streams.
Table 4 shows that with concentrations of up to 2% filtered Omega-9
water, no acute toxic responses were observed during the 9 days of exposure.
There was a minor trend for prepupae to form with more than 1% of this
effluent in the stream water.
In Run 3 unfiltered Omega-9 water was used as the test effluent. As in
Run 2, no appreciable decrease in “active” individuals was observed in the
streams receiving effluent as compared with the control stream. At the end
of day 4 in Run 3 however, there did appear to be a tendency for the larvae
in the streams with the higher Omega-9 water concentrations to become
inactive. This distinction disappeared by the conclusion of the run on day
9. In contrast to the filtered Omega-9 water run, dead larvae were found at
the higher concentrations of effluent.
The synthetic, ammonium carbonate wastewater used in Run 4 corresponds
to the ammonium levels found in streams with up to about 2% Omega-9 water
concentration. As in Run 2 with these effluent ammonium concentrations, no
pronounced difference was observed in the numbers of active Gumaga nigricula
larvae between the test and control streams of Run 4. This result was
expected as the anunonium concentrations used in Run 4 duplicated those of
Run 2; however, with Run 4 no other oil shale related constituents were
present. At most the synthetic, ammonium carbonate wastewater woudi exhibit
a toxic response no greater than that of corresponding dilutions of Omega-9
water, unless the other oil shale effluent components are antagonistic
toward salt toxicity. As the Omega-9 water dilutions used in Run 2 were
apparently too great to elicit demonstrable reductions in Gumaga nigricula
activity in 9 days, the ammonium carbonate dilutions of Run 4 similarly
proved to be too great.
The Dicosmoecus gilvipes larvae that were exposed to the synthetic
ammonium carbonate wastewater along with the Gumaga nigricula in Run 4
showed a clear sensitivity to the higher concentrations. The streams
426
-------
receiving ammonium carbonate to computed stream concentrations of 2.26 mM
and 4.52 mM had significanity fewer numbers of “active 11 Dicosmoecus gilvipes
larvae than the streams with 0.56 mM and 0 mM ammonium carbonate dilutions.
n the two higher concentration streams, a total of six Dicosmoecus gilvipes
larvae were missing over the course of the 9-day run as compared with only
one larva in the other two streams. Although the precise fate of the
missing larvae is unclear, probably they were either prompted to climb up
the cage screening above the water line and into the lower reservoir of the
stream in an avoidance response, or they were cannibalized by other larvae
and their cases used as case maintenance material by the remaining active
Dicosmoecus gilvipes larvae. Any larvae climbing into the lower reservoir
would be sucked into the recirculating pond and macerated. Although probab-
ly the missing larvae should properly be considered as demonstrating a toxic
response, particularly since only one missing larvae occurred in the control
and low concentration streams, the conclusions drawn from Run 4 remain the
same regardless of the disposition of the missing larvae.
Note is made that in Runs 3 and 4 the measured ammonia concentrations
deviate markedly from the computed concentrations (Table 2). In Run 2 the
correspondence was much closer. A definitive explanation for this deviation
awaits further investigation. Several hypotheses are evident, however. It
is possible that the primary producer organisms of the aufwuchs communities
in the streams selectively shifted to those species favoring ammonia as a
nitrogen source over nitrate. In each case the ammonia determinations were
made at the end of the run when the aufwuchs biomass was the greatest and
had the greatest nitrogen uptake. -With this eventuality the ammonia concen-
trations in the streams would be reduced to the degree that this nutrietn
was removed by the aufwuchs biomass. In Run 2 presumably the aufwuchs
conmrnnities still preferred to meet their nitrogen requirement with nitrate.
The reason for any inorganic nitrogen uptake shift by the primary producers
is not clear since 1.4 mg/i of nitrate nitrogen was always provided in the
makeup water (Table 1). Both ammonia and nitrate are readily utilizable
nitrogen sources for most species of algae and both were present in excess
of algal requirements in the streams receiving effluent additions. A more
probable explanation for the discrepancy between computed and measured
amuonia concentrations is nitrification of the ammonia to nitrite and then
nitrate by nitrifying bacteria whose growth was favored by the high ammonia
levels in the streams. Neither nitrite nor nitrate ‘is measured in teh
hypochiorite test for ammonia. This hypothesis would adequately account for
the increased divergence of the calculated ammonia concentrations from the
measured values in each successive run if the nitrifier activity in the
stream systems increased with time. It is also possible that the ammonia
was evolved as a gas. However, arguments for this mechanism are weakened by
the fact that the lower pH of some of the streams in the last two runs
should have prevented ammonia evolution and yet, the large deviations be-
tween the computed and measured concentration occurred only in the later
runs. The accuracy of the phenolhypochlorite method is supported by the
close agreement of the standard curves generated from a stock ammonia solu-
tion for each run.
427
-------
Limitations in the design of this study include the following: (1) too
few organisms were involved in the bioassays; (2) a finer discrimination of
larval activity responses is necessary, since other more subtle sublethal
effects of exposure to oil shale related effluents are ignored by this
classification scheme. In terms of this latter point, it should be noted
that the Gumaga nigricula larvae of the highest effluent concentration
streams in Runs 2 and 4 were visibly more sluggish than individuals from the
other streams. These larvae exposed to the highest effluent concentrations
moved around their cages less and appeared to have difficulty keeping
balance and maintaining a grip on the cage screening. Another response
resisting description under the current scheme is the phenomenon of case
abandonment by the larval caddisf lies. Both caddisfly species sometimes
left their cases but this occurrence in the Dicosmoecus gilvipse larvae was
much more common. A system whereby these responses can be quantitatively
measured would be preferable to the preliminary methods that we chose.
An appropriate measure of caution must be used in extrapolating labora-
tory results of effluent studies to those results that would be obtained in
natural habitat conditions. The laboratory conditions include elevated
temperature, artificial illumination, inorganic nutrient supplementation of
the stream water, and possible exposure to exotic sloughed aufwuchs orga-
nisms, all of which may significantly affect the response of the organisms
under study. Confirmation of the results obtained here by field bioassays
must be obtained prior to drawing firm conclusions as to the effect of these
oil shale related effluents on aquatic biota.
CONCLUSIONS
1. Gumaga nigricula larvae can be maintained in the laboratory model
streams with no effluent loading at nearly 100% survival for at least
12 days.
2. Concentrations of filtered Omega-9 water up to 2.12% and unfiltered
Omega-9 water up to 1.06% produce no demonstrable reductions in Gumaga
nigricula “activity” after 9 days of exposure in the model streams.
3. Gumaga nigricula “activity” is not notably reduced by rearing in
streams receiving up to 4.52 mM concentrations of ammonium carbonate in
the makeup water for 9 days.
4. The “activity” of Dicosmoecus gilvipes larvae in the laboratory model
streams that were fed ammonium carbonate concentrations of 4.52 mM and
2.26 mM is significantly reduced but not at a dilution of 0.56 mM.
5. Dicosmoecus gilvipes larvae are potentially more sensitive indicators
of environmental stress from ammonia-containing effluents than are
Gumaga nigricula larvae.
6. This continuous flow bioassay technique has many potential applications
in assessing the toxicity of oil shale related effluents.
428
-------
ACKNOWLEDGMENTS
This research was supported largely by grants from the Department of
Energy’s Laramie Energy Technology Center and the U.S. Department of the
Interior.
REFERENCES
1. Rosenberg, D.M. and A.P. Wiens. Community and Species Responses of
Chironomidae (Diptera) to Contamination of Fresh Water by Crude Oil and
Petroleum Products, with Special Reference to the Trail River,
Northwest Territories. J Fish Res Board Can. 33: 1955-1963, 1976.
2. Vascotto, G.L. Zoobenthic Responses to a Controlled Crude Oil Spill in
an Artic Stream. (Presented at the 26th Annual Meeting of the North
American Benthological Society. Winnipeg, Canada, May 1978).
3. McCauley, R.N. The Biological Effects of Oil Pollution in a River.
Limnol Oceanogr. 10(4): 475-486, 1966.
4. Mathis, B.J. and T.C. Dorris. Community Structure of Benthic Macro-
invertebrates in an Intermittent Stream Receiving Oil Field Brines. Am
Midi Nat. 80(2): 428-439, 1968.
5. Tubb, R.A. and T.C. Dorris. Herbivorous Insect Populations in Oil
Refinery Effluent Holding Pond Series. Limnol Oceanogr. 10: 121-134,
1965.
6. Goodnight, C.J. The Use of Aquatic Macroinvertebrates as Indicators of
Stream Pollution. Trans Amer Microsc Soc. 92(1): 1-13, 1973.
7. Resh, V.H., and J.D. Unzicker. Water Quality Monitoring and Aquatic
Organisms: The Importance of Species Identification. J Water Pollut
Control Fed. 47(1): 9-19, 1975.
8. Wiggins, G.B. Caddisfly Communities as Indicators. (Presented at the
26th Annual Meeting of the North American Benthological Society.
Winnipeg, Canada, May 1978.)
9. Webster, D.A. and P.C. Webster. Influence of Water Current on Case
Weight in Larvae of the Caddisfly, Geora calcarata Banks. Can Entomol.
75(6): 105-108, 1943.
10. Craig, D.A. Techniques for Rearing Stream Dwelling Oranisms in the
Laboratory. Tuatara. 14(2): 65-72, 1966.
U. Mason, W.T., Jr. and P.A. Lewis. Rearing Devices for Stream Insect
Larvae. Prog Fish Cult. 32(1): 61-62, 1970.
12. Hildebrand, S.G. The Relation of Drift to Benthos Density and Food
Level in an Artificial Stream. Limnol Oceanogr. 19(6): 951-957,
1974.
429
-------
13. Merritt, R.W., K.W. Cuimwins, and V.H. Resh. Collecting, Sampling, and
Rearing Methods for Aquatic Insects. In: An Introduction to the
Aquatic Insects of North America, Merritt, R.W. and Cummins, K.W.
(eds.). Debuque, Kendall/Hunt Publishing Co., 441 pp., 1978.
14. Nichols, H.W. Growth Media--Freshwater. In: Handbook of Phycological
Methods, Culture Media and Growth Measurements. Janet R. Stein (ed.).
London, Cambridge Univ. Press, 488 pp., 1973.
15. Solórzano. L. Determination of Ammonia in Natural Waters by the Phe-
nolhypochlorite Method. Limnol Oceanogr. 14(5): 799-801, 1969.
16. Kim, J. and F.J. Kohout. Analysis of Variance and Covariance: Sub-
programs ANOVA and ONEWAY. In: Statistical Package for the Social
Sciences, Second Edition, by Nie, N.H., Hull, C.H., Jenkins, J.G.,
Steinbrenner, K. and Bent, D.H. New York, McGraw-Hill, 1975.
17. Farrier, D.S., R.E. Poulson, Q.D. Skinner, J.C. Adams and J.P. Bower.
Acquisition, Processing, and Storage for Environmental Research of
Aqueous Effluents Derived from In Situ Oil Shale Processing. In:
Proceedings of the Second Pacific Chemical Engineering Congress. 2:
1031-1035, 1977.
18. Fox, J.P., DS. Farrier and R.E. Poulson. Chemical Characterization
and Analytical Considerations for an In Situ Oil Shale Process Water.
LETC/RI-78/7, 47 pp., 1978.
19. Wiggins, G.B. Larvae of the North American Caddisfly Genera. Toronto,
Univ. Toronto Press, 401 pp., 1977.
430
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BIOLOGICAL MONITORING OF OIL SHALE PRODUCTS AND EFFLUENTS
USING SHORT TERM GENETIC ANALYSES
T.K. Rao, J.L. Epler and M.R. Guerin
Biology Division and Analytical Chemistry Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37830
J.J. Schmidt—Collerus and L. Leffler
Denver Research Institute
University of Denver
Denver, Colorado 80208
ABSTRACT
The long term health hazards such as mutagenesis, carcinogenesis and
teratogenesis due to the exposure to crude shale oil, particulate pollutants
and the leachates from raw or spent shale constitute a major concern in the
development of shale oil technology. In order to monitor such biological
effects, we have applied short term genetic analyses with the exemplary test
materials. The Salmonella/microsomal activation system (Ames assay) was
generally applicable but only upon chemical fractionation. The Stedman
liquid/liquid extraction procedure or the Sephadex gel filtration (LH-20)
technique were effectively utilized. Mutagenicity analyses with various
crude oils and product water have revealed biological activity in the basic
(aromatic amine fractions) or in the neutral (polyaromatic hydrocarbon
fraction) fractions. Extracts and chromatographically isolated materials
from raw and spent shale were subjected to mutagenicity studies. Mutagenic
activity was noted and correlates with the biological activity of compounds
that are either identified or predicted to occur in these materials.
Comparison to other energy technologies and overall health hazard of the
test materials will be discussed.
I NTRODUCT ION
The long term health hazards such as toxicity, mutagenicity, carcino-
genicity and teratogenicity are of great concern in the development of new
alternate energy technologies, including the oil shale industry. Exposure
of the personnel in industry as well as consumers to the oil shale deposits,
contaminated aqueous effluents and airborne particulates might constitute
the biological hazard. In addition entry of the leachates from raw and
spent shale into drinking water systems represents another potential route
of entry into human environment. Thus, the need for biological monitoring
of such processes is obvious and every effort should be made to minimize the
toxic and genotoxic effects associated with this industry.
431
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The approach for biological monitoring of the shale oil industry is
twofold. (1) Development of an adequate biological testing (quality con-
trol) to monitor various processes, effluents or personnel in the develop-
ment of the engineering and control technology. Various genetic-toxicologi-
cal test procedures are now available for the detection and isolation of
biological hazard. However, it is necessary to identify which of these
procedures can be advantageously, appropriately and economically applied
(quality control) in determining biological effects. (2) Once the base line
biological data is developed, it is necessary to periodically monitor the
processes when they are completely developed for full size commercial pro-
duction.
In order to rapidly and inexpensively ascertain the potential muta-
genicity hazards of various test materials, we have examined the feasibility
of using short term genetic assays to predict and, in some cases, aid in
isolating and identifying chemical mutagens. Furthermore, recent studies 1
have shown that there is an extremely high correlation between the ability
of a compound to induce genetic damage and the carcinogenic potential of the
compound. Thus, the mutagenicity assay might act as prescreen for carcino-
gens. In the studies presented here we have used the Ames Salmonela riisti-
dine reversion assay 1 to assay the mutagenic potential of chemically
fractionated 2 crude shale oil, product water from shale oil process 3 and
chromatographically separated leachates and extracts from raw and spent
shale.
In order to maintain the uniformity of samples that are tested at
various laboratories, the repository 5 at the Oak Ridge National Laboratory,
Oak Ridge (supported by the U.S. Environmental Protection Agency), collects
and supplies adequate amounts of exemplary materials from the oil shale and
other related energy technologies. Materials used in this study were
obtained from the ORNL repository and Dr. J.J. Schmidt-Collerus, Denver
Research Institute, Denver, Colorado.
MATERIALS AND METHODS
A. Mutagenicity Testing-Methodology
The Salmonella typhimurium strains used in various assays are listed
below. All were obtained through the courtesy of Dr. Bruce Ames, Berkeley,
California.
TA100 hisG46, uvrB , rfa (missense plus R factor)
TA98 hisD3O52, uvrB , rfa (frameshift plus R factor)
In screening of fractionated materials, the two strains TA98 and TA100
were generally employed. Standard experimental procedures have been given
by Ames et al. 1 Briefly, the strain to be treated with the potential muta-
gen(s) is added to soft agar containing a low level of histidine and biotin
along with varying amounts of the test substances. The suspension contain-
ing approximately 2 x 108 bacteria is overlaid on minimal agar plates. The
bacteria undergo several divisions with the reduced level of histidine, thus
432
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forming a light lawn of background growth on the plate and allowing the
mutagen to act. Revertants to the wild-type state appear as abvious large
colonies on the plate. The assay can be quantitated with respect to dose
(added amount) of mutagen and modified to include hbon the piateH treatment
with the liver homogenate required to metabolically activate many compounds.
Fractions and/or control compounds to be tested were suspended in
dimethyl sulfoxide (supplied sterile, spectrophotometric grade from Schwarz/
Mann) to concentrations in the range of 10-50 mg/mi solids. Normally, the
fraction was tested with the plate assay over at least a thousandfold con-
centration range with the two tester strains TA98 and TA100. Revertant
colonies were counted after 48 h incubation. Data were recorded and plotted
vs added concentration, and the slope of the induction curve was determined
(Figure 1). It is assumed that the slope of the linear dose-response range
reflects the mutagenic activity. Metabolic activation for procarcinogens
was incorporated into the assay by the addition of rat liver microsomal
ensymes (liver S-9 mix from rats induced with Aroclor). Routine controls
demonstrating the sterility of samples, enzyme or rat liver 5-9 prepara-
tions, and reagents were included. Positive controls with known mutagens
were also included in order to recheck strain response and enzyme prepara-
tions. All solvents used were nonmutagenic in the bacterial test system.
See Figure 1.
B. Samples-Source
(1) A crude oil sample from the aboveground simulated in situ oil shale
retorting process; (2) the aqueous product water consisting of the
centrifuged water of combustion from the same process (both samples 2 and 3
courtesy of Dr. Ricahrd Poulson of the Laramie Energy Research Center).
(3) Carbonaceous spent shale from the TOSCO Process was obtained by the
courtesy and cooperation of Colony Corporationa(ARCO) and the second from
the Paraho (Direct Mode) Pilot processing plant.
C. Chemical Fractionation
(1) Class fractionation scheme: The fractionation technique developed
by Swain et a ). 2 and modified by Bell et al. 6 was used to fractionate the
oil samples and the aqueous samples. The technique involves acid-base
separation using liquid/liquid partitioning. The neutral fraction was
fractionated into secondary fractions using Fiorisil column and elution with
hexane, benzene, ether and methanol. The acidic and basic fractions were
separated into ether or water soluble secondary fractions.
(2) Since acid-base separation technique involves harsh chemical treat-
ment, a much gentler technique developed by Jones et al. 7 using Sephadex
LH-20 gel filtration technique was used. The technique utilizes the separa
tion of hydrophilic and lipophilic fractions in Step I, separation of
polymeric, sieved and hydrogen-bonding fraction from lipophilics in the Step
Obtained by Dr. J.J. Schmidt-Collerus.
433
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II and finally separation of aliphatic and aromatic fractions (ring size)
from sieved fraction in the Step III.
(3) Extraction of raw and spent shale: each shale sample was Soxhiet
extracted for 6 days with benzene and the residue of benzene solubles was
then concentrated by distilling off the solvent. 4 The final concentration
of the benzene solubles lies in the range of 10-20 mg/mi (a range suitable
for TIC separation). Chromatographic separation of the complex benzene
extract into polynuclear aromatic hydrocarbons (PAN, neutral), polar com-
pounds (azaarenes, phenols, etc.) and other nonpolar hydrocarbons was
achieved by using one-dimensional silica gel thin layer chromatography and
was described previously. 4 The saturated hydrocarbons run with the solvent
at the top of the plate; PAN’s run in a group forming a wide fluorescing
band in the top portion of the plate. Polar compounds migrate through the
lower half of the plate separating into bands while most polar materials
remain at the origin. The PAH residue was subjected to a second separation
on a silica gel plate to achieve a higher quality of separation. Separation
of the individual PAN compounds was achieved with reasonable success on a
two—dimensional mixed thin-layer chromatographic plate.
RESULTS AND DISCUSSION
A. Oil Samples
In the investigation of the feasibility of the coupled analytical
mutagenicity assay approach, we examined the mutagenic activity of fraction-
ated in situ retorted shale oil sample (simulated). Each primary fraction
was assayed with the Ames strains. The distribution by weight of the test
materials, the “specific activity’ t (revertants/mg) of each fraction, and the
contribution of each fraction to the mutagenic potential of the starting
material (product of weight percent and specific activity) are listed in
Table 1. Data are given for the frameshift strain TA98 with metabolic
activation with enzyme preparations from Arocior 1254-induced rats. The
shale oil contained significant activity in the neutral fractions and in
other fractions, particularly in the Basic Fraction (Br,, ether soluble).
Note that the sum of activities from the neutral subfracttbns corresponds to
the value obtained from the unfractionated neutral material.
Figure 1 shows the dose-response curves for two of the shale oil frac-
tions. The slope of the linear portion of the induction curve represents
the revertants/mg of the fraction (specific activity).
Comparable evaluations of crude synthetic fuels from coal liquefaction
processes have pointed to consistently higher mutagenic potentials in syn-
thetic fuels than in the materials assayed here. 8
B. Aqueous Sample
In order to extend the techniques to an aqueous material that might
have more environmental importance, we assayed the centrifuged product water
from the aboveground in situ retorting process (Table 2). Although a number
434
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of highly active materials occur, again in the basic fractions, the overall
contribution of the contaminating organic portion appears to be low. Note
also that the neutral portion, usually comprised of water insoluble polyaro-
matic hydrocarbons, contains little mutagenic activity in this aqueous
sample.
Since the LH-20 gel filtration technique is a much gentler system, we
have examined the feasibility of testing shale oil samples fractionated with
LH-20 fractionation scheme. The results are given in Table 3 which shows
the general applicability of this technique for biological testing. The
activity was recoverable completely (see summation column, Table 3). Total
mutagenic activity recovered after fractionation (280 rev/mg) is comparable
to the activity obtained with the acid-base fractionation technique (178
‘ev/mg).
Table 4 lists the results of mutagenicity testing (strain TA98 and
TA100, with metabolic activation) with the extracted and chromatographically
separated materials from TOSCO II series (CSA II represents test samples
from TOSCO process spent shale). The first sample, Diffuse fluorescent
material combined (D I COM) was derived from a benzene extract. The sample
represents the combined material from 5 TLC plates and is, in general,
similar to the neutral or polycyclic aromatic hydrocarbon fraction from
acid-base fractionation scheme. The main constituents are probably a homol-
ogous series of alkyl substituted and poly-condensed substituted aromatic
hydrocarbons. The mutagenicity testing results detect mutagenic activity.
The next samples represent the polar material, from TOSCO II spent
shale. The recovered materials are roughly analogous to a basic fraction by
the acid-base extraction technique. Predictions would include nitrogenous
polycondensed species, acridine, dibenzacridines, along with some acids,
phenols and high molecular weight aromatic amines. Mutagenicity can be
detected with the Salmonella system. Note in Table 2 that samples 0-1135
and 0-1165 differ quantitatively and qualitatively. Conceivably, minor
changes in the extraction procedures and chromatography can alter the bio-
assay results.
When the total benzene soluble fraction from Paraho spent shale is
analyzed, toxicity masks any mutagenic effect that might be present. How-
ever, a similar crude extract from raw shale (APVI 1 ) air particulate was
•assayable and mutagenic activity was detectable.
C. Utility of Short Term Tests for Mutagenicity
The use of short term tests for mutagenicity coupled with chemical
fractionation and analyses of test materials appears to be a valid research
approach. Their utility in predicting potential genetic hazard is obvious.
The use of the mutagenicity data as a prescreen for carcinogenesis may also
be of value, but probably not in a quantitative sense. Too many factors
modify the whole-animal carcinogensis response to expect the type of muta-
genicity screening used here to directly reflect the extent of carcinogenic
potential.
435
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The biological testing is a complex phenomenon which warrants extreme
caution in its application and interpretation. Implication of various
genetic and biochemical variables was previously described. 9 The choice of
bacterial strain or the inducer involved in metabolic activation could alter
the test results. Furthermore, no one short term test should be relied on
for testing. Other system 1 ° might complement one another.
However, in the contest of a prescreen for mutagenesis, and perhaps for
carcinogenesis, the testing of crude mixtures with the Ames system is a
feasible approach provided that appropriate fractionation, chemical analy-
ses, and validation accompany the bioassays. A more important use of the
short term mutagenicity tests may lie in the dissection of a known response
in a crude material and the tracing of the effect to the ultimate organic
component(s) responsible for the potential damage. The need exists for
standardizing test procedures so that they can be routinely utilized for
biological monitoring of processes and process streams associated with oil
shale technology.
ACKNOWLEDGEMENTS
We are indebted to Ms. K.B. Ellis, Ms. Della W. Ramey, Ms. Jeannette
King, and Mr. Ira Rubin for the bulk of the technical work involved. We
also thank the staff of the Environmental Mutagen Information Center for
their help.
REFERENCES
1. Ames, B.N., J. McCann, and E. Yamasaki, Methods for detecting carcino-
gens and mutagens with the Salmonella/mammalian-microsome mutagenicity
test, Mutat. Res. 31: 347-364 (1975).
2. Swain, A.P., J.E. Cooper and R.L. Stedman, Large scale fractionation of
cigarette smoke condensate for biologic investigations, Cancer Res. 29:
579-583 (1969).
3. Epler, J.L. T.K. Rao, and M.R. Guerin, Evaluation of feasibility of
mutagenic testing of shale oil products and effluents, Environ. Health
Perspect., in press.
4. Schmidt-Collerus, J.J.., L. Leffler, J.L. Epler and T.K. Rao, Detection
of mutagenic components in oil shale derived materials by combined
chemical and biological analyses, (in preparation).
5. Coffin, DL., M.R. Guerin and W.H. Griest, The interagency program in
health effects of synthetic fosssil fuel technologies. Operation of a
materials repository. Proceedings of the First Oak Ridge National
Laboratory Life Sciences Symposium, Gatlinburg, Tennessee, September
1978 (in press).
6. Bell, J.H., S. Ireland, and A.W. Spears, Identification of aromatic
ketones in ciaarette smoke condensate. Anal. Chem. 41: 310-313
(1969).
436
-------
7. Jones, A.R., M.R. Guerin, and B.R. Clark, Preparative-scale liquid
chromatographic fractionation of crude oils derived from coal and
shale, Analytical Chemistry, 49: 1766-1771 (1977).
8. Epler, J.L., J.A. Young, A.A. Hardigree, T.K. Rao, M.R. Guerin, I.B.
Rubin, C. -h. Ho and B.R. Clark, Analytical and biological analyses of
test materials from the synthetic fuel technologies. I. Mutagenicity
of crude oils determined by the Salmonella typhimurium/microsomal
activation system, Mutat. Res. 57: 265-276 (1978).
9. Rao, T.K., J.A. Young, A.A. Hardigree, W. Winton and J.L. Epler,
Analytical and biological analyses of test materials from the synthetic
fuel technologies. II. Mutagenicity of organic constituents from the
fractionated synthetic fuels, Mutat. Res. 54: 185-191 (1978).
10. de Serres, F.J. , The utility of short term tests for mutagenicity,
Mutat. Res. 38: 1-2 (1976).
437
-------
A
2
/0
0 d.5
0 1.0 2.0
0
mg/PLATE
rigure 1. Mutagencity Tests of Crude Oil Sample. (a) From the Above-
ground Simulated In Situ Oil Shale Retorting Process and
(b) from the Supernatant of Centrifuged Process Water from
(a).
TA-98
Shale oil
(With aroclor S—9)
c’J
0
w
I—
-J
Cr)
I-
10-
8-
6-
4-
2-
0
20
10
0
B
I I I I
TA-
98
z
I—
w
>
w
4
•c/)
Product water
9 BE
(With
aroclor S-9)
0
438
-------
TABLE 1
DISTRIBUTION OF MUTAGEMIC ACTIVITY IN FRACT1O S OF SUALE OILSa
Shale Oil
Fractionb Relative weight, Specific activity, Weighted
% of total rev/mgc activit ,
rev/mgU
1. NaOH 1.02 256 3
2. WA 1 0.05 185 >1
3. WAE 1.23 52 1
4. SA 1 0.09 0
5. SAE 0.26 159 >1
6. SAw 0.55 160 1
7. Bia 0.20 1377 3
8. BIb 0.26 800 2
9. BE 7.11 952 68
10. Bw 0.28 223 1
11. Neutral 86.66 112 ( 109 )e 97
TOTAL 97.71
Neutral subfractions
Hexane A 58.69 40 23
B 2.14 625 13
C 1.27 750 10
Hexane/benzefle A 4.38 238 10
B 1.89 340 6
C 1.39 320 4
Benzene/ether A 12.43 65 8
B 2.19 142 3
C 1.29 253 3
Methanol A 15.12 179 27
B 0.49 684 3
C 0.93 263 2
SUBTOTAL 102.21 112
439
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TABLE 2
DISTRIBUTION OF MUTAGENIC ACTIVITY IN FRACTIONS OF AQUEOUS SAMPLE a
Shale-oil Product Water
Relative weightc Specific activity, Weighted activity,
Fractionb % of total rev/mg rev/mg
1. NAOH 1
2. WA 1 1.5 397 5
3. WAE 6.3 105 7
4. SA 3.9 0 --
5. SAE 16.8 0 --
6. SAW 65.0 0 --
7. 0.1 52 <1
8. BIb 0.1 1468 1
9. BE 2.7 1575 42
10. B 1.3 868 12
11. Neutral 2.4 52 1
TOTAL 68
Footnotes
aAll assays carried out in the presence of crude liver S-9 from rats induced
with Aroclor 1254.
bj = insoluble (fractions a and b), E = ether soluble, W = water soluble,
WA = weak acid, SA = strong acid, and B = base.
Crev/mg = revertants/milligram (strain TA98). Values are derived from the
slope of the induction curve.
dWeighted activity of each fraction relative to the starting material is
the product of columns one and two. The sum of these products is given
as a measure of the total mutagenic potential of each material. The value
for the neutral fraction was calculated from the value for the weighted
subfract ions.
eActivity based on assay of the total neutral fraction before chromatography
rather than on the summation of the individual subfraction.
440
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TABLE 3
MUTAGENIC ACTIVITY OF SHALE OIL FRACTIONATED WITH
SEPHADEX L1{-20 SYSTEM
a
Specific Activity
(rev/mg)
Weighted
ctivity
Sumation
of
Fractions
1.
Original
750
3.
Hexane Insol.
1750
4.
Hexane sol.
400
5.
Hydrophilic
1400
6.
Lipophilic
175
7.
Polymeric
250
8.
Sieved
200
9.
H-Bonded
750
Aliphatic
0
Mono-aromatic
78
Di & Tn-aromatic
800
Poly-aromatic
2800
10.
11.
12.
13.
TOTAL
750
23
390
103
145
155
39 —
0
4
49
56
28 Oc
413
248
200
109
aResults obtained with strain TA9S and
Aroclor induced rat liver S-9 mix.
bRefer to Table 1.
metabolic activation with
CSumaation of fractions 3, 5, 7, 9, 10—13.
FIGURE LEGEND
Figure 1. Induction cf rev rtants in Salmonella strain TA9B with increasing
concentration of (A) Fraction 9, basic, ether-soluble from shale oil; and
(B) Fraction 9, basic, ether-soh bie from product water with a’ tivation with
an enzyme (S-9) prepared from rat livers induced with Aroclor 1254.
441
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TABLE 4
MUTAGENICITY RAW AND SPENT SHALE
Sample
Designation
Fraction
Tested
Mutag
his
enici ty
REV/mga
-
TA 98
TA100
CSA II (1) Diffused 220 360
(TOSCO) Fluorescence
CSA II (1) Polar D113S 500 400
(TOSCO) Material D116S 320 0
CSA II (2) Polar D97S 220 0
(TOSCO) Material
SA VII (1) Total Toxic Toxic
(PARAHO) Benzene
Soluble
AP VI (1) Total 60 400
(PARAHO) Benzene
Soluble
aslope of dose-response curve.
442
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DOSIMETRY OF COAL AND SHALE DERIVED CRUDE LIQUIDS
AS MOUSE SKiN CARCINOGENS
J.M. Holland, R.O. Rahn, L.H. Smith, S.S. Chang and T.J. Stephens
Biology Division
B.R. Clark
Analytical Chemistry Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37830
SUMMARY
In a series of three separate experiments mice have been exposed to
various concentrations of fossil liquids obtained from coal, oil shale or
natural petroleum. All materials were capable of inducing squamous cell
carcinoma, however, potency differed substantially. Skin carcinogenicity
was markedly greater for both coal and oil shale liquids than was observed
with natural petroleums. None of the syncrudes approached the skin carcino-
genicity of a pure reference carcinogen, benzo(a)pyrene (BP). It is
unlikely that concentration of material in the vehicle applied to the test
animal will allow meaningful comparison amoiig the diverse agents of interest
to the synthetic fuels industry. To better establish the relationship
between actual tissue dose and surface concentration we are investigating
various in vitro and biochemical measures of hydrocarbon-skin interaction to
determine which, if any, could serve € s a more definitive measure of surface
dose. Results, using BP as a marker carcinogenic hydrocarbon, suggest that
carcinogenic crudes inhibit BP metabolism in skin organ culture as well as
interaction of BP adducts with epiderntal DNA, in vivo .
INTRODUCTION
A determination of whether offsite release of liquid or particulates
from oil shale production or refining represent any significant health risk
to man or his environment is difficult, even for existing well established
industries, let alone a new and still experimental oil shale technology.
However, the potential long term costs of waiting until the industry
achieves self-sufficiency before commencing health effects research provides
sufficient justification for a careful and systematic examination of the
potential for specific hazards. Our present focus is upon the inpiant or
worker population who potentially are exposed to process streams, ambient
air or end products. For all potential risk scenarios, the one deemed most
important on the basis of both historical 1 as well as practical considera-
tions is an assessment of the potential health consequences of intermittant
skin exposure.
443
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Possible dermatologic effects represent a continuum, ranging from acute
irritation and inflammation to delayed effects such as hypersensitivity and
skin carcinogenesis. It is practically and biologically unrealistic and
inappropriate t.o single out any of the discrete components of this complex
to the exclusion of all others, but out of practical necessity our experi-
mental emphasis is placed upon the skin carcinogenicity of process mater—
ials, with special priority given to the final products. The rationale is
that no matter what the technology, its physical location or intended pur-
pose, a product oil will result and eventually enter commerce in one form or
another. An understanding of the relative skin carcinogenicities of these
materials in experimental animals, coupled with information concerning the
underlying basis for demonstrated differences could, if applied judiciously,
be of benefit both to the employer and employee as well as the general
public. This presentation will describe the approach we have taken to
accomplish this objective and will illustrate both the progress achieved as
well as the problems which remain.
MATERIALS AND METHODS
SKIN CARCINOGENESIS TESTS
Equal numbers of SPF male and female C3H/fBd mice were distributed five
per cage, given free access to pasteurized Purina 5010C and hyperchiori—
nated-acidif led water. Bedding was hardwood chips. Test materials were
dispersed or dissolved by brief sonication in a mixed solvent consisting, by
volume, of 30% acetone and 70% cyclohexane. Controls were shaved and
handled just as treated mice but exposed to the vehicle only. Materials
were applied either Monday, Wednesday and Friday or Monday and Thursday
depending upon the experiment. Three kinds of experiments have been done.
(1) Twenty-two-week, single concentration, three times weekly, followed by a
22-week period without continued treatment to assess the clinical signifi-
cance of lesions induced during or developing subsequent to the exposure
period. (2) Thirty-week exposure at various dose rates, twice weekly with a
planned 20-week clinical followup. At present we are 12 weeks into the
followup so these data must be treated as preliminary. (3) Twenty-four-
month, three times weekly exposure at various dose rates. Fifty microliter
of all test materials are applied to the dorsal skin using a micropipette.
Evaluation of lesions occurring at the site of application is based upon
histologically confirmed squamous carcinoma in the 22-week study. Since the
30-week experiment is still in progress the evaluation of the lesions is
dependent upon clinical criteria and therefore must be viewed as preliminary
pending eventual histologic confirmation. In the 24-month study animals
bearing carcinomas are distinguished on the basis of local extension and
infiltration of the tumors into the subcutis.
SAMPLE IDENTITY AND CHARACTERIZATION
The samples tested for sking carcinogenicity have also been compared
analytically with respect to constituents assumed to be especially relevant
to mammalian epidermal carcinogenesis, type and content of polyaromatic
hydrocarbon (PAH). Detailed description of the techniques used and addi
444
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tional sample data can be found elsewhere. 2 ’ 3 Briefly, the total wt % PAH
was determined by an acid-base solvent partition used extensively in the
analytical fractionation of tobacco smoke condensate, 4 benzo(a)pyrene (BP)
concentration was determined using quantitative TLC after removing inter-
fering components by gel permeation chromatography. 5
Experiments have been done using five crude materials, two coal
liquids, a shale oil, a composite natural petroleum and Wilmington,
California natural crude. Coal liquid A was produced by the synthoil cata-
lytic hydrogenation process and provided through the courtesy of the
Pittsburgh Energy Research Center. Coal liquid B was produced by the pyro-
lytic COED process from Western Kentucky Coal and was provided by the FMC
Corporation. Crude shale oil was produced in a simulated in situ above-
ground retorting of Green River oil shale and was provided by the Laramie
Energy Research Center. Natural petroleum has been tested both as a blend
and as a single crude. The blend consisted of 20% Wilmington, California;
20% South Swan Hills, Alberta, Canada; 20% Prudhoe Bay, Alaska; 20% Gach
Sach, Iran; 10% Louisiana-Mississippi Sweet; 10% Arabian Light. The single
source natural petroleum was Wilmington, California. For most experiments
materials were solubilized or suspended in a composite solvent, consisting
of 70% cyclohexane and 30% acetone, by brief sonication prior to skin
exposure. Reference BP (>99% pure) was obtained from Aldrich Chemical
Company.
SKIN PAH METABOLISM, IN VITRO
It is possible that noncarcinogenic constituents of complex mixtures
could influence the metabolic activation and clearance of known active
carcinogenic components and thus indirectly contribute to observed differ-
ences in potency. One way to determine whether this occurs and to what
extent materials differ is to compare the skin’s metabolism of tritiated
benzo(a)pyrene in the presence and absence of various amounts of crude.
This is accomplished by floating measured pieces of intact mouse skin on a
buffered physiologic solution, incubating the skin and observing the extent
to which tritium applied to the exterior surface is converted to metabolites
soluble in the aqueous phase in contact with the subcutis.
MEASUREMENT OF B(A)P BINDING TO TARGET TISSUE DNA
As a further measure of the presence of constituents that alter the
penetration or metabolism of known carcinogenic PAH, known amounts of triti-
ated BP are added to the undifferentiated crude mixtures. The spiked crudes
are applied to mice and at various times mice are killed, the skins excised
and the epidermis separated by brief heat treatment. The epidermis was
subsequently homogenized and the DNA isolated and purified.
In addition to the radiometric method, fluorescence characteristic of
BP adducts bound covalently to DNA was also determined. For these measure-
ments the DNA recovered from a single animal (“ 1O0 ug) was dispersed in a
volume of 0.15 ml, taken up in 3 mm 10 Quartz tubes and fluorescence deter-
mined at 77°K. The excitation and emission monochrometers were set 28 nM
445
-------
apart; by scanning both monochrometers simultaneously a spectrum is obtained
which is selective for BP and minimizes contributions from nonstructured
background emissions.
RESULTS
SKIN CARCINOGENICITY
Table 1 lists the results obtained when materials are applied three
times weekly for 22 weeks followed by a 22-week holding period to allow
lesions to progress without continued insult. These data together with
additional characterization appear in greater detail elsewhere. 6 Under
these conditions all the syncrudes are capable of evoking squamous epidermal
tumors that are both histologically and biologically malignant. Under
identical conditions of exposure no skin neoplasms were observed in mice
exposed to the composite petroleum.
The data presented in Table 2 represent the status of an experiment
still in progress. At this time the animals have completed 30 weeks of
twice weekly exposure and are 12 weeks into the clinical observation period.
The most striking difference between this and the preceding study was a
sharply reduced effect observed overall. It is presumed that this was the
result of a lower frequency of application.
Table 3 summarizes the results of 24 months, three times weekly appli-
cation of these materials at low dose rate. Once again, the same relative
differences were observed among the various materials, however the longer
exposure duration permitted carcinoma induction to be observed in animals
treated with the composite natural petroleum. The lethality of the skin
tumors is clearly reflected by the correlation between tumor incidence and
percent mortality across all groups.
In order t.o place the preceding data into perspective it is useful to
compare the carcinogenicity of the various crudes with that of BP applied
three times weekly in the same solvent to the same inbred strain. These
data are summarized in Table 4. The BP dose which came closest to approxi-
mating the response obtained with the most carcinogenic syncrude was 50
micrograms. itt is instructive to note that this is 1/500th the amount of
coal liquid A required to elicit a comparable skin tumor incidence.
PAH AND BP CONTENT OF SAMPLES
Table 5 compares the wt % PAH found in each material as well as the
approximate concentration of BP in the whole sample. It could be signifi-
cant that while total PAH content appears not to be correlated with carcin-
ogenicity, the concentration of BP agrees well with the potency of the whole
mixture. The limited number of samples precludes assumption of any precise
mathematical correlation, however, it will be of interest to compare other
crudes on the basis of BP content and observe how well the correlation holds
and under what circumstances it fails.
446
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Table 1. SKIN CARCINOCENICITY OF SYNCRUT)ES ASSESSED BY 3 TIMES WEEKLY APPLICATION FOR 22 WEEKS
Final % Average Mortality through
Carcinoma latency 44 weeks
Material Dose/application (mg) Number
149± 8
191±14
154± 9
CoalB
25
30
37
Shale
oil
25
30
47
Composite petroleum
25
30
0 0
20
3
37
0
-------
Table 2. SKIN CARCINOGENICITY OF SYNCRTJDES ASSESSED BY 2 TIMES WEEKLY
APPLICATION FOR 30 WEEKS
Material
Dose/application (mg) Number
Interim %
carcinoma
Average
latency
Mortality at
42 weeks
Coal A
25
20
75
206
10
12
20
35
222
0
6
20
1
247
5
3
20
0
—
0
CoalB
25
20
0
—
0
12
20
0
—
5
6
20
0
—
0
3
20
0
0
5
Shale oil
25
20
35
208
5
12
20
5
213
0
6
20
0
—
0
3
20
0
—
0
WilmIngton,
California
25
12
20
20
0
0
—
—
0
0
6
20
0
—
0
3
20
0
—
0
448
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Table 3. SKIN CARCINOGENICITY OF SYNCRIJDES ASSESSED BY 3 TIMES WEEKLY
APPLICATION FOR 24 MONTHS
Material
Dose/application (jug) Number
l
Final /
carcinoma
2
Average
latency
/
at
.
Mortality
24 months
Coal A
1.0
50
92
498(10)
78
0.3
50
26
569(18)
38
0.2
50
8
653(19)
36
0.04
50
4
680
26
Coal B
0.8
50
8
565(54)
44
0.3
50
4
668(8)
36
0.17
50
2
588(94)
46
0.03
50
2
679
44
Shale oil
2.5
50
90
483(15)
64
0.5
50
2
315
40
0.3
50
2
611
38
0.1
50
0
—
20
Composite
Petroleum
2.0
0.4
50
50
8
0
658(22)
—
20
26
.
0.3
50
0
—
22
0.08
50
O
—
30
Vehicle
—
50
0
—
28
1 Uncorrected for intercurrent mortality.
2 Days (±S.E.).
449
-------
Table 4. SKIN CARCINOGENICITY OF BENZO(AIPYRENE APPLIED THREE TIMES
WEEKLY FOR 24 MONTHS
Dose/application (nig)
Number
Final %1
carcinoma
Average 2
latency
%
at
Mortality 3
24 months
0.050
50
100
139(4)
—
0.010
50
100
206(7)
—
.
0.002
50
90
533(5)
58
‘Uncorrected for intercurrent mortality.
2 bays (±S.E.)
3 Not calculated in higher dose groups, which were terminated when the tumor
response saturated.
Table 5. WEIGHT PERCENT PAR AND CONCENTRATION OF BENZO [ A]PYRENE IN SYNCRUDES
AND NATURAL PETROLEUMS
Material Wt. Z PAM ug BP/gm
Coal A
5.1
79
Coal B
6.0
12
Shale oil
2.0
20
Composite
petroleum
2.6
‘ ‘1
Wilmington
petroleum
n.d.
1
450
-------
BP METABOLISM IN SKIN ORGAN CULTURE
Figure 1 shows the effect of various amounts of crude petroleum on the
extent of BP metabolism by skin maintained in organ culture. As can be
observed, the more carcinogenic the crude the more BP metabolism is inhibit-
ed. It is known, at present, whether this inhibition is a result of direct
cytotoxicity or metabolic competition.
BINDING OF BP ADDUCTS TO MOUSE EPIDERMAL DNA IN VIVO
There are data suggesting that the degree of covalent interaction
between specific hydrocarbon metabolites with one or more DNA nucleosides
within target tissue DNA is highly correlated with the mutagenicity and
carcinogenicity of pure PAils. 7 ’ 8 ’ 9 Assuming that this correlation will hold
for a broad range of structurally related components of complex mixtures we
are developing assay techniques that will allow a precise determination of
the time integrated dose of material that interacts with epidermal DNA
following topical application in vivo .
Figure 2 contrasts the kinetics of BP adduct formation and removal or
dilution over a 28-day period. Each data point consists of the pooled
epidermal DNA obtained from three mice treated topically at time zero with
250 uC BP in a volume of 0.1 ml acetone. Cold BP, 80 ug, was added as
carrier. The results show that binding occurs almost immediately and
reaches a maximum at approximately 24 hours followed by a gradual decrease.
It is unknown to what extent the decrease is due to specific repair process-
es or simple dilution of the label by ceildivision.
Figure 3 demonstrates the efficacy of the fluorescence method for
detection of BP adducts bound to DNA. The open circles represent the extent
of binding obtained as a function of BP dose using tritiated BP; the solid
circles reflect the relative specific fluorescence intensity in the same DNA
samples. While the data points are few there is good agreement between the
two measures of BP-DNA interaction. The data further show that the correla-
tion between the amount of BP applied to the skin and that associated with
DNA is essentially linear through 400 ug.
The influence of the various syr crudes and natural petroleum on BP-DNA
binding in viva is given in Table 6. These data agree with evidence
obtained in skin organ cultures (Figure 1) that the more carcinogenic the
material the greater the inhibition of BP binding to epidermal DNA in vivo .
DISCUSSION
The data presented reveal wide differences in the skin carcinogenicity
of synthetically derived hydrocarbon mixtures. The observed association
between concentration of BP in the parent crude and skin carcinogenicity may
be more apparent than real; however, the observation should provide an
incentive to compare a wide range of materials on this basis.
4 1
-------
TABLE 6. THE INFLUENCE OF SYP4CRUDES ON THE RELATIVE BINDING
OF TRITIATED BP TO EPIDERMAL DNA IN VIVO
Material
Moles of
BP per iU
Nucleotides
Coal A
2
20
200 ug’
9.1
7.9
6.1
Coal B
6.6
5.2
4.3
Shale Oil
7.0
5.2
4.1
Wilmington
Petroleum
5.1
4.8
4.7
‘Amount of crude applied in 0.1 ml of hexane in the presence of 250 uCI
(2.5 ug) tritiated benzo(a)pyrene.
Obtaining an integrated measure of “PAH” dose, at the molecular level,
may be feasible based upon radiometric or fluorimetric observation of target
tissue nucleotides which serve as traps for reactive metabolites generated
from complex mixtures in vivo . By routinely conducting these measurements
for any materials subjected to empirical whole animal carcinogenesis dose
response tests it may be possible to develop sufficient data base to permit
this measure to serve asa rapid, inexpensive and quantitative screen for
potential skin carcinogenicity.
Given the chemical complexity of syncrudes it is unrealistic to expect
that any single measure will suffice to predict carcinogenicity in vivo .
There simply are too many parameters involved including initial penetration
of the skin lipid barrier, metabolic conversion of various constituents to
reactive intermediates, molecular repair and cellular recovery mechanisms as
well as systemic hormonal and immunologic factors. In our opinion the only
way that experimental animal data can be used in an interpretive, quantita-
tive and predictive way is to better understand the relative importance of
each of these factors. With this information, combined with data concerning
detailed structural and functional differences between mouse and human skin,
we may be in a position to derive quantitative interspecies risk estimates.
ACKNOWLEDGEMENTS
Research sponsored by the Office of Health and Environmental Research,
U.S. Department of Energy, under contract W-7405-eng-26 with the Union
Carbide Corporation.
REFERENCES
1. Scott, A. The Occupation Dermatoses of the Paraffin Workers of the
Scottish Shale Oil Industry, With a Description of the System Adopted
and the Results Obtained at the Periodic Examination of These Workers.
Eigth Scientific Report of the Imperial Cancer Fund. London, Taylor
and Frances, 1923. pp. 85-142.
452
-------
2. Guerin, M.R., J.L. Epler, W.H.
Polycyclic Aromatic Hydrocarbons
In: Carcinogenesis: Polynuclear
P.W. and R.I. Freudenthal (eds.).
Griest, B.R. Clark, and T.K. Rao.
from Fossil Fuel Conversion Processes.
Aromatic Hydrocarbons, Vol. 3, Jones,
New York, Raven Press, 1978.
3. Griest, W.H., M.R. Guerin, B.R. Clark, C. Ho, LB. Rubin, and A.R.
Jones. Relative Chemical Composition of Selected Synthetic Crudes.
In: Proc6edings of the Symposium on Assessing the Industrial Hygiene
Monitoring Needs for the Coal Conversion and Oil Shale Industries.
Upton, New York, Brookhaven National Laboratory, November 6-7, 1978.
4. Swain, A.P.,
of Cigarette
Cancer Res.
J.E. Cooper, and R.L. Stedman. Large Scale Fractionation
Smoke Condensate for Chemical and Biologic Investigations.
29: 579-583, 1969.
5. Swanson, 0. , C. Morris, R. Hedgecoke, R. Jungers, R. Thompson, and J.
Bumgarner. A Rapid Analytical Procedure for the Analysis of Benzo(a)-
pyrene in Environmental Samples. Trends in Fluorescence. 1: 22-27,
1978.
6. Holland, J.M., M.S. Whitaker, and J.W. Wesley.
cence Intensity and Carcinogenic: Potency of
Petroleums in Mouse Skin. Am Indust Hygiene
1979.
Correlation of Fluores-
Synthetic and Natural
Assoc. 40: 496-503,
7. Malaveille, C., H. Bartsch, P.L. Grover, and P. Sims. Mutagenicity of
Non-K-Region Diols and Diol-Epoxides of Benz(a)anthracene and Benzo(a)-
pyrene in S. Typhimurium TA100. Biochem Biophysical Research Communi-
cations. 66: 693-700, 1975.
8. Wood, A.W., R.L. Chang, W. Levin, R.E. Lehr, M. Schaefer-Ridder, J.M.
Kane, D.M. Jenina, and A.H. Conney. Mutagenicity and Cytotoxicity of
Benz(a)anthracene Diol Epoxides nd Tetrahydro-Epoxides: Exceptional
Activity of the Bay Region 1, 2-Epoxides. Proc Nat Acad Sci. 74:
2746-2750, 1977.
9. Levin, W., A.W. Wood, H. Yagi, P.M. Dansette, D.M.
Conney. Carcinogenicity of Benzo(a)pyrene, 4, 5-,
10-Oxides on Mouse Skin. Proc Nat Acad Sci. 73:
Jenina, and A.H.
7, 8,-, and 9,
243-247, 1976.
453
-------
o: 50
3
ru
S_
§~s
R-a 30-^
40-
<
o
<
CE
20-
10-
0.1 1.0
SUBSTANCE APPLIED (
10.0
Fig. 1.
Relative amount of tritiated
BP in medium at 24 hours in
the prescence of various
material. Coal liquid A (0),
coal liquid B (0), Wilmington
Petroleum (A), shale oil (zi).
Bars represent the standard
error.
4 6
TIME (days)
Fig. 2. Kinetics of tritiated BP
binding to mouse skin
epidermal DNA j_n vivo. Rb
(relative bindings) equals
the moles of BP bound per
mole of DNA nucleotide.
6x10
4x10~-
cc
o
o 2 x 10-5-
100 200 300
BENZO (a) PYRENE APPLIED (fj.q)
400
Fig. 3. Dose response relationship obtained
for BP binding to mouse epidermal
DNA jji vivo as determined by DNA
associated tritium counts (Rb) or
relative fluorescence intensity (Fl).
454
-------
THE CARCINOGENICITY OF VARIOUS SHALE OILS AND SHALE OIL PRODUCTS
William Barkley, Klaus, L. Stemmer, Jane Agee,
Raymond R. Suskind and Eula Bingham*
Department of Environmental Health
College of Medicine
University of Cincinnati
Cincinnati, Ohio 45267
ABSTRACT
Initial studies of workers involved in the production and use of
Scottish shale oil revealed an increased incidence of cancer. Subsequently,
other studies reported that prolonged exposures to shale oil can indeed
produce skin cancer in humans. This fact and the likelihood that the nation
will utilize one of its most abundant energy resources, oil shale, dictate
that an assessment of the potential health effects of American shale oil and
shale oil products should be investigated.
We have investigated the potential carcinogenic potency of several raw
shale oil samples produced from various retort methods. In addition to the
raw shale oils, we have also studied the carcinogenic potency of several raw
shale and processed or spent shale samples. This presentation reports the
results of prolonged and repeated applications of these materials to the
skin of mice.
INTRODUCTION
Studies of Scottish workers involved in the production and use of shale
oil revealed an increased incidence of cancer. It is well known from the
reports of Bell’ (1876) and Scott 2 (1923) that prolonged exposure to shale
oil can produce skin cancer in humans. These reports, as well as reports of
others, dictate that an assessment of the potential health effects of shale
oil and shale oil products should continually be made as new technologies
are developed.
As great emphasis is being placed on developing domestic energy
sources, it is likely that not only new technologies will be utilized in
this endeavor, but also new sources of energy will be used. One such source
is oil shale. The elevated temperatures necessary for various retort
methods to extract oil from oil shale are likely to produce many organic
compounds. Some of these compounds may be carcinogens, co-carcinogens, or
promotors and could be present in both the crude shale and the processed or
spent shale. To insure protection of the worker who may come in contact
455
-------
with these proaucts, it would be prudent to evaluate the carcinogenic poten—
tial of these materials.
Like other investigators, we have reported the benzo(a)pyrene (BaP)
content of test materials. Thi practice was predicated on the early belief
that since BaP was present in coal tar, it might also be present in other
carcinogenic materials such as crude shale oil. Although many investigators
studied the carcinogenic constituents of shale oil during the 1930s, it
wasn’t until 1943 that Berenblum and Schoental 3 identified BaP in Scottish
shale oil. In addition, they also observed a fraction of shale oil to be
carcinogenic that did not contain detectable quantities of BaP. Later
Hueper and Cahnman 4 (1958) and Bogovsky 5 (1962) reported BaP free American
and Estonian shale oil, respectively, to be carcinogenic in mouse skin
painting studies. It would behoove toxicologists to perform biological
testing to go along with chemical analyses in evaluating the potential
hazards of these complex mixtures.
MATERIAL AND METHOD
The following materials were evaluated for their carcinogenic potential
in mouse skin painting studies: four crude shale oils which represent two
Paraho processes (direct mode and indirect mode), the Union B process and a
Colony semiprocess; three raw shales and four spent shales, all provided as
finely ground materials.
Young adult C3H/HeJ male mice were treated twice weekly with 50 mg of
the test material. The material was applied to the interscapular area of
the shaven backs with a microliter pipette or a calibrated dropper. The raw
and spent shales were suspended in rioncarcinogenic white mineral oil
(U.S. P.) in 1:2 ratio (by weight). Mice were treated for 80 weeks or until
the appearance of a papilloma. If a papilloma progressed and was diagnosed
grossly as a carcinoma the mouse was killed and autopsied. However, if the
papilloma regressed the treatments were resumed. In addition to the test
groups, the study included two negative and two positive control groups.
One negative control group received no treatment, while the other received
50 mg of mineral oil twice weekly. The two positive control groups received
50 mg twice weekly of solutions of BaP in mineral oil at concentrations of
0.15 and 0.05%.
At autopsy all the mice were examined grossly for skin tumors. If
tumors were observed, a description, size measurements, and location were
recorded on the autopsy record. Skin from the total treatment area of each
mouse, as well as other tissues, were submitted for histological examina-
tion. This presentation will report only the results of microscopic exami-
nation of the skin.
RESULTS AND C0t ’VIENTS
A summary of the results of repetitive application of shale’oils, raw,
and spent shales upon the skin of mice can be seen in the tables that
follow. Given in Table 1 are the tumor incidence and the average time at
456
-------
which tumors appeared in the experimental groups receiving the four shale
oils and two concentrations of BaP. It can be seen that about 80% of the
animals treated with the shale oils developed neoplasms while almost 100% of
the mice treated with BaP developed tumors. The average latent periods of
the mice treated with the oils are similar to the mice receiving the high
positive control (0.15% BaP). The biological activities induced by these
oils could not be attributed to their BaP content since they range from
0.00018-0.00042%. Although the tumors recorded in Table 1 were gross obser-
vations, they agree very well with the microscopic examinations. The number
of mice, observed grossly, having tumors was 225. Two hundred and sixteen
of these were confirmed histologically. Six of the remaining nine were not
examined microscopically because of postmortem changes.
In Table 2 are the tumor incidence and latent period of various complex
mixtures after topically applying them to the backs of mice. Of note is the
biological activity exerted by the three shale oils, while no activity was
seen among the petroleum crude oils.
Table 3 gives the carcinogenic potency of a sample of raw and upgraded
shale oil. It can be seen that upgrading the oil reduces its biological
activity. The raw shale oil induced tumors in 86% of the surviving mice
while the upgraded oil induced 13%.
Table 4 lists the various neoplasms resulting from topical applications
of shale oils and BaP to the skin of mice. The first three types of tumors,
fibrous papilloma, squamous papilloma, the keratoacanthoma are benign,
whereas the latter two, squamous cell carcinoma and fibrosarcoma are malig-
nant. All the malignant neoplasms induced by the BaP were epithelial in
origin, namely, squamous cell carcinomas. The BaP-induced malignancies
differ from the shale oil-induced tumors in that all four shale oils also
initiated the development of sarcomas. The number of mice developing sar-
comas was relatively small, although significant.
The fact that the shale oils stimulated manifestation of fibrosarcomas,
suggest that some components of shale oil affect the fibrous tissue of the
dermis. This is further substantiated by the occurrence of fibrous tissue
papillomas, which is relatively rare in mice treated with BaP. Another fact
that may have contributed to the induction of sarcomas was that all of the
oils were ulc rgenic and depilatory.
Table 5 lists the still-in-progress groups of negative controls, raw,
and spent shales. After 80 weeks of topical applications of these materials
to the backs of mice and 6 weeks of post exposure, no skin tumors have been
observed in any of the animals. The lack of biological activity in the oil
shale groups can be attributed to (1) the relatively low concentration of
polycyclic hydrocarbons (<0.00001% BaP), (2) that these compounds are very
tightly bound to the particulate material and, therefore, no skin absorp-
tion, (3) that very little of the raw and spent shale remained in the skin
for any length of time.
457
-------
Although the preceding data apply to C3H mice, it should arouse aware-
ness of the possible hazard that may be associated to human exposure to
these oils. New technologies should be monitored or evaluated for their
potential health effects on workers who may be exposed.
REFERENCES
1. Bell, B. Paraffin Epithelioma of the Scrotum. Edin. Med. J.
22:135-137 (1876).
2. Scott, A. On the Occupational Cancer of the Paraffin and Oil Workers
of the Scottish Shale Industry. Br. Med. J. 2:1108-1109 (1922).
3. Berenbium, I. and Schoental, R. Carcinogenic Constituents of Coal Tar.
Brit. J. Exper. Path. 24:232-239 (1943).
4. Hueper, W.C. and Cahnmann, H.J. Carcinogenic Bioassay of Benzo(a)-
pyrene-Free Fractions of American Shale Oils. Arch. Path. 65:608-614
(1958).
5. Bogovsky, P. On the Carcinogenic Effects of Some 3,4-Benzopyrene-Free
and 3,4-Benzopyrene-Containing Fractions of Estonia Shale Oil. Aceta
Univ. Inter. Contra. Cancrum. 18:37-39 (1962).
This work was supported by a contract from the American Petroleum
Institute.
*Dr. Eula Bingham is presently on leave from the Department of Environ-
mental Health, University of Cincinnati. Her current address is
Department of Labor for Occupational Safety and Health Administration,
Washington, DC 20210.
458
-------
TABLE 1
TUMOR INCIDENCE AND LATENT PERIOD
AFTER TOPICAL APPLICATION OF SHALE OILS TO MICE
Sample
Number
Number
of
Mice
Final Effective*
Number
Number**
with
Tumor
Average Latent
Period (Weeks)
RO—1
50
42
34
31.9
RO—2
50
43
35
21.6
RO—3
50
46
40
28.5
RO—4
50
48
39
25.7
BaP
0.05%
50
48
47
37.8
BaP
0.15%
30
30
30
27.4
*Gross Observation
**Fjnal Effective Number is the number of mice alive at the time of appear ince of
the median tumor plus those mice that may have died with tumors.
-------
TABLE 2
TUMOR INCIDENCE AND LATENT PERIOD AFTER TOPICAL APPLICATION OF VARIOUS COMPLEX MIXTURES TO MICE
Sample
Number
of Mice
Number
of
Mice Developing Tumors
Average
Period
Latent
(Weeks)
Papillomas
Carcinomas
Shale Oil 1 (Heat 20 1 17 43±4
Transfer)
Shale Oil 2 (Heat 30 1 18 36±2
Transfer)
Shale Oil 3 (Direct 30 3 22 43±5
Combustion)
Crude Oil 1 (Texas) 20 0 0
Crude Oil 2 20 0 0
(Asphaltic)
Paraffinic Distil— 30 4 2 64±6
late (Uncracked
Crude)
Industrial Fuel Oil 20 1 18 17±2
Residuum (Catalyti— 30 0 30 8±1
cally Cracked)
0.005% Benzo(a)— 50 6 i 80±8
pyrene in Toluene
0.2% Benzo(a)— 30 3 27 31±4
pyrene in Toluene
-------
TABLE 3
CARCINOGENIC POTENCY OF RAW AND UPGRADED SHALE OIL
Sample
Number
of
Mice
Final*
Effect
Number
Number of Mice
Developing Tumors
Average Latent
Period
(Weeks)
Malignant
Benign
Raw Shale Oil
50
45
21
18
30
Upgraded Shale
50
39
3
2
49
Oil
Positive Control
100
92
75
9
46
0.05% BaP in
Toluene
Negative Control
100
91
0
0
-
Toluene Only
*Final Effective Number is the number of mice alive at the time of appearance of the median
tumor plus those mice that may have died with tumors.
C•)
The nunther for the Solvent Control is the number of mice alive after one year.
-------
TABLE 4
TUMORS RESULTING FROM TOPICAL APPLICATION OF SHALE OILS TO C3H/HeJ MICE
Sample
Fibrous
Papilloma
No. of No. of
Squamous
Papilloma
No. of No. of
Kerato—
Acanthoma
No. of No. of
Squamous
Carcinoma
No. of No. of
Fibro
Sarcoma
No. of No. of
Number
Animals
Tumors
Animals
Tumors
Animals
Tumors
Animals
Tumors
Animals
Tumors
RO—l
6
7
9
12
10
13
8
10
6
7
RO—2
5
8
8
14
13
19
15
24
8
9
R0—3
3
3
9
12
16
21
17
23
6
6
RO—4
6
8
12
18
17
24
15
20
5
5
BaP
2
2
10
17
22
36
30
35
0
0
0. 05%
0.15%
0
0
8
16
15
20
25
36
0
0
-------
Raw
Shale 1
Raw
Shale 2
Raw
Shale 3
(A.)
Spent
Shale 1
Spent
Shale 2
Spent
Shale 3
Spent
Shale 4
Control
No Treatment
Control
Mineral Oil
TABLE 5
TUMOR INCIDENCE AND LATENT PERIOD
AFTER TOPICAL APPLICATION OF RAW AND SPENT SHALES
Sample
Number
Number
of
Mice
Final Effective
Number
Nu lnber*
with
Tumor
Average Latent
Period
(Weeks)
50
50
50
50
50
50
50
50
50
In
In
In
In
In
In
In
In
In
Progress**
Progress**
Progress**
Progress**
Progress**
Progress**
Progress**
Progress**
Progress**
0
0
0
0
0
0
0
0
0
*Gross Observation
**After 86 Weeks Duration
-------
CHROMOSOME ABERRATIONS AND LOSS OF SOME CELL FUNCTIONS
FOLLOWING IN VITRO EXPOSURE TO RETORTED OIL SHALE
Agnes N. Stroud
Mammalian Biology Group
University of California
Los Alamos Scientific Laboratory
Los Alamos, New Mexico 87545
ABSTRACT
An investigation of cellular level effects of processed oil shale from
a simulation of modified in situ retorting was undertaken as part of an
assessment of the toxicity and mutagenicity of oil shale. Complete assess-
ment of the health hazards associated with physical contact, inhalation or
ingestion of oil shale has not been examined in humans and until it becomes
practical to assess these hazards in man, we must rely upon well-established
in vitro detection procedures in addition to whole animal testing. CHO
cells and L-2 rat lung epithelial cell lines were exposed in vitro to pro-
cessed oil shale particles at different intervals following exposure. Cells
were analyzed for chromosome alterations, cell colony forming ability, DNA
synthesis and cell transformation. The results of these studies demonstrate
that retorted oil shale, under these experimental conditions, does modify
cells in vitro . Chromosome aberrations increased with dose, cell colony
forming ability decreased exponentially with dose, and the rate of DNA
synthesis was affected, however cell transformation was not demonstrated
after 3 months. Further studies are in progress. (This work was performed
under the auspices of the U. S. Department of Energy.)
INTRODUCTION
There is concern over potential health hazards from pollutants formed
as byproducts in commercial production of energy. As the technology for
processing new sources of energy becomes available, a variety of exogeneous
agents will be introduced into the industrial environment which may be
implicated in the etiology of occupational diseases, primarily lung ail-
ments.
One potential source of energy currently under development is oil
shale, which if commercially produced may raise industrial health questions.
Some of the materials of concern are the polycyclic aromatic hydrocarbons
and other organic compounds, organo-metallic compounds, trace metals, raw
and processed shale, liquids and vapors, and other crude products.
464
-------
Complete assessment of the health hazards associated with inhalation,
ingestion or physical contact with spent oil shale has not been examined in
humans and until it becomes practical to perform these studies in man, we
must rely on well-established detection procedures devised and refined by
many researchers in a variety of in vitro and in vivo methods. Toxicologi-
cal studies of oil shale can be assessed by in vitro methods in a way not
feasible in an in vivo system. Therefore, we have undertaken a study to
determine the effects of spent oil shale on cells growing in vitro . Three
biological parameters which are important for survival, reproductive integ-
rity (colony forming ability), chromosome stability, and DNA synthesis, were
examined. These experiments will form a background against which the action
of spent oil shale in vivo could perhaps be viewed with respect to lung
tissue.
The results of these studies demonstrate that spent oil shale, in the
form used and under the conditions of these experiments, does modify cells
in an in vivo system.
METHODS
CELL LINES
Two established cell lines which grow as monolayers in culture were
used to evaluate the in vitro effects of the action of spent oil shale. The
cell lines employed for different aspects of this study were CHO (Chinese
hamster ovary fibroblasts) and L-2 (Fischer rat lung epithelial cells). The
CHO line was obtained from Puck 2 in 1962 and has been maintained and charac-
terized at Los Alamos Scientific Laboratory by Deaven. 3 It has a near-
diploid stemline of 21 chromosomes and for these experiments the cells were
grown in Hams F-1O medium (Microbiological Association, MBA), containing 50
units/mi Penicillin G potassium and 40 pg/ml Streptoniycin sulfate. The L-2
cell line was obtained from Kaighn 4 at the 16th passage and at the time of
these experiments had a modal chromosome number of 68. When the cell was
cloned in culture it was characterized as a type II pneunonocyte. 5 This
cell line was maintained on a medium modified by Kaighn 5 and designated
F-12K medium (GIBCO), 15 percent fetal bovine serum (MBA), 50 units/ml
Penicillin, and 50 pg/nil streptornycin.
OIL SHALE
The retorted oil shale was obtained from Laramie Energy Technology
Center (LETC) after simulated in situ processing in a 150 ton retort. The
shale was ball-milled to a dust ranging in size from l.5-20p, with the
majority of the particles between 6-lOp. For use in culture, the shale was
concentrated in a slurry in about 0.3-0.5 ml dimethylsulfoxide (DMSO) and
diluted with 0.8 percent NaC1 for sterilization by autoclave. Further
dilutions were made in the appropriate media for adding to cultures. The
oil spent shale in media was added to cultures immediately following the
plating of cells. The pH of the media was not changed more than 0.4 on the
pH scale.
465
-------
CHROMOSOME ANALYSIS AND PULSE LABELING WITH TRITIATED THYMIDINE
Cell suspensions, in their appropriate media, were inoculated into
plastic T-25 flasks (Costar) at a concentration of 1 x 106 cells for chromo-
some analysis and pulse labeling studies. Oil shale (0.05 - 0.15 mg/mi) was
added to three flasks for each dose following the inoculation of cells. The
flasks were then incubated under 5 percent CO 2 and air at 38°C in a humid
atmosphere. Chromosome and DNA synthesis analyses were performed at 16, 22,
46, and 70 hours following treatment, and at these designated time periods,
0.1 pg/mi Colcemid (GIBCO) was added to each flask during the final 3 hours
of incubation to block cells in division at metaphase. Twenty minutes prior
to fixing cells, tritiated thymidine { 3 H] TdR, in a concentration of
0.75 pCi/ml, was added to each flask to radioactively label DNA in the
cells. Both chromosome analyses and labeling assessment were performed on
cells which had been pooled from three flasks; however, separate slides were
prepared for each. Chromosome spreads were made after the cells were treat-
ed with Colcemid, placed in warm hypotonic KCL (0.075) for 15 minutes at
38°C, and fixed in three changes of fresh cold fix, consisting of one part
glacial acetic acid and three parts absolute methanol, at 4°C for about an
hour. When the last fix was decanted, the cells were dropped from a micro-
pipette onto chemically cleaned microslides which were removed from chilled
distilled water (4°C). The metaphase chromosomes spread on the wet slides
after dropping, and to enhance the spreading, the slides were air dried by
waving the slides in front of a hair dryer at 58°C for 1/2 minute. They
were stained with 4 percent Gurr Giemsa Stain (Improved R66) for 5 minutes.
Between 100-200 V metaphase spreads (50 per slide) of good quality were
analyzed for chromosome aberrations for each dose level.
Autoradiography was performed on cells which had been pulsed labeled
with { 3 H) TdR to determine the labeling index. For this analysis cells from
fixative were dropped onto microslides and air dried. The slides were
dipped in Kodak Liquid emulsion (NTB) which had previously been diluted with
equal parts of distilled water, and then stored in black slide boxes with a
drying agent at 4°C for 7-10 days before developing with 019 developer and
staining with 1 percent Gurr Giemsa. For each dose, and subsequent time
interval, 500 cells were scored for incorporation of [ 3 H] TdR into DNA.
CELL SURVIVAL
Cell survival was studied by exposing single cells in culture to vari-
ous doses of oil shale and scoring for visible cell colonies after a suit-
able period of incubation with the spent oil shale and subsequent removal of
the agent. Spent oil shale (0.05-0.3 mg/mi) was added to plastic petri
dishes (60 mm, Lux) which had previously been seeded with about 200 single
cells. After an incubation period of 6 days with spent oil shale suspension
the spent oil shale was removed and the petri dishes were washed three times
with Hank’s balanced solution (GIBCO). Fresh media was then added and the
dishes were allowed to incubate 10 days at which time the cell colonies were
fixed and stained with 1 percent Gentian Violet. Colonies containing 50 or
more cells were scored and the mean number of 3-5 replicate dishes was
determined. A survival curve was determined from the mean number of
466
-------
colonies formed from the single cells following treatment. Two or three
experiments of the same type were performed at different times and the
results were very similar, therefore, only one survival curve will be shown
for the CHO or L-2 cell lines.
RESULTS
CELL SURVIVAL
The response of cells to spent shale suspensions was studied by expos-
ing single cells in culture to various concentrations of shale for 6 days,
and scoring for visible cell colonies 1 week following the removal of spent
shale. Colony surviving fraction is plotted as a function of dose. The
survival curves for the cell lines CHO and L-2 are shown in Figure. 1. The
colony surviving fraction for both cell lines shows an exponential response
resembling that of a single-hit kinetics 6 with an extrapolation number very
close to one. The mean lethal dose or the percentage necessary to reduce
survival to 50 percent (LD 50 ) was 0.33 mg/ml for CHO cells and 0.14 mg/mi
for L-2 cells indicating that the L-2 (rat lung epithelial cells) were more
sensitive to the spent shale than CHO (Chinese hamster ovary fibroblast
cells).
DNA SYNTHESIS
CHO cells were pulsed labeled for 20 minutes with [ 3 H] TdR before the
end of exposure to spent shale. The percent of cells incorporating [ 3 H] TdR
is plotted as a function of the duration of oil shale in Figure. 2. The
labeling index of the control cells was between 52 percent and 62 percent
over a 44 hour period. The treated cells (all doses) showed a decrease in
incorporation of [ 3 H] TdR at 17 hours and reached a plateau at 21 days;
thereafter, no further decrease was seen. There was a significant differ-
ence between the control and treated cells; and between the lowest dose (0.5
mg/ml) and the two higher doses (1.0-1.5 mg/ml), but no difference between
the two. It was interesting that the reduction of DNA synthesis to 40
percent at 24 hours for the treated cells did not go below this fraction at
48 hours, indicating that there may be two cell types, one sensitive and the
other insensitive to oil shale. The suppression of DNA synthesis was sig-
nificant but the degree of suppression was not as great as one encountered
with radiation and radiometic drugs. It was noted that the rate of DNA
synthesis was somewhat reduced among the treated cells, as measured by grain
counts, and the reduction was related to dose.
CHROMOSOME ABBERRATIONS
The scoring of chromosome aberrations were analyzed on CHO cells in
metaphase after the cells were exposed to oil shale for 16, 22, 46, and 70
hours. The frequency of aberrations is plotted against the duration of
shale treatment in Figure. 3. The peak of aberration frequency for the two
higher doses occurred at 16 hours and was 13 percent at 0.15 mg/mi and 9.5
percent at 0.10 mg/ml. There was a 2-2.5 fold decrease in frequency at 22
hours and thereafter, very little or no significant change in the slope of
467
-------
the curves. At the lowest dose (0.05 mg/mi) the peak (6 percent) was not
reached until 22 hours after treatment and the curve remained the same at 46
hours, and by 70 hours the frequency had returned to control values. The
accumulated data for chromosome aberrations were combined over the 70-hour
exposure period and were plotted as a function of dose in Figure. 4, and the
accumulated data for the frequency of cells with aberrations over the same
period were also plotted as a function of dose in Figure. 5. In both cases,
the dose response curves were linear indicating that chromosome aberration
frequency and cells with aberrations were dose dependent. Chromosome aber-
rations produced in CHO cells vary both in type and frequency. Some of the
types observed are shown in Plate 1; (a) centric fusion, (b) chromatid
deletion, (c) dicentric, and (d) badly damaged chromosomes. Not shown were
isochromstid deletions, exchanges and translocations. There were an abnor-
mal number of centric fusion types (a) where the centromeres appeared to be
affected and two chromosomes fused at this junction. There was an exponen-
tial increase from 1 percent at control levels to 9.5 percent at the highest
dose (0.15 mg/ml) of spent shale. Polyploid cells for all doses increased
2-2.5 fold over controls, but the increase was not exponential.
In one experiment with human cells, lymphocytes (leucocytes) from blood
were grown in culture and exposed to spent shale (0.5-2.0 mg/mi) for 46 or
67 hours before chromosome preparations were made. Metaphase cells were
scored for chromosome damage and the frequency of chromosome aberrations is
plotted as a function of dose in Figure. 6. There is a linear dose response
to spent shale between 1.0 and 2.0 mg/ml. There were very few chromosome
aberrations 46 hours following treatment.
Sister chromatid exchanges (SCE) were investigated in L-2 cells after
spent shale treatment. The cells were exposed to shale at the beginning of
the culture period and about 48 hours later, chromosome preparations were
made and SCE were scored. Table 1 represents the frequency of SCE follow-
ing 0.10 mg/mi oil shale. The data show that spent shale was effective in
increasing the production of SCE over controls and the SCE/chromosome was
significantly higher in the treated compared to the controls.
TABLE 1. SISTER CHROMATID EXCHANGES (SCE) IN L-2 CELLS
FOLLOWING EXPOSURE TO SPENT OIL SHALE
Dose
mg/mi
Chromosome
Number
(Mean)
Number
Chromosomes
Scored
Total
Number
SCE
SCE Per
Chromosome
SCE Per
Metaphase
(Mean)±
0
69
1718
209
0.12
8.4 ± 2.
00
0.1
70
1813
311
0.17
12.0 ± 0.
12
* 25 Metaphase spreads were analyzed at 0 dose and 26 at 0.10 mg/mi.
± Range of SCE/metaphase was from 5-13 for controls and 7-17 for the treatei.
468
-------
DISCUSSION
It can be concluded that under the conditions of these experiments, the
spent oil shale composite affected the reproductive integrity of the cells
and the ability of cells to form colonies. [ -2 cells were more sensitive
than CHO cells. Chromosome aberrations were produced and DNA synthesis was
to a certain extent impaired in CHO cells. Mutagenicitv in nude mice was
not shown after treating CHO and L-2 cells with different doses of oil shale
and for different lengths of time.
It is not known what material in the retorted oil shale is responsible
for producing loss of some cellular function in vitro , but it is possible
that leaching out of a metal or metals from the composite, could be respon-
sible.
The data in this report are preliminary and the effects of spent shale
on cells in culture does not imply that spent oil shale may act similarily
in vivo over an extended period of time even though chromosome aberrations
were produced in lymphocytes of peripheral human blood in vitro . It should
be noted that spent shales may have processed specific characteristics in
creating a biological effect and that the effects associated with any one
type of spent shale cannot necessarily be considered as typical.
REF ER EN CES
1. Stroud, A. N. and Ortiz, Y.E. , “Cell Damage Following In Vitro Exposure
to Retorted Oil Shale.” 1977. In: “Biomedical and Environmental
Research Program of the LASL Health Division, Jan. -Dec. ,“ D. F.
Petersen and E. M. Sullivan, Eds. Los Alamos Scientific Laboratory
report LA-7254-PR, pp. 11-13.
2. Puck T. 1. , Cieciura, S. J. , Robinson, A. , 1958. “Genetics of Somatic
Mammalian Cells,” J. Exp. Med. 108, 945-956.
3. Deaven, L. L. , and Petersen, D. F. , “The Chromosomes of CHO; and
Aneuploid Chinese Hamster Cell Line: G-Band, C-Band, and Autoradio-
graphic Analysis,” Chromosome, 41, 129-144 (1973).
4. Douglas, W. H. and Kaighn, ft E. , “Clonal Isolation of Differentiated
Rat Lung Cells,” In Vitro 10, 230-242 (1974).
5. Kaign, M. E. and Douglas, W. H. J. , “Isolation of Clonal Lines from
Normal Rat Lung with Lung Specific Properties,” Journal of Cell
Biology, 59, 60a (1973).
6. Lea, D. E. Actions of Radiations on Living Cells . Cambridge University
Press.
469
-------
too
CHO13
\
o - •> 0 mg/ml
« -- x 0.5mg/ml
D --- o l.Omg/ml
a — —A l.5mg/ml
24
DUR&TION OIL SHALE (h)
36
48
O.I 02
OIL SHALE (mg/ml)
Figure 1. The survival curves for CHO
and L-2 cells as measured by
colony formation after expo-
sure to oil shale. The mean
lethal dose (LDSO) for the
CHO line was 0.33 mg/ml and
0.14 mg/ml for the L-2 line.
Figure 2.
14 1 —• '—
\ »
i CM04/\
!2j- ' \
i / \
H /'«\
8 S ' * \
IT // l>
tit ! f ? l
5 ! /' \
*r ;/ \r
W ! / ' *
: l
»« .« 0 mg/ml
B_ _.c OOSmg /ml
& & OO75m8/ff5i
e ^s QOKDffig/wl
o-~~ — o O s5ffi9 /m!
"•—CT~ ~- — ***^"
"^^^ '^^>^'^'
•" NT-*- ^"~^.
-- \ -*• *s*-.
-
^
The percent of CHO cells
labeled with tritiated
thymidine [3H] TdR as a
function of the duration
of oil shale following
exposure to different
concentrations of oil
shale.
CHROMOSOME ABERRATION (%)
—^— 55- -jg-
0*. SHALE (h)
Figure 3, The frequency of chromosome
aberrations in CHO cells as
a function of the duration
of oil shale following ex-
posure to different concen-
tration of oil shale.
Figure 4. The accumulated frequency
of chromosome aberrations
in CHO cells over the 70
hour exposure period to
oil shale as a function
of dose.
470
-------
°-05 i.O
OIL SHALE (mg/ml)
Figure 5. The accumulated frequency of CHO cells with chromosome
aberrations over the 70 hour exposure period to oil shale
as a function of dose.
10.0
o
<
ET
ft
UJ
SO
5.0
o
CO
o
5
o
or
I
o
HUMAN LEUCOCYTES
67 h
0
1.0
OIL SHALE (mg/ml)
2.0
Figure 6. The frequency of chromosome aberrations in human leucocytes
67 hours after exposure to oil shale as a function of dose.
471
-------
I
B
D
p
1ø.
Plate 1. Types of
following
chromati d
chromosomes
chromosome
oil shale
deletion,
aberrations produced in CHO cells
treatment. (a) centric fusion, (b)
(c) dicentric, and (d) badly damaged
*
V
A
4
C
472
-------
DETECTION OF CHEMICAL MUTAGENS IN EXTRACTS OF SPENT
OIL SHALE USING THE AMES TEST
J.G. Dickson, V.D. Adams, J.H. Manwaring,
D.L. Sorensen, and D.G. Porcella
Utah Water Research Laboratory
Utah State University
Logan, Ut.ah 84322
ABSTRACT
The Ames/Salmonella-microsome test was applied to determine the carcin-
ogenic potential of spent oil shale. Solutions of chemicals suspected to be
present in extracts of spent oil shale, as well as samples of soxhiet-
extracted spent. shale, were assayed. Results indicate at least three dif-
ferent unknown chemical mutagens exist in the extracts of two spent shales.
Data is also presented regarding the effect of solvent on the mutagenic
response of the Ames test and the possibility that chemical interactions may
mask the detection of certain mutagens in chemical mixtures.
INTRODUCTION
SPENT SHALE DISPOSAL
One of the greatest concerns regarding the operation of a full-scale
oil shale industry is the environmental impact of spent shale disposal.
Under natural weathering conditions, processed shale is a potential source
of surface and groundwater contamination by inorganic salts, heavy metals
and organic chemicals (EPA, 1971; Atwood and Coomes, 1974).
Prior to studies concerning the transport mechanisms and overall fate
of these pollutants in the environment, it is essential to determine whether
that activity varies with different retort processes.
CARCINOGENIC ORGANIC CHEMICALS (PAN) AND THEIR MODE OF ACTION
A component of the organic resicue formed during the pyrolysis of oil
shale is made up of polycyclic aromatic hydrocarbons (PAH). Selected parent
compounds belonging to this class range in tumor-initiating ability from
noncarcinogenic to strongly carcinoger ic (Searle, 1976). It is hypothesized
that there is a common mode of carcinogenic activity related to chemical
structure which results in abnormal function of somatic cells (Chu, 1979;
McKinney et al, 1979). When PAH compounds are metabolized (often to an
epoxide form), the molecule is thought to intercalate into the DNA and form
a colavent bond with that structure (Marx, 1978). This process represents a
473
-------
type of mutation. In the absence of DNA excision repair, replication would
proceed with the mutation incorporated in the genome. If the cell remained
viable, its function could be altered by the mutation.
A microbiological growth test that depends on this type of mutation
could serve as a bioassay to detect chemical carcinogens which produce their
effect by somatic mutation.
AMES TEST FOR MUTAGENS
The Ames test is an example of such a bioassay and the following brief
description outlines the principles of the test (Ames, et al., 1975).
Histidine-requiring mutant strains derived from the bacterium, Salmonella
typhimurium , are used as the test organisms. These strains were selected
for sensitivity and specificity in being reverted from a histidine require-
ment back to tiistidine independence by a wide variety of mutagens (Ames, et
al. , 1975). A metabolism component is essential to carcinogenesis by PAH in
mammalian systems and it is incorporated into the microbial assay. Micro-
somal enzymes suitable for this purpose are obatined from a rat liver hom-
ogenate (S-9). A spontaneous rate of reversion exists for each TA strain
(Table 1). Therefore, it is recommended (Ames et al., 1975) that the
enhanced reversion rate can be attributed to the presence of a mutagen if it
is at least twice the spontaneous rate. The data herein are reported as net
revertants (Total Spontaneous).
OBJECTIVES
The objectives of this study were to (1) use the Ames test to detect
chemical mutagens in extracts of spent oil shale, (2) to determine which
extraction and concentration procedures for obtaining spent shale extract
samples were suitable for Ames-mutagenicity testing, and (3) to determine
whether the sensitivity of the Ames test is sufficient to allow identifica-
tion of chemical mutagens in a sample of unknown composition.
MATERIALS AND METHODS
In the plate incorporation assay, the chemical is added directly to
molten top agar (45 C) along with bacteria and liver homogenate (5-9) and
then poured onto a petri dish containing agar media. Initially, chemicals
are assayed over a wide concentration range both in the presence and absence
of S-9, using each TA strain. A positive or questionable result can be
confirmed by demonstrating a dose-response effect using a narrower range of
concentrations (Ames et a]., 1975). Several controls are included: bacter-
ial cultures and S-9 are plated to check for contamination, spontaneous
reversion rates are determined using the identical test procedure except the
appropriate solvent is invcorporated without chemical, and assays of known
mutagens are conducted as a positive control to determine whether the bac-
terial cultures are reverting normally.
In addition to known chemical solutions which serve as standards of
mutagenic activity, extracts of two types of spent shale (here referred to
474
-------
Table 1. Characteristics of the TA Strains
Strain Spontaneous Revertants
TA 1535 20
TA 1537 20
TA 1538 40
TA98 50
TA 100 160
Rat liver homogenate, S-9, added.
used for Mutagen Testing
Class of Mutations Detected
Base-pair substitution
Frame-shi ft
Frame-shift
Frame-shift
Base-pair substitution
-------
Table 2. Characteristics of Spent Shale Extract Samples
Assayed Using the Ames Test
Sample
Spent
Shale
UNKNOWN SAMPLES ASSAYED
Days Extracted in
Soxhiet and Solvent
Concentration
Technique
o 1-B
A
1-Benzene, 1-Methanol
Roto. Evap.
O 2-B
A
4-Benzene, 4-Methanol
Roto. Evap.
D 2-B
A
4-Benzene, 4-Methanol
Kuderna Danish
O 3-B
B
3-Benzene, 5-Methanol
Roto. Evap.
o 3-B
B
3-Benzene, 5-Methanol
Kuderna Danish
D 5-B
A
3-Pentane
Kuderna Danish
D 6-A
A
Methanol
Roto. Evap.
-------
as spent shale A and B), provided by our colleague, D.L. Maase, were assayed
(Maase et al., 1979). Spent shale samples were obtained by soxhlet extrac-
tion, first with benzene and then separately with methanol. The methanol
fraction was concentrated by either a rotating flash evaporator or a Kuderna
Danish apparatus (Table 2). The concentrate was diluted with methanol and
assayed along with a methanol control.
RESU LTS
SPENT SHALE EXTRACTS
Three of the five spent shale extracts assayed showed a mutagenit:
response which was dependent upon metabolic activation. Figures 1 and 2
(fractions D-1B and D-2B, respectively) indicate that at least two chemical
mutagens are extracted from spent shale A using the soxhlet extraction
procedure. This is evident by the distinct pattern of mutagenic response
exhibited and the particular TA strains which respond. This result is
likely related to the length of time the spent shale was extracted with
benzene and/or methanol. When extracted for one day with either solvent
(D-1B), four strains showed a weak response (Figure 1, Table 3). When the
shale was subjected to four days of extraction with either solvent (D-2B),
only one strain, TA 100, showed an enhanced degree of mutation (Figure 2,
Table 3). It also appears that the technique used to concentrate the
extract sample D-2B, either by flash evaporation or Kuderna Danish evapora-
tion, had little effect on the concentration and rnutagenic activity of the
chemical mutaçjen (Figure 2).
Table 3. Results of Spent Shale Extract Samples
Assayed Using the Ames Test
Sample
Spent
Shale
Results of Samples
Days Extracted in
Soxhiet & Solvent
Tested
Mutagen
Strength
TA Strain
Responding
o 1-B
0 2-B
A
A
1-Benzene, 1-Methanol
4-Benzene, 4-Methanol
+
+
98,1537,1538,100
100
O 3—B
B
3-Benzene, 5-Methanol
++
98,1537,1538,100
O 5-B
A
3-Pentane
-
none
O 6-A
A
3-Methanol
-
none
The chemical mutagen implicated by the mutagenic response of TA strains
98, 1538 and 1537 in extract D-3B from shale B, was apparently different
from either mutagen detected in spent shale A (Figure 3, Table 3). The
responding strains and the magnitude of response, in particular, suggest
this tentative conclusion. The most sensitive strains were TA 1538 and TA
98 while TA 100 showed a very weak response.
477
-------
100
ill
-I
fi-
fe
UJ
a.
75
50
cr
UJ
>
UJ
o:
UJ
25
SAMPLE D-1B
O O TA 100
a a TA98
& A TA1538
• • TAI537
RELATIVE CONCENTRATION
Figure 1. Mutagenic response of TA strains (98, 1537, 1538 and 100)
assayed with spent shale extract sample D-1B in the presence
of rat liver honogenate (5-9). Each point represents an
average of four replicates, Methanol was the solvent used.
478
-------
150
yj
100
or
LJ
Q.
CO
cr
UJ
50
UJ
SAMPLE D-2B
TA 100
O O KUDERNA DANISH
• • ROTO-EVAPORATION
RELATIVE CONCENTRATION
Figure 2. Mutagenic response of TA strains (100 only) assayed with
spent shale extract sample D-2B in the presence of rat liver
homogenate (S-9). Also shown is the effect of concentration
method (Kuderna Danish or Roto-Evaporation). Each point
represents an average of four replicates. Methanol was the
solvent used.
479
-------
500
400
UJ
Q.
Q_
V)
o:
UJ
300
200
too
SAMPLE D-3B
O O TA 100
Q Q TA98
TA 1538
TA 1537
RELATIVE CONCENTRATION
figure 3. Mutagenic response of TA strains (98, 1538, 1537 and 100)
assayed with spent shale extract sample D-3B in the presence
of rat liver homogenate (S~9). Each point represents an
average of four replicates, Methanol was the solvent used.
480
-------
The other extracts from shale A did not yield any further information
due to solvent incompatibility (pentane from extract D-5B volatilized when
mixed with molten top agar) and to dilution of sample (D-6B).
KNOWN PAH COMPOUNDS
To enable identification of the unknown mutagens in the spent 5häi
extracts, several chemicals suspected to be present were assayed using the
Ames test. A unique pattern of rnutagenic response, in terms of particular
strains sensitive to the mutagen and the shape and magnitude of the dose-
related response curves, was demonstrated for each chemical mutagen.
Examples of these characteristic patterns are shown in Figures 4 and 5 for
mutagens 7, 12 dimethylbenz(a)anthracene and benz(a)anthracene, respective-
ly. The responding strains, the relative mutagenic strength of the chemical
and the dose at which maximum response was detected, indicated that the
characteristic pattern of mutagenic response could be used to identify
chemical mutagens in a mixture of unknown composition (Table 4). For
example, the presence of benzo(ghi)perylene would be indicated by the moder-
ate response of TA 1537 at dilute concentration and no response by the other
four strains.
The original testing of known chemicals was performed using diniethyl
sulfoxide as the solvent. It was assumed that as long as the chemical
dissolved in that solvent and the solvent was not toxic to the bacteria,
that the solvent, effect of mutagenic response would be negligible. However,
since the unknown chemical solutions were dissolved in methanol we felt it
was preferable to assay all chemical standards that were soluble in methanol
in that solvent. Unfortunately, of the seven mutagens listed in Table 4,
only three were soluble in methanol at a sufficiently high concentration to
eliminate procedural problems (e.g., solvent toxicity). Figures 6, 7 and 8
show the comparative mutagenic response for 7, 12 dimethylbenz(a)anthracene,
benz(a)anthracene and fluoranthene, respectively, in the two solvents,
climethyl sulfoxide and methanol. These figures suggest that the effect of
the solvent of inutagenic response can be significant and may result in
chemical toxicity.
CHEMICAL INTERACTIONS IN MIXED SOLUTIONS
Assays were conducted employing a single strain which responded unique-
ly to two different chemicals to determine whether the Ames test could be
used to detect mutagens in chemical mixtures. These pairs included: (1) a
nonmutagen with a moderate mutagen and (2) a weak mutagen and a strong
mutagen. It was hypothesized that in a chemical mixture (vary concentration
but same 1:1 ratio) the mutagenic “esponse to the two mutagens would be
additive. In other words the nonmutagen would antagonize the response of
the moderate niutagen and the weak inutagen would enhance the response of the
strong mutagen.
The results of these assays tentatively indicate that for selected
chemicals a dcminance hierarchy exists. In particular, the dominant chemi-
cal, benz(a)pyrene ( nonmutagen for strain TA 1537) suppressed the response
of TA 1537 to perylene (a strong mutagen) in a mixture (Figure 9).
S A c 4 u erS Lthrary
481 coo : 3404T
2DC FennsvIvafl Avenue NW
VashingtOfl DC 20460
202- 6-0
-------
400
£
-i
300
200
$
100
7,12 DIMETHYLBENZCA) ANTHRACENE
TAIOO
TA98
TA 1537
25 50 75
CONCENTRATION,
100
Figure 4. Mutagenic response of TA strains (100, 98 and 1537) to 7, 12
di»ethy1benz(a)anthracene assayed at various concentrations
in presence of rat liver homogenate (S-9). Each point
represents an average of four replicates. Methanol was the
solvent used.
482
-------
1000
800
UJ
-------
Table 4. Results of known chemicals assayed using the Ames test.
Compound
Strength
Mutagen
RESULTS
of
OF CHEMICALS TESTED
Solvent
TA Strains
Responding
Concentration
for Mutagenic
Methanol
DMSO
Response
7, 12 Dimethylbenz—
(a)anthracene
-H-
x
x
100,98,1537
1538
>25 pg/plate
Benzo(a)pyrene
-I--I-I-
x
100,98,1537
5 pg/plate
Dibenz(a,h)anthracene
+
x
100,1537
25 pg/plate
Benzo(ghi)perylene
++
x
1537
>2 pg/plate
Benz(a)anthracene
+
x
x
100,98,1537
>25 pg/plate
Anthracene
—
X
none
Phenanthrene
—
X
none
Pyrene
—
X
none
Perylene
-H-I-
X
1537,98
5 pg/plate
Carbazole
—
X
none
Fluoranthene
+
X
X
100,98
10 pg/plate
-------
300
TA 100
METHANOL
DMSO
METHANOL
Q Q DMSO
200 _
UJ
or
UJ
Q_
05
I-
f* 100
a:
100
CONCENTRATION, ^g OF
7, 12 DIMETHYLBENZ(A)ANTHRACENE
Figure 6. Mutagenic response of TA strains (100 and 98) to 7,12
dimethylbenz(a)anthracene dissolved in two solvents (dimethyl
sulfoxide and methanol) in the presence of rat liver
homogenate (S-9). Each point represents an average of four
replicates. Thin vertical lines connecting symbols indicate
values which are not statistically different (t-test
a = 0.05).
485
-------
1000
800
kJ
CC
200
TA100
• • METHANOL
O 0 DMSO
TA98
• • METHANOL
CD Q DMSO
\r% ^^A.
dr^ir T
100
CONCENTRATION, ^g OF
BENZ(A)ANTHRACENE
figure 7, Mutagenic response of TA strains (100 and 98) to benz(a)-
anthracene dissolved in two solvents (dimethyl sulfoxide and
rocthanol) in the presence of rat liver homogenate (S-9).
Each point represents an average of four replicates. This
vertical lines connecting symbols indicate values which are
not statistically different (t-test, a = 0.05).
486
-------
300 __
UJ
St
or
LJ
Q.
CO
H
LJ
111
o:
H
Ld
200
METHANOL
DMSO
\ D Q DMSO
75
100
OF
CONCENTRATION,
FLUORANTHENE
Figure 8. Mutagenic response of TA strains (100 and 98) to fluoranthene
dissolved in two solvents (dimethyl sulfoxide and methanol)
in the presence of rat liver homogenate (S-9). Each point
represents an average of four replicates. Thin vertical
lines connecting symbols indicate values which are not sta-
tistically different (t-test, a = 0.05). * indicates that
the mutagenic response of strain 98 for concentrations >50 pg
was less than the spontaneous reversion value resulting in a
negative number for net revertants per plate.
487
-------
500 _
PERYLENE
BENZOCAIPYRENE/PERYLENE
BENZO(A)PYRENE
O
25 5O 75
CONCENTRATION,/^
100
Figure 9, Mutagenic response of TA 1537 to chemicals perylene and
ben?o(a)pyrene assayed separately and together in a chemical
mixture (1:1 ratio, by weight) in the presence of rat liver
homogenate (S-9). Each point represents an average of four
replicates. Dimethyl sulfoxide was the solvent used. Thin
vertical lines connecting symbols indicate values which are
not statistically different (t-test, a = 0.05).
488
-------
Alternatively, the presence of 7, 12 dimethylbenz(a)anthracene was not
detected when it was assayed along with benzo(a)pyrene (a strong mutagen)
using TA 98 (Figure 10).
With most chemical pairs assayed in a 1:1 solution, the dominance
seemed to be complete; the mutagenic response to the mixture could not be
distinguished from the rnt tagenic response to one of the chemicals alone.
These data suggest that the presence of certain chemicals can mask the
detection of subordinant mutagens in the Ames test. For this reason,
samples of unknown composition should be fractionated prior to mutagenic
testing.
DISCUSSION
While three apparently different chemical mutagens were detected in
extracts of spent oil shale using the Ames test, unanticipated problems of
solvent effect on mutagenicity and chemical insolubility limited the number
of known chemical mutagen standards that could be used for identification
purposes. For these reasons it is nearly impossible to attempt identifica-
tion based either on the results presented herein or reported elsewhere.
The Ames test proved successful in detecting potential environmental
carcinogens in spent shale extracts. The nature of chemical interactions
shown to exist in solutions cannot preclude the possibility that other
mutagens are present in these mixtures or that samples not showing mutagenic
activity are without mutagens. We agree that the Ames test is a powerful
tool for use in the detection of environmental carcinogens, however, we
emphasize that it should be used as originally intended, for initial screen-
ing and should be followed by other testing procedures.
CONCLUSIONS
Regarding the objectives of this study, the following conclusions were
made:
1. The Ames test has been found suitable for the detection of
chemical mutagens in refined extracts of spent oil shale.
2. The soxhiet procedure using a combination of solvents, ben-
zene and methanol, has been found effective in isolating
chenical mutagens from spent oil shale.
3. Although pentane is an effective extraction solvent, the
solution should be prepared by evaporating and redissolving
in another more suitable solvent (dimethyl sulfoxide or
methanol) prior to mutagenicity testing.
4. Use of the Ames test for identification of chemical mutagens
in a sample of unknown composition appears limited at this
time, due to the effect of solvent on mutagenic response and
the lack of knowledge regarding the nature of chemical inter-
actions in niixed solutions.
489
-------
200
LLI
IT
LJ
CL
CO
UJ
£T
I-
Ul
100
O O BENZO(A)PYRENE (BaP)
• • BaP/DMBA
A A 7,12 DIMETHYLBENZ(A)ANTHRACENE
(DMBA)
0
25 50 75
CONCENTRATION,
100
Figure IQ.Mutagenic response of TA 98 to chemicals benzo(a)pyrene and
7,12 dimethy1benz(a)anthracene assayed separately and to-
gether in a chemical mixture (1:1 ratio, by weight) in the
presence of rat liver homogenate (S-9). Each point repre-
sents an average of four replicates. Dimethyl sulfoxide was
the solvent used. Thin vertical lines connecting symbols
indicate values which are not statistically different
(t-test, a = 0.05).
490
-------
ACKNOWLEDGEMENTS
The authors wish to thank Drs. B.N. Ames and J. McCann for tester
strains and advice. In addition, we would like to acknowledge the Office of
Water Research and Technology (Project No. B-154-UTAH: Contract No. 14-34
0001-8123), United States Department of the Interior, Washington, DC, which
provided funds for research and publication, as authorized by the Water
Research and Development Act of 1978.
Contents of this publication do not necessarily reflect the views and
policies of the Office of Water Research and Technology, U.S. Department of
the Interior, nor does mention of trade names or commercial products consti-
tute their endorsement or recommendation for use by the U.S. Government.
REFERENCES
1. Ames, B.N., J. McCann, and E. Yamasaki. Methods for Detecting Carcino-
gens and Mutagens with the Salmonella/Mammalian-Microsome Mutagenicity
Test. Mut. Res. 31:347-364, 1975.
2. Atwood, M.T. and R.M. Coomes. The Question of Carcinogenicity in
Intermediates and Products of Oil Shale Operations. Colony Paper, May
1974.
3. Chu, K.C. Quantitative Structure-Activity Relationships in Chemical
Carcinogens. Paper presented at ACS/CSJ meetings, Honolulu, HI, April
1-6, 1979.
4. Environmental Protection Agency. Water Pollution Potential of Spent
Oil Shale Residues. Colorado State University, Fort Collins, CO.
Grant No. 14030 EDB, December 1971.
5. Maase, D.L., V.D. Adams and 0.8. Porcella. Isolation and Identifica-
tion of Organic Residue from Processed Oil Shale. Paper presented at
EPA Oil Shale Sampling, Analysis and Quality Assurance Symposium,
Denver, GO, March 26-28, 1979.
6. McKinney, J.D., P. Singh, L. Levy and M. Walker. High Toxicity and
Cocarcinogenic Potential of Certain Halogenated Aromatic Hydrocarbons.
Some Structure-Activity Aspects. Paper presented at ACS/CSJ meetings,
Honolulu, HI, April 1-6, 1979.
7. Marx, J.i. DNA Repair: New Clues to Carcinogenesis. Science Vol.
200. (No. 4341):218-221, 1978.
8. Searle, G.E. (ed.) Chemical Carcinogens , American Chemical Society
Monograph 173. Washington, DC, 1976, pp. 788.
491
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FLOW CYTOMETRIC METHODS FOR ASSAYING
DAMAGE TO RESPIRATORY TRACT CELLS
J. A. Steinkamp
Biophysics Group
J. S. Wilson
Mammalian Biology Group
Los Alamos Scientific Laboratory
University of California
Los Alamos, New Mexico 87545
ABSTRACT
This paper summarizes results of experiments designed to develop
automated flow-analysis assay methods for discerning damage to exfoliated
respiratory tract cells in model test animals exposed by inhalation to
physical and chemical agents associated with the production of synthetic
fuels from oil shale, the specific goal being the determination of atypical
changes in exposed lung macrophage5 and epithelial cells. Animals were
exposed to oil shale particulates (raw and spent), silica, and ozone, and
respiratory tract cells were obtained by lavaging the lungs with normal
saline. Cell samples were stained with fluorescent dyes specific for
different biochemical parameters and analyzed as they flowed through a
chamber intersecting a laser beam(s) of exciting light where sensors
measured fluorescence and light scatter (cell size) on a cell-by-cell basis.
Cellular parameters proportional to optical signals were displayed as
frequency distribution histograms. Cells also were separated according to
cytological features and identified. The basic features of the methodology
are presented, along with examples of results that illustrate characteriza-
tion and analysis of normal and exposed respiratory tract cells based on DNA
content, total protein, size, and phagocytic activity.
INTRODUCTION
The application of advanced flow cytometric instrumentation to measure
cytological and biochemical properties of respiratory tract cells provides a
new approach for assessing potential damage to lung epithelium exposed by
inhalation to toxic environmental pollutants associated with the production
of synthetic fuels from oil shale and coal.’ This includes development of
automated cytological methods for determining atypical changes in exfoliated
respiratory tract cells from experimental animals, the end objective being
examination of sputum samples from exposed humans. To develop analytical
flow-analysis methods for quantitative assessment of cellular damage, auto-
mated cell-analysis and sorting instrumentation 6 8 is being applied to study
respiratory tract cells from hamsters exposed to particulates of oil
492
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shale, silica, and ozone. This includes the acquisition of exfoliated lung
cells by lavaging the respiratory tract with normal saline; utilization of
fluorescence staining methods to measure cellular biochemical parameters:
and exposure of experimental animals to physical and chemical toxicants,
followed by flow cytometric analysis. Examples of results from initial
studies involving measurement of DNA content and total protein in normal arid
exposed respiratory tract cells are presented, along with a brief descrip-
tion of the instrumentation technique. A new method for quantitating
pulmonary macrophage phagocytosis in rats using fluorescent microspheres
also is under development. This technology provides a new approach for
studying the mechanisms of damage to respiratory tract cells, with future
anticipated results serving to assist in estimating risks, evaluating dose-
damage relationships, and establishing guidelines for determining exposure
levels to humans.
MATERIALS AND METHODS
The principle of measurement is illustrated in Figure 1. Normal and
exposed respiratory tract cells composed of macrophages, leukocytes,
ciliated columnar and basal undifferentiated cells stained with fluorescent
dyes were analyzed in liquid suspension as they flowed through a chamber
intersecting a laser beam(s) of exciting light. 9 ’’ 0 Multiple sensors
measured fluorescence and light-scatter optical signals on a cell-by-cell
basis. CeliuL r parameters proportional to optical measurement (e.g. , DNA
content, tota protein, cell size, and phagocytic activity) were displayed
as frequency distribution histograms using a multichannel pulse-height
analyzer. Cells also were separated according to various cytologic
parameters and identified microscopically.
To study cellular changes in animals exposed to particulates of oil
shale and sil ca, Syrian hamsters were injected intratracheally with 10 mg
of ball-milled (2- to 7-pm diameter range) raw and spent oil shale and
silica (4-pm n ean diameter) suspended in 0.2 ml of normal saline. Hamsters
were exposed also to 0.2 ml of saline alone. Raw shale (type 2) was
obtained from Anvil Points, Colorado. The two spent shales (types 1 and 2)
were from solid heat transfer and gas combustion processes, respectively.
Silica was obtained from the Pennsylvania Glass and Sand Corporation.
Hamsters anesthetized with “Brevital” (5 mg) prior to intratracheal instil-
lation of particulates and saline via the oral cavity were returned to the
colony. Animals were sacrificed by pentobarbital injection 28, 35, and 42
days later. The lungs were then lavaged four times with saline to obtain
exfoliated cells, which were fixed in 35% ethanol prior to staining for DNA
content with mnithrarnycin, 11 ’ 12 excited at 457 nm wavelength (argon laser),
and analyzed for fluorescence properties.
Syrian hamsters also were exposed to acute levels of ozone (4 ppm for 4
hour) and sacrificed at different times ranging from 0 to 56 hour after
termination of exposure. Respiratory tract cells were obtained at sacrificE
using pentobarbital, followed by lavaging the lungs with saline, fixng in
ethanol, staining with mithramycin ( [ ‘NA content), and analysis.
493
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NOJ
LIGHT
SCATTER
CELL
FLUORESCENCE
KRYPTON
LASER
ARGON
LASER
Figure 1.
Cutaway
chamber,
view of the
illustrating
mul tiparameter cell
dual-laser excitation.
separator
CELL
FLOW CHAMBE
VOLUME
SENSING
ORIFICE
SHEATH NO. 2
4 ,
EXIT ORIFICE/
.
N
flow
494
-------
Normal hamster respiratory tract cells also were characterized initial-
ly based on DNA content and total protein. Cells were obtained by
sacrifice, followed by lung lavage using saline. DNA content and total
protein were measured by fixing the cells in 35% ethanol, staining with
mithramycin (ONA) and rhodamine 640 (protein), and analyzed for two-color
fluorescence properties by exciting bound fluorochromes at 457 nm (argon
laser) and 468 nm (krypton laser), respectively. 10
To develop an automated method for quantitating alveolar macrophage
phagocytosis, normal Sprague-Dawley rats were anesthetized by inhalation of
Metafane. The trachea was then intubated with a blunt needle via the oral
cavity, and 1 to 2 x io polystyrene latex spheres (fluorescent) of 1.83-pm
diameter suspended in 0.5 ml of saline were delivered to the respiratory
tract. After 2 hours, rats were sacrificed by pentobarbital injection and
their lungs lavaged with 4 ml of saline (four times). Cells were fixed in
35% ethanol, rinsed and resuspended in saline, excited at 457 nm (argon
laser), and analyzed for fluorescence (phagocytized spheres) and light
scatter (size).
RESULTS AND DISCUSSION
DNA Measurements: Respiratory Tract Cells Exposed to Oil Shale Particulates
and Silica
To initiate studies with classes of particulates and known toxic
agents, hamsters were exposed to raw and spent oil shale particulates and
silica by intratracheal injection. Since DNA content distributions showed
no significant changes compared to controls up to 28 days after exposure, it
was decided to examine respiratory tract cells that had been exposed 28 days
or more. Figure 2 shows the DNA content per cell distribution of respira-
tory tract cells from hamsters exposed to saline, silica, and raw and spent
oil shale particulates 28, 35, ano 42 days after exposure. DNA content
distributions of normal control animals are shown in Figure. 2A. Peak 1
represents cells having 2C DNA content and peak 2 binucleated cells and
doublets (4C DNA content). 5 DNA content distributions of lung cells from
hamsters exposed to saline (Figure. 2B) closely resemble controls. However,
DNA content distributions of lung cells from hamsters exposed to silica
(Figure. 2C), which appear normal t 28 days postexposure, begin to show
atypical chancies at 35 and 42 days. A third region has appeared to the left
of peak 1, which is most likely dead cells. At 42 days, cells within region
3 have increased and a shoulder is beginning to develop on the right side of
peak 1. The percentage of binucleated cells appears to be increasing also.
Preliminary DNA content distributions of lung cells exposed to raw and spent
oil shale are shown in Figs. 2D, 2E, and 2F. Figure 2D illustrates DNA
content distributions of lung cells exposed to type 1 spent shale. These
distributions appear nearly normal, with the exception that the left side o
peak 1 is skewed.
DNA content distributions from respiratory tract cells exposed to
type 2 raw and spent shale are shown in Figs. 2E and 2F, respectively.
Distributions from hamsters exposed to raw shale appear nearly normal
495
-------
however, DNA cc itent distributions from hamsters exposed to spent shale show
atypical changes 35 and 42 days postinstillation. A definite shoulder
appeared on the right side of peak 1, and the number of cells within
region 3 increased. These changes were better observed by increasing the
amplifier gain of the fl uorescenc channel, thus centeri ng peak 1 1 n
channel 30 of the multichannel pulse-height analyzer (Figure. 3). Peak 1
now shows a well—defined region of cells to the right side that is similar
to the results from hamsters exposed to ozone, as described below. Although
these cells have not been identified at this time, experiments are under way
to determine the cell types present in peaks 1 and 3.
DNA Measurements: Respiratory Tract Cells Exposed to Ozone
Hamsters were exposed to acute levels of ozone (4 ppm for 4 hour) and
sacrificed at different times after exposure. These results are shown in
Figure. 4. Figure 4A shows a typical DNA content distribution obtained on a
normal (control) hamster. DNA content distributions obtained from a hamster
immediately at (0 hour) and 1 hour after exposure are shown in Figs. 4B and
4C. Peaks 1 and 2 both show a general broadening, with an increase in the
t.otal number of cells contained in peak 2 (binucleated cells and doublets).
DNA content distributions measured on hamster lung cells 3, 5, and 7 hour
after exposure (Figs. 4D, 4E, and 4F) appear similar. Peak 1 is divided
into two separate parts (bimodal distribution), whereas the number of cells
contained within peak 2 has diminished. The DNA distribution of hamster
respiratory tract cells 28 hour after exposure is shown in Figure. 4G. This
distribution, which is similar to those recorded in Figs. 4D, 4E, and 4F,
has an increased percentage of cells between peaks 1 and 2. Figure 4H shows
a DNA content distribution for hamster cells 48 hour after exposure, which
“resembles” a typical DNA distribution for randomly growing CHO cells in
which peak 1 r presents G 1 -phase cells (2C DNA content) and peak 2 G 2 and
M-phase cells (4C DNA content). Cells located between peaks 1 and 2 would
then be S-phase cells. In Figure. 41 (56 hour after exposure), the DNA
content distribution per cell has reverted back to resemble distributions
recorded at earlier times after exposure (Figs. 4D, 4E, and 4F).
These initial results vividly demonstrate the importance of using flow
cytometric analysis methods as a new methodology to study the effects of
exposure and recovery to known toxic agents. Future experiments will
consist of verifying these results, studying other time increments after
exposure, and correlating cytology (morphological features, differential
cell counts, etc.) with DNA content measurements. Cells also will be sorted
and microscopically identified. DNA measurements also can be used to study
cell-cycle kinetics and would thus permit recovery mechanisms to be analyzed
dynamically, especially when coupled with other cellular parameters (e.g.
protein, enzymes, etc.) using multiparameter analysis methods.
DNA-Protein Measurements: Normal Respiratory Tract Cells
A new dual-laser multiparameter flow system has broad potential appli-
cation in basic cell biology research, including the analysis of respiratory
tract cells. This system has been used recently to measure DNA content with
4%
-------
2,000 -
HAMSTER RESPIRATORY TRACT CELLS EXPOSED TO SILICA AND SHALE PARTICULATES
~~~! 1 1 1 1
(A)
o
2,000
LU
O
DC
LU
CO
z o1
2,000]
CONTROLS
I
r
50
(B)
J
SALINE
(+28 Days)
(+35 Days)
.L
(+42 Days!
I .....i.
0 50
CHANNEL NUMBER
J L,
SILICA
(+28 Days)
(+35 Days)
(+42 Days)
Figure 2. (2A, 2B, and 2C). The DNA content per cell distribution of
respiratory tract cells from hamsters under control, saline
and silica exposures.
497
-------
-
TYPE 1
SPENT
(-1-28 Days!
CO
_l
_i
yj
U
u_
O
cr
LLJ
m
D
r
2.000-
t+42 Days)
(F) TYPE 2
H SPENT I
M (+28 Days)
I; J
(+35 Days)
•
%«tlH.I.«*f**»..
fl (+35 Days)
I •
0
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50
(+42 Days)
/I
' a J-** j_ *>%»»L^JiM^.^»llll)»^ia - t
0 50
CHANNEL NUMBER
i+42 Days)
1
50
Figure 2. (20, 2E, and 2F). DNA content frequency distribution
histograms of hamster respiratory tract cells exposed
(intratracheal injection) to type 1 spent shale, and type 2
raw and spent shale prior to sacrificing 28, 35, and 42
days later. Cell samples were obtained by lung lavage,
fixed in 35% ethanol, stained with mithramycin, and analyz-
ed for fluorescence. Types 1 and 2 spent shales were
obtained fron solid heat transfer and gas combustion pro-
cesses, respectively.
498
-------
2,000
HAMSTER 1
C/)
LU
O
o
HAMSTER RESPIRATORY TRACT
CELLS 42 DAYS AFTER
EXPOSURE TO TYPE 2
SPENT SHALE
2,000
HAMSTER 2
LU
00
100
CHANNEL NUMBER
Figure 3. DNA content frequency distribution histrograms of hamster
respiratory tract cells exposed (intratracheal injection)
to type 2 spent shale prior to sacrificing 42 days later.
Cell samples were obtained by lung lavage, fixed in 35%
ethanol, stained with mithramycin, and analyzed for fluo-
rescence. Type 2 spent shale was obtained from a gas
combustion process.
499
-------
2,000r
1 ---- 1 - 1
(A)
CONTROL
(B)
0 HRS AFTER
EXPOSURE
(C)
1 HR AFTER
EXPOSURE
ZOOOr
(D)
3 HRS, AFTER
EXPOSURE
5 HRS AFTER
EXPOSURE
(F) 7 HRS AFTER
EXPOSURE
(I)
56 HR& AFTER
EXPOSURE
i
50 WO 0
CHANNEL NUMBER
Proportional to DNA Content)
50
100
Figure 4. DNA content frequency distribution histograms of hamster
respiratory tract cells fixed in ethanol (70%) and stained
with mithramycin. Hamsters were exposed to 4 ppm of ozone
for 4 hours prior to obtaining samples at increments rang-
ing from 0 to 56 hours after exposure.
500
-------
dyes having ultraviolet absorption ranges 4 and to analyze DNA content ar 1 d
protein in cel 1 s stained with mithramycin and rhodamine.’° Mithramycin and
rhodamine have violet and yellow excitation ranges, respectively, with
overlapping emission spectra.’° This measurement is made possible only
through the use of dual-laser excitation. For example, hamster respiratory
tract cells have been analyzed recently using this procedure, as illustrated
in Figure. 5. Peak 1 of the DNA content distribution reoresents mono-
nucleated cells (macrophages, leukocytes, etc.) having 2C DNA content and
peak 2 binucleated cells and doublets (4C DNA content). Figure 5B, which
represents the distribution of protein within the lung cell population, is
broad and similar to that previously reported using the propidium iodide-
fluorescein isothiocyanate method. 3 The nuclear and cytoplasmic diameter
distributions are shown in Figs. 5C and 50, respectively. Peaks 1 and 2
(cytoplasmic diameter distribution) have been identified recently as being
composed of (a) leukocytes and (b) macrophages and epithelial cells,
respectively. This new staining and analysis method has broad application
in measuring the biochemical and cytological properties of respiratory tract
epithelium.
Quantitation of Pulmonary Macrophage Phagocytosis
Phagocytic activity, which is the primary function of pulmonary
macrophages, is normally measured by exposing test animals to toxic agents,
followed by intratracheal injection of micron-sized polystyrene latex
particles or bacteria for a fixed time period and lung lavage to remove
rnacrophages, and microscopic enumeration of macrophages containing 1, 2, 3,
etc. , particles per cell. Described below is a new method to study the
mechanisms of phagocytosis of alveolar macrophages from experimental animals
exposed to toxicants using 1.83-pm diameter polystyrene latex (fluorescent)
spheres. Figure 6A shows the fluorescence distribution of phagocytized and
nonphagocytized spheres obtained from lavaging the respiratory tract. This
distribution was obtained by recording the fluorescence signals from
macrophage-ingested spheres and nonphagocytized particles. Peaks 1, 2, and
3 of Figure. 6A represent single macrophages that contain one sphere or a
single sphere alone; single macrophages containing two spheres or two
spheres stuck together (doublet); and single macrophages containing three
spheres or three spheres stuck together (triplet), respectively. To
distinguish between macrophages that have phagocytized spheres and non-
phagocytized particles, the light-scatter method 6 ’ 9 for cell-size determina-
tion was used. Since 1.83-pm diameter spheres are much smaller than
pulmonary cells, they did not appear in the cell-size distribution
(Figure. 6B). Peak 1 is thought to be leukocytes and cellular debris.
Peak 2 has been identified to represent macrophages that do and do not
contain phagocytized spheres. Therefore, by requiring fluorescence signals
to be or not to be in coincidence with light-scatter signals (cells), non-
phagocytized spheres and macrophages that have phagocytized spheres can be
distinguished. For example, Figure. i5C shows the fluorescence distribution
of only macrophages that have ingested spheres as obtained by displaying
only those fluorescence signals that also scatter light. Cells contained
within peaks 1 to 5 (Figure. 6C) represent macrophages having phagocytized 1
501
-------
2,000
u
Li_
O
CO
Z
(A)
DNA CONTENT
2,OQOr
NUCLEAR
DIAMETER
50
100 0
CHANNEL NUMBER
CYTOPLASM 1C
DIAMETER
100
figure 5. Frequency distribution histograms of normal hamster respi-
ratory tract cells fixed in ethanol (70%) and stained with
iii thramycin (DNA content) and rhodamine 640 (total pro-
tein): (a) DNA content; (b) total protein; (c) nuclear
diameter; and (d) cytoplasmic diameter. The nuclear and
cytoplasmic diameter distributions were obtained by measur-
ing the time durations of the fluorescence signals from the
nucleus (mithramycin) and cytoplasm (rhodamine), respec-
tively.
502
-------
2.000 ;
(A)
FLUORESCENCE DISTRIBUTION
NONPHAGOCYTIZED AND PHAGOCYT1ZED
SPHERES
CO
X
CJ
(- 2000-
CL :
CO
UJ
CJ
DC
LU
03
2000 r
(B!
LIGHT SCATTER DISTRIBUTION
CELLS INCLUDING MACROPHAGES
CONTAINING PHAGOCYT1ZED SPHERES
• C
FLUORESCENCE DiSTRIBLmON
PHAGOCYTtZED SPHERES
/ \
50
100
128
CHANNEL NUMBER
Figure 6. Frequency distribution histograms of microspheres and cells
obtained by sacrificing normal rats and lavaging the lungs
with saline 2 hours after instilling 1 to 2 x 107 1.83-um
diameter fluorescent spheres in 0.5 ml saline: (a) fluo-
rescence distribution of nonphagocytized and phagocytized
spheres obtained by recording all fluorescence signals; (b)
light-scatter distribution (size) of cells, including
macrophages containing phagocytized spheres; and (c) fluo-
rescence distribution of phagocytized spheres obtained by
recording only those fluorescence signals associated with
light-scatter signals. Cells were fixed in 35% ethanol
prior to fluorescence and light-scatter analysis.
503
-------
to 5, respectively, as identified b3, sorting cells from each peak. 5 This
technique has potential for permitting rapid and accurate determination of
phagocytosis and will be used subsequently to assay for toxicity related to
macrophage function.
ACKNOWLEDGMENTS
This work was performed under the auspices of the United States Depart-
ment of Energy, with joint support from the United States Environmental
Protection Agency (interagency agreement EPA-IAG-D5-E681).
REFERENCES
1. Steinkamp, J. , M. Ingram, K. Hansen, and J. Wilson. Detection of Early
Changes in Lung Cell Cytology by Flow-Systems Analysis Techniques. Los
Alamos Scientific Laboratory report LA-6267-PR, March 1976.
2. Steinkamp, J. , K. Hansen, J. Wilson, and G. Salzman. Detection of
Early Changes in Lung Cell Cytology by Flow-Systems Analysis Tech-
niques. Los Alamos Scientific Laboratory report LA-6418-PR, August
1976.
3. Steinkamp, J. , K. Hansen, J. Wilson, and G. Salzman. Detection of
Early Changes in Lung Cell Cytology by Flow-Systems Analysis Tech-
niques. Los Alamos Scientific Laboratory report LA-6602-PR, December
1976.
4. Steinkamp, J., K. Hansen, J. Wilson, and L. Holland. Detection of
Early Changes in Lung Cell Cytology by Flow-Systems Analysis Tech-
niques. Los Alamos Scientific Laboratory report LA-6888-PR, July 1977.
5. Steinkamp, J. , K. Hansen, J. Wilson, G. Saunders, D. Orlicky, and H.
Crissman. Detection of Early Changes in Lung Cell Cytology by Flow-
Systems Analysis Techniques. Los Alamos Scientific Laboratory report
LA-7247-PR, April 1978.
6. Mullaney, P. , J. Steinkamp, H. Crissman, L. Cram, and D. Holm. Laser
Flow Micrciphotometers for Rapid Analysis and Sorting of Individual
Mammalian Cells. In: Laser Applications in Medicine and Biology,
Wolbarsht, M. L. (ed.). New York-London, Plenum Press. 1974. Vol. 2,
p. 151-204.
7. Crissman, H. , P. Mullaney, and J.. Steinkamp. Methods and Applications
of Flow Systems for Analysis and Sorting of Mammalian Cells. In:
Methods in Cell Biology, Prescott, D. M. (ed.). New York, Academic
Press. 1975. p. 179-246.
8. Steinkamp, J. Multiparameter Analysis and Sorting of Mammalian Cells.
In: Methods of Cell Separation, Catsimpoulas, N. (ed.). New York-
London, Plenum Press. 1977. p. 251-300.
504
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9. Steinkamp, J. , M. Fuiwyler, J. Coulter, R. Hiebert, J. Homey, and P.
Mullaney. A New Multiparameter Separator for Microscopic Particles and
Biological Cells. Rev Sci Instrum. 44:1301-1310, 1973.
10. Steinkamp, J. , D. Orlicky, and H. Crissman. Dual-Laser Flow Cytometry
of Single Mammalian Cells. J Histochem Cytochem. 1979, in press.
11. Crissman, H., and R. Tobey. Cell Cycle Analysis in Twenty Minutes.
Science. 184:1297-1298, 1974.
12. Crissinan, H. , M. Oka, and J. St inkamp. Rapid Staining Methods for
Analysis of DNA and Protein in Mammalian Cells. J Histochem Cytochem.
24:64-71, 1976.
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BIOLOGICAL MONITORING METHODOLOGIES FOR OIL
SHALE AREA SURFACE WATERS WITH EMPHASIS ON
MACROINVERTEBRATE SAMPLING TECHNIQUES
Wesley L. Kinney
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Las Vegas, Nevada 89114
C. Evan Hornig and James E. Pollard
Department of Biological Sciences
University of Nevada, Las Vegas
Las Vegas, Nevada 89154
ABSTRACT
There exists a pressing need for reliable biological sampling method-
ologies applicable to streams of the semi-arid west. This is particularly
relevant for rivers such as the White and Yampa which are potentially
subject to nonpoint pollution impact as a result of oil shale and coal
development.
The efficiency of two types of artificial substrate samplers (basket
and multiple-plate), the Surber sampler, and variations of a traveling-kick
method was evaluated for describing macroinvertebrate communities represent-
ative of the White River, Utah and Colorado. Basket samples provided the
largest number of animals per sample, while the kick method provided data
with the best statistical reproducibility. Multiple-plate and Surber
samples provided highly variable results in terms of the number of animals
and taxa collected. The kick technique was effective in riffle areas where
the bottom fauna was particularly sparse and where a prohibitive number of
Surber or multiple-plate samples would be required to adequately describe
the benthic community.
INTRODUCTION
This paper addresses aspects of environmental monitoring that all too
frequently are omitted in discussions of surface water quality monitoring
requirements. We refer to the need to incorporate biological components
into comprehensive water quality monitoring programs to broaden the spectrum
and increase the efficiency of monitoring networks. In this paper we point
out certain advantages to biological monitoring and address some of the
approaches and associated problems. Although much of this discussion
relates to aquatic biomonitoring as it applies regionally or nationally,
506
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particular emphasis is focused on an evaluation of methodologies for sampl-
ing aquatic macrobenthic communities in streams in the oil shale area.
The authors of the Federal Water Pollution Control Act Amendments of
1972 (PL 92-500) recognized the significance of aquatic organisms as natural
monitors of water quality and recommended their use for that purpose.
However, in an essay entitled, “Problems in Implementing U.S. Water Quality
Goals,” Westrnan’ contended that biological monitoring requirements of PL
92-500 were not being met and attributed that failure in part to:
(1) the lack of biologically trained personnel at the monitoring
sites;
(2) a long—standing bias toward chemical and physical rather than
biological monitoring; and
(3) a lack of guidelines for conducting biological monitoring in
receiving waters.
He further stated that neither EPA’s Office of Research and Development nor
the Department of Interior’s Fish and Wildlife Service is conducting any
research on development of biological monitoring methodologies.
From a national perspective, Westman is probably correct in his conten-
tion that biological monitoring in receiving waters is not being conducted
at all places “where appropriate.” The lack of biologically trained person-
nel at the monitoring sites is obviously a factor, but this is not due to a
shortage of competent biologists. Rather it reflects the long-standing bias
toward physical and chemical monitoring to which Westman refers in his
second point. His third point, a lack of guidelines for conducting biolog-
ical monitoring, is only partially justified. EPA has issued guidelines for
biomonitoring in receiving waters in the form of manuals and numerous indi-
vidual technical papers and has funded the development of many others.
Furthermore, considerable research effort is being directed toward the
development and refinement of biomonitoring methodologies by EPA.
As viewed from the standpoint of the analytical chemist, the state-of-
the-art of biological monitoring is still quite primitive. The water
quality biologist attempts to unravel the complexities of a highly intricate
system in an effort to determine if observed changes in the biota are real
and, if so, whether these changes can be attributed to man-made influences.
Consequently, additional research must be directed towards the development
of new techniques and the refinement of existing methodologies in a continu-
ing effort to describe the most cost-effective methodologies for specific
monitoring applications.
507
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BIOLOGICAL MONITORING
ADVANTAGES
Foreign materials introduced into an aquatic environment interact in a
complex and often non-linear manner with one another and with the numerous
other factors inherent to the environment. 2 Aquatic organisms and communi-
ties respond to the sum of the interactions of these environmental factors.
Thus, biomonitoring is particularly well suited to detecting changes in
ambient conditions caused by both suspected and unsuspected foreign mater-
ials, even though the actual cause-effect relationships may be too complex
to readily evaluate.
An additional and very significant advantage of biological monitoring
is that it provides a mechanism for the integration of conditions between
sampling periods. Aquatic communities are affected by short-lived perturba-
tions of the environment and these effects normally persist for the weeks or
months required for the communities to recover. 3 Thus, periodic biomoni-
toring can be used to detect short-term events, which chemical/physical
monitoring is unlikely to detect. Biological monitoring, then, is especial-
ly advantageous because it will detect the full spectrum of suspected and
unsuspected impacts including manifestations of intermittent insults even
through periodic sampling. Pollution is fundamentally a biological problem.
We monitor certain chemical-physical parameters primarily because we know or
suspect that they directly or indirectly affect living organisms.
APPROACHES
Biological monitoring generally approaches the recognition or detection
of problems in aquatic systems by three basic types of measurements:
1) toxicity of pollutants both in the field and laboratory; 2) bioconcentra-
tion; and 3) community composition and structure.
Measurements of the toxicity of pollutants provide information on the
effects of their exposure upon living organisms. Such measurements can be
conducted in a laboratory under closely controlled conditions, or in the
field under ambient conditions. There are advantages and disadvantages to
both approaches. Laboratory environments are easily controlled, but do not
precisely simulate field conditions. Results obtained in the laboratory are
thus of limited value as predictors of the impacts of pollutants when
applied to the “real-life” conditions of a specific environment. 4 Field
measurements of toxicity (e.g. , caged animals placed in an outfall or
receiving stream) are direct in their approach, but are subject to vandal-
ism, flooding, and a host of other uncontrollable factors including exposure
of test organisms to unknown constituents and dosages for indefinite periods
of time. Feedback between laboratory and field studies will, on the one
hand, aid in the refinement of toxicity tests and, on the other hand, add
light to the information collected in the field. In an optimal monitoring
program, laboratory testing would be complemented by field studies designed
to assess the actual impacts of particular substances or combinations of
substances on organisms within a particular environment.
508
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The result:; of toxicity studies may be readily apparent, as in acute
tests which result in the death of a percentage of test organisms that are
exposed for a finite period to known concentrations of a particular sub-
stance (e.g., LC 50 ), or tests in which caged test organisms die when exposed
to an effluent. More frequently, however, responses are less pronounced and
result in alterations of physiological or behavioral responses such as
respiration rate, movement, reproductive success, incidence of tumors, etc.
Chronic exposure to low-level concentrations may have no measurable effect
on the test organisms themselves, but the effect of exposure may be mani-
fested several generations later, in the form of abnormalities resulting
therefrom.
The second biomonitoring approach utilizes the characteristic of living
organisms to act as natural compositors and integrators of substances from
the surrounding water medium. Plants and animals accumulate and concentrate
substances in tissue through bioaccumulation (uptake from the water) and
biomagnification (uptake through the food chain). The uptake characteris-
tics of organisms vary by individual, species and trophic level. Ideally,
receptors at several trophic levels would be included in bioconcentration
studies. The most obvious advantage of bioconcentration studies is that
they provide detection of hazardous materials at levels below analytical
limits and sugciest the potential hazard to various food chain components,
including man. Thus, these analyses offer an efficient means for screening
and identifying potentially hazardous substances in water before they pose a
serious detriment to human health and aquatic life.
The third type of measurement--one in which the Environmental Monitor-
ing Systems Laboratory (EMSL-LV) is very directly involved--is the response
of aquatic communities to pollution-induced stress. It is difficult to
obtain reliable measurements of this type because of the need to sample
highly variable populations where composition and structure are subject to
all the perturbations of the environment, both natural and man-induced. For
example, immature aquatic insects (which are the most prominent organisms in
most macrobenthic samples from streams of semi-arid regions) are highly
sensitive to pollution-induced stress conditions, but they also show large
natural seasonal variations due to characteristics of their life cycles and
natural fluctuations in stream conditions. In addition, identification of
many groups of these insects poses special problems, due largely to the lack
of taxonomic information on their immature stages. In spite of the diffi-
culties involved in characterization of aquatic communities, be they fish,
invertebrates or plant life, carefully planned and executed investigations
are well worth the effort. They offer a fairly efficient means of detecting
pollution-induced stress once the natural community patterns have been fully
described.
INTEGRATION WITH PHYSICAL-CHEMICAL MON [ TORING
The use of biological monitoring to complement physical-chemical
approaches is particularly advantageous for instream monitoring in the oil
shale area. Developmental activities will most likely result in nonpoint-
source impacts via landscape disturbances, diffusion through ground water,
1.
‘ -I
-------
and fallout from the atmosphere. In addition, accidental spills and dis-
charges pose a substantial threat. A strictly physical-chemical monitoring
network that would monitor all suspected pollutants continually and through-
out the stream reaches of the potentially affected area would be very
difficult and expensive to operate and could still fail to detect unsuspect-
ed or low-level pollutants. However, oeriodic biological samples, collected
from strategically located stations, could detect Dollution-induced changes
in the biota and provide an alert to hazardous conditions. In response to
such an alert, intensive physical-chemical monitoring could pinpoint both
the “danger spots” and the causative agents and sources. As additional
information becomes available concerning the relationships between specific
pollutants and specific changes in the biota, the identification of the
pollutants and their sources will become easier. Only the complementary use
of biological and physical-chemical monitoring will make it feasible to
detect impacts and their sources over entire stream systems.
COMMUNITIES MOST SUITABLE FOR MONITORING
The stream communities in the oil shale area most appropriate for
monitoring are the benthic macroinvertebrates owing to their ease in sampl-
ing, low mobility and ubiquitous distributions. The high mobility of fish
not only increases the difficulty of collecting reproducible samples, but
often enables the fish to avoid temporary perturbations, reducing their
effectiveness as monitors of short-term events. Benthic organisms, on the
other hand, are relatively stationary and have been demonstrated to respond
in a measureable way to even very slight and periodic pollutant “leakages”
to the environment. 5 Phytoplankton and aquatic vascular plants are of minor
significance in streams of the oil shale area where periphyton are the
primary producers.
MACROINVERTEBRATE SAMPLING TECHNIQUES
Although the accurate measurement of standing crop and absolute
community composition of stream macroinvertebrates may be desirable, it has
seldom been achieved in aquatic investigations. In fact, it has been
demonstrated that conventional sampling techniques do not accurately measure
these parameters. 6 Biological water quality investigations, however, are
primarily comparative in nature, measuring spatial and temporal changes in
community composition and structure. Therefore, the reduction in variabil-
ity of estimates is of primary significance. Although it is not generally
valid to compare standing crop and community composition estimates resulting
from different types of collection techniques, it is meaningful to compare
the variabilities associated with these techniques, and thus, their relative
potential for collecting reproducible data sets. 7
Probably the most widely used conventional stream macroinvertebrate
sampling device is the Surber square-foot sampler. High sample-variability
and species-selectivity has been associated with this method. 8 ’ 9 An
improved modification of the Surber sampler is the enclosed box sampler
which prevents loss of organisms due to backwash.’° Both of these samplers,
however, are restricted to use in riffle areas with water depths of under 30
510
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centimeters.” Furthermore, they have the disadvantage of collecting from
small areas of substrate (0.1 square meter); therefore, they require large
numbers of replicate samples to adequately characterize communities which
exhibit sparse or patchy distributions. Artificial substrates such as the
multiple plate and basket samplers nave been effectively used in large
streams to reduce sample variability Dy more accurately defining the actual
area of habitat sampled.’ 2 ’’ 3 However, these devices are of limited utility
in streams of the arid and semiarid regions which are characterized by
highly irregular flow patterns and heavy sediment loads. Our experience
with both types of artificial substrates in these waters has been very
discouraging. During high flow periods, the samplers are often swept away,
badly clogged with debris or even buried by sediments. As the water level
recedes, suspended samplers are often left exposed above the water line. In
addition, artificial substrates are highly susceptible to vandalism.
TESTS OF MACROINVERTEBRATE MONITORING TECHNIQUES
In an effort to assess the utility and limitations of the various
macroinvertebrate sampling methodologies, considerable testing was conducted
in the lower arid intermediate reaches of the White River during 1975-Th in a
wide range of habitats and river conditions. These studies have been
described in several technical reports.’ 4 ’’ 5 ’’ 6 Some of the more signifi-
cant results are discussed below.
SAMPLING STATIONS
Five stations in the vicinity of the U-a/U-b federally leased oil-shale
tracts were sampled during 1975-76 (Figure 1). This portion of the river
flows through arid, sparsely vegetated country and is characertized by
irregular flow patterns, highly turbid waters, and unstable bottom sub-
strates. As a result of these conditions, the bottom fauna is often very
sparse, which greatly limits the usefulness of conventional, small-area
sampling methods.
Collections were also taken during 1977 and 1978 at three stations in
the Colorado portion of the river. Two stations were located in the
vicinity of the Piceance Creek confluence and the third station was located
12 kilometers upstream from Meeker (Figure 1). The bottom substrates at
these stations were more stable than those at the Utah stations and support-
ed much greater densities of macroinvertebrates. Under such conditions,
small-area samplers collected greater numbers of organisms, and thereby
provided more information and more reliable data sets than in the unstable
substrate near the U-a/U-b tracts.
TECHNIQUES EVALUATED
A standard Surber sampler, as supplied by Wildlife Supply Company, was
used to collect samples from the White River, Utah, during fall, 1975. For
subsequent sampling, the original net (68-cm long with 10 strands/cm) was
replaced with a net 90-cm long with 12 strands/cm (30 mesh). It was assumed
511
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Ln
H-j
ro
Fedoral
Oil Shale
Lease Tracts
Federal Oil Shale
iea»e Tracts
For* ^
Miles
Kilometers
Black Sulfur Creek
15 25
Utah
Colorado
Figure 1. Map of the study area showing location of the White River sampling stations and oil shale tracts,
-------
that the longer net would reduce backwash and the finer netting allow
entrapment of the smaller macroinvertebrates.
Collections with an enclosed Portable Invertebrate Box Sampler (PIBS),
supplied by Ellis-Rutter Associates, were included during the 1977 and 1978
investigations. This enclosed-box sampler collects from 0.1 square meter
(m 2 ) of bottom substrate and was supplied with a 76-cm-long, 30-mesh net.
A Standardized Traveling Kick Method (STKM) which consists of holding a
net at arms length and traveling slowly downstream while vigorously kicking
the substrate was also utilized. All kick samples were standardized in
terms of length of time the net was held in the water and the distance of
downstream travel.
One-minute traveling kick samples were collected during spring 1976,
with a round, conical-shaped, 50-cm-long, coarse-mesh dip net with a 25-cm
mouth opening. All other traveling kick samples were collected with a
triangular, 76-cm-long nylon dip net. Forty-mesh netting was used for the
fall 1976 collections and 30 mesh netting was used for the 1977 and 1978
collections. The triangular kick nets had a mouth opening of 28 cm by 28 cm
by 24 cm. The 30-second kick samples were collected from areas of approxi-
mately 314 by 4 meters (3 rn 2 ), while the one-minute collections were taken
from areas approximately 6 m 2 .
Two types of multiple-plate samplers were used in the study. During
the fall of 1975, multiple-plate samplers used consisted of nine hardboard
plates, evenly spaced 0.4 cm apart to provide a total surface area of 0.11
m 2 . A 45-cm all-thread bolt, inserted through the center of the plates and
spacers, held the assembly together. The multiple plate samplers used in
1976 were constructed as described in the U.S. EPA Biological Field and
Laboratory Methods Manual.’ 7 These samplers, which consisted of 14, 7.5 cm
diameter unevenly spaced circular plates, provided a total surface area of
0.12 m 2 . Both types of samplers were secured in the stream by driving them
into the substrate and were retrieved 4 to 6 weeks after placement.
Basket samplers utilized were cylindrical, chrome-plated, wire barbeque
baskets, 17 cm in diameter and 26 cm in length. These were filled with
cleaned rocks from the stream bank and placed on rocky areas of the stream
bottom. Baskets remained in the stream 4, 6 or 8 weeks before retrieval.
Although both 40- and 30-mesh nets were used to collect STKM samples
during 1976, all samples were washed ill a 30-mesh sieve to obtain consistent
minimum organism size from sample to sample.
COMPARISON OF TECHNIQUES
Various combinations of techniques were tested in both the Utah and
Colorado reaches of the White River to investigate their relative perform-
ance in fauna-poor and fauna-rich areas. All sampling was confined to
riffle areas of the stream. The number of replicates for each set varied.
In order to facilitate direct comparison between sampling methodologies,
513
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most sample sets were collected by the various methods from sites identical
or adjacent to each other. Each sample set represented a group of replicate
samples specific for a given combination of site, date and method.
Standardized traveling-kick samples generally provided relatively large
numbers of organisms per sample with the least variability of the five
sampling methods tested (Tables 1 and 2). Mean coefficients of variation
for the numbers of organisms collected within sample sets ranged from 42 to
78 for Surber and PIBS samples and 30 to 37 for kick samples. The rela-
tively low variability between samples within kick-sample sets was most
evident for those collected from the fauna-poor lower-river stations (Utah),
where the mean coefficients of variation ranged from 36 to 37 for the STKM
sample sets and 68 to 78 for the Surber sample sets. Multiple-plate and
basket samplers yielded intermediate sample-to-sample variability (Table 2).
For purposes of community composition estimates, kick samples generally
collected higher numbers of taxa per sample with lower between-sample vari-
ability than the other sampling methods (Tables 1 and 2). In addition, the
traveling kick samples yielded the lowest between-sample variability for
Shannon-Weiner diversity values (Table 2).
Of the four methods tested in Utah for similarity in species composi-
tion, the samples obtained with the kick and Surber method most nearly
approximated one another. However, this was not the case with calculated
diversity indices. Close comparisons of the species composition of these
two methods reveal that the kick method selected more heavily for the loose-
ly attached baetid mayflies, while the Surber sampler selected more heavily
for the closely attached black flies. However, most species collected by
Surber samples were also collected by kick samples, and, in fact, individual
kick samples collected, on the average, 3 to 5 more taxa than did Surber
samples (Table 1). Relative selectivities of methods were also demonstrated
in the Colorado collections; the Surber and PIBS methods generally yielded
higher proportions of the closely attached organisms than did the kick
method.
The primary purpose of community analyses in water-quality investiga-
tions is the detection and quantification of changes in the biological
components of the aquatic ecosystem resulting from physical and chemical
alterations in the aquatic environment. The STKM was observed to be con-
siderably more efficient for sampling the unstable, fauna-poor substrates of
the lower White River, Utah, than the other methods tested. In addition,
the kick method compared favorably with the conventional small-area Surber
and PIBS methods in the fauna-rich riffles of the upper White River,
Colorado. Thus, although the STKM shows its greatest usefulness in the
downstream reaches of the river, its favorable performance in the upper
reaches enables direct comparisons between fauna-rich and fauna-poor sta-
tions. The kick method is also applicable across a broader range of stream
depths than the Surber method. This extended flexibility of the STKM makes
it the method of choice particularly during high flow periods, when main-
stream shallow riffles are unavailable for Surber or PIBS sampling.
•5 14
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TABLE 1. TOTAL NUMBER OF SAMPLES (n), MEAN NUMBER OF ORGANISMS (XA), MEAN NUMBER OF TAXA ( T),
AND MEAN SHANNON DIVERSITY INDEX ( H) FOR FIVE MACROINVERTEBRATE SAMPLING METHODS
Surber P185 Traveling
River
Season-Year n A ‘T H n XA ‘r H n
Kick
Multiple Plate
Basket
rr H n
A r
YH
n A Y’r
H
white River, Utah,
Fall, 1975 80 28 6 1.8
21
27 7
2.0
3 112 10
2.3
White River, Utah,
Spring, 1976 48 114 8 1.8 34* 221
11 2.0 16
155 9
1.8
4 716 15
1.8
White River, Utah.
Fall, 1976 95 104 10 2.8 135 276
15 2.8 12
29 9
2.6
11 121 13
2.9
White River, Colorado,
Fall, 1977 10 584 21 2.7 10 1074
26 2.9
White River, Colorado,
Spring, 1978 5 654 15 2.0 15 750 18 2.5 15 1161
18 2.5
TABLE 2. NUMBER OF SAMPLE SETS (n), NUMBER OF REPLICATES
COEFFICIENT OF VARIATION OF THE SAMPLE SETS FOR
OF TAXA (T), AND SHANNON DIVERSITY INDEX (H)
PER SAMPLE
NUMBER OF
SET (r),
ORGANISMS
AND
(A),
THE MEAN
NUMBER
Surber Traveling
River
Season-Year n r A T H n r A I H n r A
Kick
Multiple Plate
Basket
T H n
r A I
H
n r A
J H
White River, Utah,
Fall, 1975 8 10 68 39 34
4
4-6 51 32
35
White River, Utah
Spring, 1976 6 8 68 25 14 4* 4-10 36
19 13 4
4 52 17
17
White River, Utah,
FoIl, 1976 7 10-15 78 35 26 9 10—15 37
17 8 1
12 56 44
31
1 6 48 27 15
White River, Colorado,
Fall, 1977 2 5 47 13 4 2 5 30
13 4 --
— —
White River, Colorado,
Spring, 1978 1 5 42 15 16 3 5 45 19 15 3 5 32
14 6 --
01
01
*60_second traveling kick samples were collected in spring, 1976, and 30-second traveling kick
samples were collected on all other dates.
-------
Results from this study indicate that the standardized traveling-kick
method, when used as described, is the most efficient technique for sampling
stream benthos in semi-arid regions such as the western oil shale area.
This method is particularly effective where and when faunal patchiness and
paucity render more conventional small-area sampling methods impractical.
REFERENCES
1. Westman, W.E. Problems in Implementing U.S. Water Quality Goals.
Amer. Sci. 65:197-203, 1977.
2. Tonolli, L. Ecological Variables and their Effect on Aquatic Fauna.
In: Principles and Methods for Determining Ecological Criteria on
Hydrobiocenoses. R. Amavis and J. Smeets (Eds.). Pergamon Press, New
York, 1976. p. 83-123.
3. Hilsenoff, W.L. Use of Arthropods to Evaluate Water Quality of
Streams. Technical Bulletin No. 100, Department of Natural Resources,
Madison, WI, 1977, p. 15.
4. Horning, W.B. Research Related to Biological Evaluation of Complex
Wastes. In: Biological Monitoring of Water and Effluent Quality. J.
Cairns and K.L. Dickson (Eds.). American Society for Testing and
Materials, Philadelphia, PA 1976. p. 191-199.
5. Hynes, H.B.N. The Biology of Polluted Waters. Liverpool University
Press, Liverpool, England. 1960. p. 202.
6. Hynes, H.B.N. The Ecology of Running Waters. Liverpool University
Press, Liverpool England. 1970. p. 855.
7. Beak, T.W. , T.C. Griffing and A.G. Appleby. Use of Artificial
Substrate Samplers to Assess Water Pollution. In: Biological Methods
for the Assessment of Water Quality. 3. Cairns and K.L. Dickson
(Eds.). American Society for Testing and Materials, Philadelphia, PA
p. 227-241, 1973.
8. Chutter, RM. A Reappraisal of Needham and Usinger s Data on tne
Variability of a Stream Fauna when Sampled with a Surber Sampler.
Limnol. Oceanogr. 17:139-141, 1972.
9. Kroeger, L. Underestimation of Standing Crop by the Surber Sampler.
Limnol. Oceanogr. 17:475-479, 1972.
10. Jacobi, G.Z. An Inexpensive Circ:ular Sampler for Collecting Benthic
Macroinvertebrates in Streams. Arch. Hydrobiol. 83:126-131, 1978.
11. Frost, S., A. Huni and W.E. Kershaw. Evaluation of a Kicking Technique
for Sampling Stream Bottom Fauna. Can. J. Zool. 49:167-173, 1971.
516
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12. Mason, W.T., C.I. Weber, P.A. Lewis and E.C. Julian. Factors Affecting
the Performance of Basket and Multiplate Macroinvertebrate Samplers.
Fresh Wat. Biol. 3:409-436, 1967.
13. Crossman, J.S. and J. Cairns. A Comparative Study Between Two
Different Artificial Substrate Samplers and Regular Sampling Tech-
niques. Hydrobiologia. 44:517-522, 1974.
14. Hornig, C.E. and J.E. Pollard. Macroinvertebrate Sampling Techniques
Applicable to Streams of Semi-Arid Regions. Environmental Monitoring
Series. EPA-600/4-78-040. U.S. Environmental Protection Agency, Las
Vegas, NV, 1978. 21 p.
15. Kinney, W.L., J.E. Pollard and C.E. Hornig. Comparison of Macro-
invertebrate Samplers as they Apply to Streams of Semi-arid Regions.
In: Conference Proceedings of the 4th Joint Conference on Sensing of
Environmental Pollutants, New Orleans, La. Nov. 6-11, 1977, Amer.
Chem. Soc. Wash. DC 1978. p. 515-518.
16. Pollard, J.E. and W.L. Kinney. Assessment of Macroinvertebrate Môni-
toring Techniques in an Energy Development Area. U.S. Environmental
Protection Agency, ORD, EMSL-LV, In Press.
17. U.S. Environmental Protection Agency. Biological Field and Laboratory
Methods for Measuring the Quality of Surface Waters and Effluents.
Environmental Monitoring Series. EPA-670/4-73-OO1. U.S. Environmental
Protection Agency. Cincinnati, OH, 1973. p. 176.
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THE BIOLOGY OF A PLAINS STREAM, SALT WELLS CREEK,
IN AN OIL SHALE AREA, SOUTHWESTERN WYOMING
Morris J. Engelke, Jr.
Hydrologist
U.S. Geological Survey
P.O. Box 1125
Cheyenne, Wyoming 82001
Salt Wells Creek typifies plains streams draining extensive oil shale
areas of southwestern Wyoming. The stream is intermittent but has several
small tributaries in its headwaters that are perennial due to springs.
Springs and perennial reaches support an abundant aquatic community, includ-
g several species of small fish. Aquatic organisms found in downstream
intermittent reaches are generally washed in from upstream. Some inverte-
brates survive dry periods by burrowing into the streambed.
Each of the three stream environments--ponds, springs, and perennial
reaches--contains distinct invertebrate communities. Green and bluegreen
algae are dominant during high streamf low. Diatoms are dominant during low
streamfiow. Seasonal succession of community development occurs in pen-
phyton and benthic invertebrates. Amphipods and caddisflies are the princi-
pal benthic invertebrates. Aquatic organisms in plains streams survive
through periods of relatively high temperature and high concentration of
suspended sediment and dissolved solids.
(Paper presented at Symposium but not submitted for publication in the
Proceedings. For more information, contact the author.)
518
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AQUATIC TOXICITY TEST5 ON INORGANIC ELEMENTS
OCCURRING TN OIL SHALE
Wesley J. Birge, Jeffrey A. Black,
Albert G. Westerman, and Jarvis E. Hudson
T.H. Morgan School cf Biological Sciences
University of Kentucky
Lexington, Kentucky 40506
ABRACT
Using the rainbow trout ( Salmo irdneri), embryo-larval toxicity tests
were performed on 33 elements which occur in oil shale and other fossil
fuels. Continuous exposure was maintained from fertilization through 4 days
posthatching, employing static renewal procedures and test responses were
based on lethality and teratogenesis. The LC 50 s were under 1.0 mg/i for 19
of the 33 elements, indicating high sensitivity of developmental stages of
the rainbow trout to a wide range of elements which occur in oil shale,
spent shale, and process waters. ElEments which proved most toxic to trout
eggs and larvae were Hg, Ag, La, Ge, Ni, Cu, and Cd, with probit—derived
LC 50 s of 0.005, 0.01, 0.02, 0.05, 0.05, 0.11, and 0.14 mg/i, respectively.
Exposure levels which produced 1% control-adjusted impairment of test
populations (LC 1 ) were also determined by log probit analysis, to provide a
basis for estimating threshold concentrations. The LC 1 values were at or
under 10 pg/i for 12 elements, including Ag, Be, Cd, Cu, Ge, Hg, La, Ni, Pb,
Ti, V, and Zr. To determine reliabi ity of the LC 1 values, they were com-
pared with maximum acceptable toxicarit concentrations developed in contin-
uous flow embryo-larval and chronic reproductive studies and with current
freshwater criteria. Good correlaticns generally were obtained where data
were adequate to permit comparisons. Results showed that static renewal
tests with trout embryo-larval stages afforded a reliable and economical
means of screening oil shale contaminants for toxic properties, identifying
those of greatest concern to aquatic ecosystems, and estimating concentra-
tions which may produce hazardous effects. To assist further in prioritiz-
ing elements for studies on environmental monitoring and biological effects,
oil shale, spent shale, and retort waters were compared for elemental compo-
sition.
Trout embryo-larval tests also were conducted on simple metal mixtures,
to evaluate possible antagonistic, additive, or synergistic interactions.
Mercury was mixed in equal proportions with each of three other metals,
including cadmium, copper, and selenium. Analysis of dose-response data
clearly indicated that the type of interaction varied with concentration.
At lower exposure levels, copper-mercury was antagonistic, and the other
-------
iixtures were . dditive to antagonistic. All mixtures became synergistic at
or above median lethal concentrations. As synergism was dependent on high
exposure levels, this interaction appeared less likely to be significant
under ambient conditions.
INTRODUCTION
Upwards of 65 elements reportedly occur in oil shale, and spent shale
and retort waters contain significant concentrations of many elements con-
sidered hazardous to aquatic biota.’ 2 Environmental outfall of inorganic
contaminants may approach or equal that observed for coal. Using data from
recent investigations,’ 18 the composition of oil shale, coal and their
waste products was compared for 33 of the elements which may prove detri-
mental to aquatic life (Table 1). Utilization of oil shale will yield a
high ratio of waste products, averaging about 91 tons of spent shale and 400
to 3,000 liters of retort water for every 100 tons of shale processed. 4
Aqueous leaching of solid wastes may constitute the principal threat to
surface and groundwaters, particularly as retort and other wastewaters may
be used in wetting down spent shale. 4 Fallout from atmospheric discharges
also may reach aquatic ecosystems, including waters affected by local depo-
sition flux, atmospheric scavenging, and terrestrial runoff. 5 ’ 19 ’ 2 ° The
Green River ofl shale formation covers approximately 17,000 square miles in
Colorado, Utah, and Wyoming, and reserves have been calculated at 600 bil-
lion barrels of oil, considering only that shale estimated to yield a mini-
mum of 25 gallons of oil per ton. 6 Due to the potential magnitude of this
new energy technology and the expansive geographic regions which will be
affected, it is essential to establish waste disposal guidelines which will
ensure environmental acceptability.
However, definitive freshwater criteria have been slow to develop and
currently exist for only a small fraction of the elements found in oil
shale. If an adequate data base for hazard assessment is to be achieved
within the time frame contemplated for the implementation of oil shale
technology, it is essential to develcp a more rapid and economical means of
delineating guidelines for waste disposal. McKim 21 recently reviewed data
for a wide rar!ge of aquatic toxicants and concluded that continuous flow
fish embryo-larval tests which extended beyond hatching by 30 days or more
yielded responses comparable to those produced in chronic life-cycle
studies. As suggested by McKim, this affords a somewhat more economical
procedure for estimating maximum acceptable toxicant concentrations (MATC).
In addition, continuous flow embryo-larval tests of even shorter duration
have produced sensitivity equal to that observed with chronic testing
procedures. 22 24 However, considering the many aquatic contaminants which
may result from new energy technologies, still simpler and more rapid
screening procedures are required. 25 In this investigation, static renewal
toxicity tests with embryos and larvae of the rainbow trout were used (1) to
compare toxicity of 33 elements which occur in oil shale, (2) to identify
particularly hazardous elements for more comprehensive study, and (3) to
provide a basis for estimating initial freshwater guidelines in instances
where established criteria are lacking.
2O
-------
Table 1. CONCENTRATIONS (ppm) OF TOXIC ELEMENTS OCCURRING IN OIL SHALE, COAL, AND THEIR WASTE PRODUCTS
4300 - 30400
0.2 - 8.9
0.5 - 106
33 - 750
0.2 - 31
1.2 - 356
0.1 - 65
0.49 - 1.5
0.3 - 610
0.5 - 43
1.8 - 185
1.1 - 61
1.0 - 819
3.3 - 98
4 - 218
3.1 - 25
100 - 2500
6 - 181
0.01 - 1.6
1.0 - 73
0.4 - 104
0.4 - 7.7
0.03 - 0.19
10.0 - 37
0.25
0.5
0.29 - 2.0
1.0 - 51
20 - 3200
0.04 - 3.0
10 - 1281
15 - 5600
8 - 133
1.4 - 7.2 0.007 - 3
- 0.007 - 0.5
0.005 - 0.038 0.001 - 1
0.1 - 0.3 0.02 - 0.1
<0.01 - 0.01 <0.02 - 0.02
- 0.4-82
0.001 - 0.037 0.01
- <0.005 - 0.01
0.004 - 0.067 0.004 - 0.6
- 0.002 - 0.5
0.01 - 0.31 0.002 - 5.0
- <0.005
- 0.001 - 0.01
- 0.05 - 0.46
0.01 - 0.06 0.002 - 1.0
- 0.001 - 0.020
0.4 - 14 0.32 - 50.0
0.01 - 0.58 0.01 - 15.0
0.0002 - 0.038 0.007 - 0.030
- 0.001 - 0.5
0.05 - 1.1 0.001 - 10.0
0.002 - 0.065 0.002 - 0.3
<0.01 - 0.01 <0.02
- 0.015 - 0.120
- O]. - 0.3
- <0.003
- <0.003
- <0.02 - 0.1
- 0.003 - 0.3
- 0.03 - 0.07
- 0.001 - 0.033
0.03 - 1.51 0.007 - 5.0
- 0.02 - 0.1
Element Oil Shale Spent
Oil Shale
Oil Shale Coal Ash Pond
Retort Water Effluent
Coal Conversion
Process Waters
Aluminum
5000 -
>10000
-
0.11 - 0.66
Antimony
0.20 -
11
1
0.004 - 0.036
Arsenic
2.6 -
108
15
4.6 - 10
Barium
32 -
750
180
0.002 0.22
Beryllium
0.26 -
35
0.3
Boron
12 -
140
53
4.4 8.8
Cadmium
0.02 -
1.4
O.8
Cesium
0.06 -
11
7
0.002 - 0.007
Chromium
21 -
1000
125
0.011 - 0.037
Cobalt
0.78 -
39
11
0.002 0.65
Copper
15 -
120
48
0.007 0.16
Gallium
1.1 -
18
14
Germanium
0.37 -
2.9
0.65
0.001 - 0.007
Lanthanum
1.1 -
50
20
0.002 - 0.010
‘—
Lead
Lithium
1.0 -
1.9 -
70
850
19
160
0.062 - 0.37
0.004 - 0.75
Magnesium
5000 -
>10000
-
5.3 - 8.7
Manganese
9 -
390
405
0.042 - 0.14
Mercury
0.2 -
1.4
-
<0.1 - 0.1
Molybdenum
4.9 -
87
. 6
0.056 - 0.340
Nickel
28 -
760
15
0.37 - 2.6
Selenium
0.08 -
5.2
1
0.003 - 0.98
Silver
0.04 -
1.7
1.7
0.002 - 0.230
Strontium
59 -
2700
460
0.003 - 0.48
Tantalum
0.04 -
4.8
<0.2
-
Tellurium
0.11 -
0.35
<0.2
0.001
Thallium
0.3 -
1.4
<0.2
Tin
0.11 -
11
1.5
8.9 - 100
Titanium
150 -
2600
-
0.64 - 21
Tungsten
0.03 -
2.9
1.25
0.003 - 0.024
Vanadium
10 -
280
135
0.004 - 11
Zinc
12 -
136
35
0.26 - 0.47
Zirconium
3.0 -
60
110
0.008 - 0.390
-------
MATERIALS AND METHODS
Embryo-larval toxicity tests were performed with the rainbow trout
( Salmo gairdneri), using static renewal procedures previously described by
Birge et al. 26 Test water and tox cant were changed at regular 12-hour
intervals. Treatment was maintained continuously from fertilization through
4 days posthatchinq, giving an exposure period of 28 days. Water hardness
ranged from 92 to 110 mg/i CaCO 3 , and pH varied from 6.9 to 7.8. Moderate
aeration was used to maintain dissolved oxygen within a range of 9.3 to 10.1
mg/i. A minimum of 7 mg/l has been recommended for trout and salmon spawn-
ing waters. 27 Other physicochemical characteristics of the test water have
been described by Birge et al. 22 AU tests were conducted in environmental
rooms and temperature was maintained at 12° to 13°C. Test populations were
examined each day to tabulate frequencies of lethality and teratogenesis.
Log probit analysis was used to determine control-adjusted LC 1 , LC , 0 , and
LC 50 values with 95% confidence limits. The lethal concentrations were
determined with the method of Finney, 28 rather than the procedure of Daum 29
used in earlier investigations. 3 ’ 26 Teratic survivors, as described by
Birge and Black, 3 ° were counted as lethals in probit calculations. Minimum
sample size was set at 100 eggs, using 500-ml exposure chambers.
Elements and compounds selected for testing are given in Table 2.
Hydrated salts were used for Al, Ba, Cd, Co, Cu, Fe, Mg, Mn, Mo, Ni, Sr, and
Te. Concentrations of elements contained in prepared test solutions added
to exposure chambers were confirmed with a Perkin-Elmer atomic absorption
spectrophotometer (Model 503), equipped with an HGA 2100 graphite furnace
and a mercury analyzer. 3 ’ 3 ’ Test water was monitored for temperature,
dissolved oxygen, water hardness, and pH, using a YSI telethermometer with
thermocouple, YSI oxygen meter (Model 51A), Orion divalent cation electrode,
and a Corning digital pH meter (Model 110).
RESULTS AND CONCLUSIONS
Tests conducted on embryonic and larval stages of the rainbow trout are
summarized in Table 2. Median lethal concentrations (LC 50 ) and other values
(IC 1 0 , LC 1 ) were based on control-adjusted responses (lethality, teratogene-
sis) incurred during the 28-day expsoure period. Control populations,
maintained simultaneously with experimentals, survived at frequencies rang-
ing from 83% to 96%. The LC 50 s were under 1.0 mg/i for 19 of the 33 ele-
ments, indicating high sensitivity of developmental stages of the rainbow
trout to a wide range of elements which occur in oil shale and coal.
Mercury, silver, and lanthanum were the most toxic. Of the remaining 14
elements, LC 50 s ranged from 1.1 to 7.3 mg/i for Zr, Zn, Mn, Ga, Ta, Se, and
Ti, and those elements which exhibited the lowest toxicity, based on median
lethal concentrations, included B, Ba, Cs, Li, Mg, Te, and W. The high
sensitivity of rainbow trout embryos and alevins has been noted in numerous
previous investigations. 26 ’ 32 34 In particular, McKim et al. 34 observed
life-cycle stages of the rainbow trout to be more susceptible to copper than
were those of seven other fish species. Considering the LC 50 s given in
Table 2, a number of toxic elements occur in oil shale, retort waters, and
-------
Table 2. TROUT EMBRYO-LARVAL BIOASSAYS ON ELEMENTS OCCURRING IN OIL SHALE
Test
Element
LCSO
(mg/i)
95% Confidence
Limits
LC 10 95% Confidence
(pg/i) Limits
LC 1
(pg/i)
95% Confidence
Limits
Mercury (HgC1 ) 0.005 0.004 - 0.005 0.9 0.7 - 1.2 0.2 0.1 - 0.3
Silver (A9NO3) 0.010 0.008 - 0.011 0.9 0.7 - 1.2 0.1 0.1 - 0.2
Lanthanum (LaC1 3 ) 0.02 0.01 - 0.03 3.7 2.3 - 5.2 0.9 0.4 - 1.5
Germanium (GeO2) 0.05 0.04 - 0.05 2.8 1.9 - 3.8 0.3 0.2 - 0.5
Nickel (N1C12) 0.05 0.04 - 0.06 10.6 7.4 - 13.9 3.0 1.7 - 4.5
Copper (CuSO4) 0.11 0.09 - 0.14 16.5 10.1 - 23.7 3.4 1.6 - 5.9
Cadmium (CdC12) 0.14 0.13 - 0.16 29.2 22.8 - 36.1 8.0 5.4 - 10.9
Vanadium (V2O5) 0.17 0.14 - 0.21 33.8 22.0 - 46.8 9.0 4.7 - 14.6
Thallium (T1C13) 0.18 0.14 - 0.22 36.3 24.0 - 50.0 9.9 5.2 - 15.8
Chromium (Cr03) 0.19 0.15 - 0.23 56.9 34.6 - 80.2 21.5 10.3 - 35.2
Lead (PbC12) 0.22 0.19 - 0.25 40.9 31.1 - 51.4 10.3 6.9 - 14.6
Strontium (SrC12) 0.25 0.20 - 0.30 49.0 32.0 - 67.7 13.0 6.7 - 21.2
Beryllium (BeCl2) 0.38 0.26 - 0.53 42.0 19.8 - 72.1 7.0 2.2 - 15.5
Tin (SnC12) 0.42 0.35 - 0.50 75.5 53.4 - 99.9 18.6 10.9 - 283
Cobalt (Co(NO3)2) 0.49 0.38 - 0.59 120 64.4 - 176 38.2 14.1 - 69.6
w Arsenic (NaAsO2) 0.55 0.49 - 0.61 134 104 - 164 42.1 28.6 - 57.4
Aluminum (AlCl 3 ) 0.56 0.51 - 0.61 369 301 - 420 260 190 - 315
Antimony (SbCl3) 0.66 0.53 - 0.79 157 101 - 216 48.9 24.8 — 79.2
Molybdenum (Na2MoO 4 ) 0.79 0.61 - 0.99 125 76.5 - 183 27.8 13.5 - 48.3
Zirconium (ZrCl4) 1.08 0.70 - 1.57 79.0 32.2 - 150 10.3 2.4 - 24.1
Zinc (ZnC12) 1.12 1.00 - 1.24 451 366 — 533 216 157 — 275
Manganese (MnC12) 2.91 2.60 - 3.23 958 779 - 1134 388 ?80 - 501
Gallium (GaCi 3 ) 3.51 2.47 - 4.73 316 156 - 540 44.5 15.6 - 96.7
Tantalum (KTaO 3 ) 4.33 3.08 - 5.84 525 260 - 869 94.0 32.0 - 198
Selenium (Na2SeO4) 5.17 4.15 - 6.26 786 483 - 1137 169 79.9 - 296
Titanium (TIC14) 7.31 5.33 - 9.51 981 513 - 1578 191 72.7 381
Lithium (LiC1) 9.28 6.68 - 12.3 1783 901 - 2826 464 163 - 916
Tungsten (Na2WO4) 16.5 14.0 - 19.4 3651 2609 - 4748 1066 629 - 1591
Tellurium (K2TeO3) 21.6 14.5 - 30.6 1263 523 — 2377 125 31.5 - 327
Barium (BaC12) 42.7 32.2 - 54.2 9543 5566 - 14097 2813 1267 - 4924
Boron (H 3 B03) 70.1 37.0 - 184 1016 156 - 2067 31.6 0.8 - 191
Cesium (C5C1) 181 133 - 235 21826 9807 - 37054 3887 1092 - 8842
Magnesium (MgCl ) 1355 1199 - 1507 660500 517600 - 788000 367600 254800 - 475800
-------
solid wastes at concentrations sufficient to pose appreciable risk to trout
and other aquatic biota (Table 1).
Particular attention was given to exposure levels which produced 10%
(LC 10 ) and 1% (LC 1 ) impairment of test populations, to evaluate use of such
probit-derived values for (1) approximating threshold concentrations, and
(2) application in initial hazard assessment programs. To determine relia-
bility of the LC 1 values, they were compared with MATCs or no effect concen-
trations developed in continuous flow embryo-larval and chronic life-cycle
tests, as well as with current freshwater criteria. 35 The LC 1 of 0.2 pg/i
mercury was in close agreement with MATCs determined in chronic studies with
the fathead minnow 21 (0.07-0.13 pg/i), flagfish 2 ’ (0.17-0.33 pg/i), and
brook trout 36 (0.29-0.93 pg/i), and with the freshwater criterion of 0.05
pg/i. 35 However, in continuous flow embryo-larval and chronic reproductive
tests with the rainbow trout, developmental stages suffered lethality at 0.1
pg/i mercury. 22 ’ 32 Though data on silver were limited, the LC, of 0.1 pg/i
agreed closely with a long term no effect concentration set between 0.09 and
0.17 pg/l in an 18-month study with the rainbow trout. 37 The copper LC 1 of
3.4 pg/i was close to estimated MATC ranges of 3.0 to 5.0 and 5.0 to 8.0
pg/i determinea for the brook trout by Sauter et al. , and just below the
no effect concentration of 9.4 pg/i given by McKim and Benoit. 39
The cadmium LC, of 8.0 pg/i was in agreement with estimated MATCs for
eight species of fish, 2 ’ including the range of 3.8 to 11.7 pg/i determined
for brown trout. 40 An MATC of 1.7 to 3.4 pg/i was established for chroni-
cally exposed brook trout, 4 ’ and present EPA criteria for salmonids were set
at 0.4 and 1.2 pg/i for cadmium in soft and hard water, respectively. 35 The
chromium LC 1 was 21.5 pg/l, compared to an estimated MATC of 51 to 105 pg/i
established ‘in a 60-day test with embryonic, larval, and juvenile stages of
the rainbow trc’ut. 38 In a complete life-cycle study with the brook trout,
the MATC range for chromium was 200 to 350 pg/i, 42 and the EPA criterion for
aquatic life was set at 100 pg/i. 35 In life-cycle studies with Daphnie
magna , Biesinger and Christensen 43 reported 16% reproductive impairment at a
chromium concentration of 330 pg/l, while Trabaika and Gehrs 44 observed
significant effects on survival and reproduction at exposure levels as low
as 10 pg/i.
In studies with lead, MATC ranges of 31.3 to 62.5 and 58 to 119 pg/i
were determined in chronic reproductive tests on the flagfish 2 ’ and brook
trout, 45 respectively. An MATC for rainbow trout was estimated to fall
between 71 and 146 pg/i in 60-day tests on developmental and juvenile
stages. 38 However, the toxicity of lead may vary substantially depending on
water hardness and other test conditions. 35 ’ 46 In chronic studies with the
rainbow trout, 46 MATCs for total lead administered ‘in soft water were within
ranges of 4.1 to 7.6 pg/i and 7.2 to 14.6 pg/i, depending on whether
exposure was initiated at the eyed stage or after hatching. The most sensi-
tive test responses included discoloration of the tail and abnormalities of
the spinal column (i.e. , lordosis, scoliosis). These MATCs closely approxi-
mated the LC 1 of 10.3 pg/i given in [ able 2. It is important to note that
the latter value was determined by combining frequencies for embryo-larval
lethality and teratogenesis, basing exposure on total lead administered ir
,noderately hard water.
524
-------
No chronic data were available for beryllium, but the LC 1 did not
differ significantly from the EPA criterion of 11 pg/i established for
aquatic life exposed in soft water. 35 The LC 1 for cobalt was 38.2 pg/i, and
this was in reasonable agreement with an MATC of 48.7 to 112.5 pg/i, which
we estimated from results on growth and survival obtained in 30-day tests
with embryos and larvae of the fathead minnow. 47 In the latter investiga-
tion, the bioconcentration of cobalt .‘as si nificant at 48.7 ua/i.
Though a final criterion for arsenic has not been developed, the EPA
recommendation for domestic water supplies (50 pg/i) was considered adequate
to protect aquatic life. 35 The arsenic LC 1 was 42.1 pg/i. Zinc gave an LC 1
of 216 pg/i, compared to MATCs of 30 to 180 and 532 to 1368 pg/i determined
in chronic reproductive studies with the fathead minnow 48 and brook trout, 21
respectively. In addition, the LC 1 was in ciose agreement with the esti-
mated MATC of 139 to 267 pg/i obtained in 30-day tests with the flagfish. 49
The boron LC 1 of 31.6 pg/i was obtained in tests conducted in moderately
hard water (100 mg/i CaCO 3 ) and was approximately midrange between vaiues
reported in coqtinuous flow tests in which trout embryos and iarvae were
exposed in soft. and hard water. 3 ° Ccmpared with a boron LC 50 of 70.1 mg/i,
tile LC 1 was unusually iow. However, this was due in large part to terato-
genic effects of boron observed at low concentrations. 30 The LC 10 of 1016
pg/i further characterized the gradual slope of the dose-response curve
obtained for boron. Though chronic data were not avaiiable for magnesium,
the [ C 50 of 1355 mg/l appeared reasonable in view of 96-hour LC 50 s which
ranged up to 4200 mg/i for adult fish. 5 ° In tests with magnesium, water
hardness was substantiaiiy increased at the higher exposure leveis.
A poor correiation between LC 1 and MATC values was observed for nickel.
Despite the importance of nickel in hazard assessment programs for oil shale
and coai, chronic toxicity tests with this element have been limited to very
few aquatic species. The most comprehensive investigation was conducted on
the fathead minnow by Pickering. 51 Nickel concentrations up to 1.6 mg/i did
not affect survival or growth of the first generation of fish, which were 6
weeks of age at the onset of exposure. Spawning began after approximately 5
months, and both fecundity and egg hatchability were sharply reduced at a
mean nickel concentration of 730 pg/i. The average number of eggs per
spawning was 66 and hatchab -ility was 42%, compared to control values of 188
and 94%, respectively. Though egg production appeared repressed at iower
exposure levels, results could not be verified statisticaily. For exampie,
when nickel was administered at 380, 180, and 82 pg/i, mean egg production
per female for au spawnings was 13% to 31% less than observed for controis.
The maximum acceptabie toxicant concentration for nickei in hard water was
judged to fail between 380 and 730 pq/i, and Pickering 51 predicted an MATC
of 68 to 132 pg/i for fathead minnows exposed in soft water.
In other investigations, Biesinger and Christensen 43 reported 50% and
16% reproductive impairment in Daphnia at nickel concentrations of 95 and 30
pg/i, respectively. While Daphnia and the fathead minnow may differ in
their tolerances to nickel, 35 the wide variation between resuits of
Biesinger and Christensen 43 and Pickering 5 ’ probably resulted in part from
the different statisticai procedures appiied to their data. Biesinger and
525
-------
Christensen obtained concentrations for reproductive impairment using t e
method of Litchfieid and Wilcoxon, 52 which involved fitting a regression
line to dose-response data plotted on logarithmic-probability paper. 53 On
the other hand, Pickering applied analysis of variance to his results. Even
though he used four replicates per treatment level and obtained good pre-
cision in regu ating exposure concentrations of nickel, it was not possible
to show significance for the consistent reductions in fecundity observed at
all exposure levels below 730 pg/i. Time and cost limitations involved in
long term investigations frequently curtail use of sufficient replicate
exposures to provide adequate differentiation of low-level test responses
using the more traditional statistical procedures (e.g., analysis of vari-
ance). Therefore, when the dose-response is adequately characterized,
regression analysis generally provides a more effective means of approximat-
ing threshold concentrations for toxic effects. 24 When data obtained with
trout embryo-larval stages were analyzed using log probit regression, sensi-
tivity to nickel equalled or exceedeo that observed for Daphnia (Table 2).
The [ C 10 and [ C 1 values were 10.6 and 3.0 pg/l. In other static renewal
tests with embryos and larvae, nickel LC 1 s of 3.6, 10.6, and 97.7 pg/i were
obtained for the channel catfish, largemouth bass, and goldfish. 54 It
should be noted that these values, as well as those presneted in Table 2,
were determined with the probit method of Finney, 28 rather than by Daum’s
procedure 29 which was used in previous investigations. This, together with
inclusion of some additional data from replicate experiments, gave lethal
concentrations which differed slightly from preliminary findings. 3 ’ 26 ’ 32
The data correlations reviewed above were complicated somewhat by
differences in test procedures, water conditions, and animal test species.
However, where data were sufficient to permit comparisons, LC 1 s obtained in
static renewal embryo-larval tests with trout were in reasonable agreement
with no effect concentrations and MATCs determined in continuous flow
embryo-larval and chronic life-cycle studies and with most existing EPA
criteria for freshwater biota. 35 Differences between LC 1 s and MATCs for
specific elements generally were no greater than variations among MATCs
reported in different investigations (Table 3). Also as shown in Table 3,
an interesting relationship existed between LC 10 values and metal concen-
trations which produced 16% reproductive impairment in Daphnia magna. 43
Compared with Daphnia on this basis, trout embryo-larval stages were more
sensitive to As, Hg, Mn, Ni, Sn, and Sr, about equally affected by Al, Ba,
Cu, and Pb, and more tolerant to Cd, Co, Mg, and Zn. When the different
elements were compared for relative toxicity, the order varied somewhat
depending on whether LC 50 , LC 10 , or LC 1 values were used (Table 2). The
order of toxicity of metals to chronically exposed Daphnia also varied to
some extent when determined by LC 50 s. Maximum acceptable toxicant concen-
trations given in Table 3 were estimated from 30- to 90-day continuous flow
embryo-larval tests or determined in partial and complete life-cycle
studies, and the values for Daphnia were taken from Biesinger and
Christensen. 43 The EPA Red Book 35 was the source for criteria for fresh-
water aquatic life, as revisions currently in progress were not available
for inclusion in this study.
-------
Table 3. MAIC’S COMPARED WITH LC 1 AND LC 10 VALUES DETERMINED
IN STATIC RENEWAL TESTS WITH TROUT EMBRYO-LARVAL STAGES
Element 1 LC 10 2
(pg/i)
LC 1 2
(pg/i)
MATC 3 Species
(pg/i)
Test 4 Daphnia 5
(pg/i)
Al umi num
Arsenic
Barium
Cadmi um
Chromi urn
Cobalt
Copper
Lead
Magnes i urn
Manganese
Mercury
Nickel
Silver
Strontium
Tin
Zinc
369
134
9543
29.2
56.9
120
16.5
40.9
660500
958
0.9
10.6
0.9
49.0
75.5
451
260
42.1
2813
8.0
21.5
38.2
3.4
10.3
367600
388
0.2
3.0
0.1
13.0
18.6
216
1.7 - 3.4
brook trout 1 1
dc
3.0 - 6.5
3.8 - 11.7
fiagfish 2 ’
brown trout 0
el
el
4.1 - 12.5
coho salrnon 0
el
7.4 — 16.9
8.1 - 16.0
fiagfish 21
flagfish 9
dc
el
51 - 105
rainbow trout 38
el
200 - 350
brook trout 2
dc
3.0 — 5.0
brook trout 38
el
5.0 - 8.0
brook trout 38
el
9.4 - 17.4
brook trout 39
dc
4.1 - 7.6
7.2 - 14.6
31.3 — 62.5
58 - 119
rainbow troutL 6
rainbow trout’ 6
fiagfish 2 ’
brook trout 5
plc
plc
dc
dc
71 - 146
rainbow trout 38
el
0.07 - 0.13
fathead minnow 21
dc
0.17 - 0.33
0.29 - 0.93
flagfish 21
brook trout 36
plc
dc
380 - 730
fathead minnow 51
plc
0.09 - 0.17
rainbow trout 37
plc
30 180
139 - 267
532 — 1368
fathead rninnow’ 8
fiagfish 9
brook trout 21
plc
el
plc
320
520
5800
0.17
330
10
22
30
82000
4100
3.4
30
42000
350
70
‘Administered in static renewal tests from fertilization through 4 days post-
2 hatching.
Determined with the probit method of Finney 28 , rather than the procedure of
3 Daum 29 used in earlier investigations 3 ’ 26 ’ 32 .
4 Additional values were presented by McKim 21 .
MATC’s were estimated from 30 to 90-day embryo-larval tests (el) or deter-
5 mined in partial (plc) and complete (dc) life-cycle studies.
Chronic values for 16% reproductive impairment given by Biesinger and
Chri stensen 3 .
527
-------
Firm criteria for aquatic biota have been developed for only a small
fraction of the elements which occur in process waters and solid wastes
associated with oil shale and coal (Table 1), and energy engineers are faced
with an uncertain future concerning regulatory guidelines for waste
disposal. Consistent with recommendations of the Interagency Workshops on
Oil Shale 55 and Coal Conversion, 25 early identification of potential hazards.
is essential to assure en ’iron nental acceptability of new and rapidly emerg-
ing energy technologies. The promulgation of freshwater criteria has
progressed slowly since implementation of the Water Quality Act of 1965, due
in substantial measure to the strin ent requirements of the present testing
program. Static renewal bioassays evaluated in the present investigation
can be conducted at a small fraction of the time and cost involved in
partial and complete chronic life-c ’c1e tests generally used to establish
MATCs for aquatic life. As rainbow trout are endemic to many waters which
potentially may be affected by the processing of oil shale, the LC 1 s and
LC 10 s given in Table 2 should be useful in estimating impact of contaminants.
on aquatic biota, pending development of regulatory criteria by State and
Federal agencies. It should be noted, however, that toxicity of trace
elements in antural waters may be affected by various transport-fate phenom-
ena, water characteristics (e.g., pH, hardness, suspended solids), or
chemical form and solubility of the contaminant. 35 ’ 56 ’ 57 The comparative
toxicological ranking given in Table 2 should also be useful in prioritizing
trace elements for more comprehensive studies on environmental monitoring
and biological effects. Particular attention should be given to the more
toxic elements which appear at appreciable concentrations in oil shale waste
products (Table 1).
Trout embryo-larval tests also were conducted on simple metal mixtures,
to evaluate possible antagonistic, additive, or synergistic interactions.
Mercury was mixed in equal proportions with each of three other metals, and
the resulting LC 50 s (pg/i) with 95% confidence limits given parenthetically
were 10 (6-18), 10 (9-12), and 18 (12-25) for mercury-cadmium, mercury-
selenium, and mercury-copper, respectively. Given in the same order, LC 50 s
(pg/i) calculated for additive effects were 25 (19-32), 90 (64-131), and 15
(12-20). Except for mercury-copper, the actual LC 50 s reflected net syner-
gism. However, as noted in earlier studies, 22 ’ 26 analysis of dose-response
data clearly indicated that the type of interaction varied with exposure
concentration. The results for mercury-copper are shown in Figure 1.
Antagonism was observed at 1 to 10 pg/i (P <0.005). Throughout this expos-
ure range, the hatchability of trout. eggs consistently exceeded frequencies
calculated for additive effects, but synergism became significantly at 50
pg/i (P <0.001). Based on IC 50 values given in Table 2, mercury was more
than 20 times as toxic to trout eggs as copper. However, the mercury-copper
mixture was less toxic than copper at lower exposure levels, but equally as
toxic as mercury at high concentrations. Below median lethal concentra-
tions, mercury-selenium and mercury-cadmium were moderately antagonistic tc
additive, and synergism was observed only at higher exposure levels. On the
basis of these initial results, it appears that synergism usually is depen-
dent on high exposure concentrations and, therefore, less likely to be a
significant factor in most natural trout waters. This is consistent with
528
-------
earlier results of in situ embryo-larval tests conducted on coal ash efflu-
ents which contained complex metal mixtures. 3
ACKNOWLEDGEMENTS
We are deeply grateful to Barbara A. Ramey for preparation of ihe
m.ic r*f (‘ fl I4 . . . .. .4 r IA_fl_. ..._.I1 .C_...
... ,.., . auI A v I aisi I._. rI ...L .l JIItI I I I 1)1 t ..e(...II!I I I. ..d I
assistance. Research was supported on funds provided by the Kentucky Insti-
tute for Mining and Minerals Research (project 7576-EZ), the National
Science Foundation (RANN, grant no. AEN 74-08768 AOl), and the U.S. Depart-
ment of the Interior, Office of Water Research and Technology (project.
8-044-KY).
REFER ENC ES
1. Poulson, R.E. , J.W. Smith, N.B. Young, W.A. Robb, and T.J. Spedding.
Minor Elements in Oil Shale and Oil Shale Products. Laramie Energy
Research Center, ERDA, LERC/RI-77/1, 1977. p. 16.
2. Hildebrand, S.G. , R.M. Cushman and J.A. Carter. The Potential Toxicity
of Bioaccumulation in Aquatic Systems of Trace Elements Present in
Aqueous Coal Conversion Effluents. In: Trace Substances in Environ-
mental Health-X, Hemphill, D.D. (ed.). University of Missouri,
Columbia MO, 1976. p. 305-313.
3. Birge, W.J. Aquatic Toxicology of Trace Elements of Coal and Fly Ash.
In: Energy and Environmental Stress in Aquatic Systems, Thorp, J.H.
and J.W. Gibbons (eds.). DOE Symposium Series, 48 (CONF-771114),
Washington, DC, 1978. p. 219-240.
4. Crawford, K.W. , C.H. Prien, L.B. Baboolal, C.C. Shih, and A.A. Lee. A
Preliminary Assessment of the Environmental Impacts from Oil Shale
Developments. Office of Research and Development, U.S. Environmental
Protection Agency, EPA-600/7-77-069, 1977. p. 173.
5. Vaughan, B.E. , K.H. Abel, D.A. Cataldo, J.M. Hales, C.E. Hane, L.A.
Rancitelli, R.C. Routson, R.E. Wildung, and E.G. Wolf. Review of
Potential Impact on Health and Environmental Quality from Metals Enter-
ing the Environment as a Result of Coal Utilization. Pacific Northwest
Laboratory, Battelle Memorial Institute, Richiand, WA, 1975. p. 75.
6. Dinneen, G.U. Oil Shale and Its Potential Utilization. In: Symposiun’
Proceedings: Environmental Aspects of Fuel Conversion Technology, St.
Louis, MO, May, 1974, U.S. Environmental Protection Agency,
EPA-65O/2 -74-118, 1974. p. 341-352.
7. Fruchter, J.S. , M.R. Peterson, J.C. Laul, and P.W. Ryan. High Preci-
sion Trace Element and Organic Constituent Analysis of Oil Shale and
Solvent Refined Coal Materials. (Presented at Oil Shale and Tar Sand
Chemistry Symposium. New Orleans, LA. Mar. 27-Apr. 1, 1977. NTIS,
BNWL-SA-6301.)
529
-------
8. Cook, E.W. Elemental Abundance in Green River Oil Shale. Chem. Geol.
11: 321-324, 1973.
9. Pellizzari, E.D. Identification of Components of Energy-Related Wastes
and Effluents. Environmental Research Laboratory, Office of Research
and Development, U.S. Environmental Protection Agency, Athens, GA,
EPA-600/7-78-004, 1978. p. 500.
10. Fulkerson, W. , A. Andren, N. Bctlton, J. Carter, J. Emery, C. Feldman,
L. Hulett, 0. Klein, W. Lyon, M. Mills, J. Ogle, V. Talmi, and R.
VanHook. Allen Steam Plant Study. In: Energy Division Annual
Progress Report, ORNL 5030, Oak Ridge National Laboratory, Oak Ridge
TN, 1975. p. 77-82.
11. Carter, J.A. Trace Element Composition of Coal-Derived Materials
(NSF-RANN). In: Coal Technology Program Quarterly Progress Report No.
1, ORNL 5026, Oak Ridge National Laboratory, Oak Ridge, TN, 1975. p.
66-69.
12. Ruch, R.R., H.J. Gluskoter, anti N.F. Shimp. Distribution of Trace
Elements in Coal. In: Symposium Proceedings: Environmental Aspects
of Fuel Conversion Technology, St. Louis, MO, May, 1974, U.S. Environ-
mental Protection Agency, EPA-650/2-74-118, 1974. p. 49-53.
13. Torrey, S. Trace Contaminants from Coal. Noyes Data Corp. , Park
Ridge, NJ, 1978. p. 294.
14. Bolton, P.E., J.A. Carter, J.F. Emery, C. Feldman, W. Fulkerson, L.D.
Hulett, and W.S. Lyon. Trace Element Mass Balance Around a Coal-Fired
Steam Plant. In: Trace Elements in Fuel, Babu, S.P. (ed.). Advances
in Chemistry Series, 141, 1975. p. 175-187.
15. Gluskoter, H.J., R.A. Cahill, W.G. Miller, R.R. Ruch, and N.F. Shimp.
An Investigation of Trace Elements in Coal. In: Symposium Proceed-
ings: Environmental Aspects of Fuel Conversion Technology, II,
Hollywood, FL, Dec., 1975, U.S. Environmental Protection Agency,
EPA-600/2-76-149, 1976. p. 39-46.
16. Chu, T.J., R.J. Ruane, and P.A. Krenkel. Characterization and Reuse of
Ash Pond Effluents in Coal-Fired Power Plants. J. Water Pollut.
Control Fed. 50: 2494-2508, 1978.
17. Alford, A.L. and W.T. Donaldson. Chemical Constituents Found in Wastes
from Coal Conversion and Oil Shale Processing. In: Energy/Environment
II. Office of Research and Development, U.S. Environmental Protection
Agency, EPA-600/9-77-012, 1977. p. 443-447.
18. Forney, A.J., W.P. Haynes, S.J. Gasior, R.M. Kornosky, C.E., Schmidt,
and A.G. Sharkey. Trace Elements and Major Component Balances Around
the Synthane PDU Gasifier. In: Symposium Proceedings: Environmental
530
-------
Aspects of Fuel Conversion Technology, II, Hollywood, FL, Dec., 1975,
U.S. Environmental Protection Agency, EPA-600/2-76-149, 1976. p. 67-81.
19. Brooks, A., R. Ellson, 0. Fields, J. Mankin, M. Mills, J. Munro, M.
Patterson, R. Raidon, M. Reeves, B. Rust, W. VanWinkle, and S.B.
Watson. Development of an Environmental Unified Transport Model for
Toxic Materials. In: Ecology and nalysis of Trace Elements, Progress
Report, ORNL-NSF-EATC-1, Oak Ridge National Laboratory, Oak Ridge, TN,
1973. p. 27-59.
20. Pillay, K.K.S., C.C. Thomas, Jr., J.A. Sondel, and C.M. Hyche. Mercury
Pollution of Lake Erie Ecosphere. Environ. Res. 5: 172-181, 1972.
21. McKim, J.M. Evaluation of Tests with Early Life Stages of Fish for
Predicting Long Term Toxicity. J. Fish. Res. Bd. Can. 34(8):
1148-1154, 1977.
22. Birge, W.J. , J.A. Black, A.G. Westerman, and J.E. Hudson. The Effects
of Mercury on Reproduction of Fish and Amphibians. In: Biogeochemis-
try of Mercury, Nriagu, J.O. (ed.). Elsevier/North-Holland Biomedical
Press, 1979. (in press).
23. Birge, W.J. , J.A. Black, J.E. Hudson, and D.M. Bruser. Embryo-Larval
Toxicit.y Tests with Organic Compounds. In: Aquatic Toxicology,
Marking, L.L. and R.A. Kimerle (eds.). Special Technical Publication
667, American Society for Testing and Materials, Philadelphia, PA,
1979. p. 131-147.
24. Birge, W.J., J.A. Black, and D.M. Bruser. Toxicity of Organic Chemi-
cals to Embryo-Larval Stages of Fish. Office of Toxic Substance, U.S.
Environmental Protection Agency, EPA-560/11-79-O07, 1979. p. 60.
25. Mitre Corporation, Metrek Division. The Health and Environmental
Effects of Coal Gasification anc Liquefaction Technologies: A Workshop
Summary and Panel Report, 1979. (in press)
26. Birge, W.J., J.E. Hudson, J.A. Black, and A.G. Westerman. Embryo-
Larval Bioassays on Inorganic Coal Elements and in situ Biomonitoring
of Coal Waste Effluents. In: Surface Mining and Fish/Wildlife Needs
in the Eastern United States, Proceedings of a Symposium, Samuel, D.E.,
J.R. Stauffer, C.H. Hocutt, and W.T. Mason (eds.). Office of Biologi-
cal Sciences, Fish and Wildlife Service, U.S. Department of the
Interior, FWS/OBS-78/81, 1978. p. 97-104.
27. National Technical Advisory Committee. Water Quality Criteria. U.S.
Department of the Interior, Washington, D.C., 1968. p. 234.
28. Finney, D.J. Probit Analysis, Third Edition. Cambridge Press, New
York, 1971. p. 333.
531
-------
29. Daum, R.J. A Revision of Two Computer Programs for Probit Analysis.
Bull. Entom. Soc. Am. 16: 10-15, 1969.
30. Birge, W.J. and J.A. Black. Sensitivity of Vertebrate Embryos to Boron
Compounds. Office of Toxic Substances, U.S. Environmental Protection
Agency, Washington, DC, EPA-560/1-76-008, 1977. p. 66.
31. Perkin-Elmer Corporation. Analytical Methods for Atomic Absorption
Spectrophotometry. Perkin-Elmer Corporation, Norwalk, CT, 1973.
32. Birge, W.J. , J.A. Black, and A.G. Westerman. Evaluation of Aquatic
Pollutants Using Fish and Amphibian Eggs as Bioassay Organisms. In:
Proceedings of the Symposium on Pathobiology of Environmental Pollu-
tants: Animal Models and Wildlife as Monitors, Peter, F.M. (ed.).
Institute of Laboratory Animal Resources, National Research Council,
National Academy of Sciences, Washington, DC, 1979. (in press)
33. Birge, W.J., A.G. Westerman, and O.W. Roberts. Lethal and Teratogenic
Effects of Metallic Pollutants on Vertebrate Embryos. In: Trace
Contaminants in the Environment, Proceedings of the Second Annual
NFS-RANN Trace Contaminants Conference, Asilomar, CA, 1974. p.
316 -320.
34. McKim, J. 1. , J.G. Eaton, and G.W. Holcoinbe. Metal Toxicity to Embryos
and Larvae of Eight Species of Freshwater Fish--Il: Copper. Bull.
Environ. Contam. Toxicol. 19: 608-616, 1978.
35. U.S. Environmental Protection Agency. Quality Criteria for Water.
U.S. Environmental Protection Agency, Washington, DC, 1976. p. 256.
36. McKim, J.M., G.W. Holcombe, G.F. Olson, and E.P. Hunt. Long Term
Effects of Methylmercuric Chloride on Three Generations of Brook Trout
( Salvelirius fontinalis) : Toxic:ity, Accumulation, Distribution, and
Elimination. J. Fish. Res. Bd. Can. 33: 2726-2739, 1976.
37. Davies, P.H., J.P. Goetti, Jr., and J.R. Sinley. Toxicity of Silver to
Rainbow Trout ( Salmo gairdneri) . Water Res. 12: 113-117, 1978.
38. Sauter, S., K.S. Buxton, K.J. t acek, and S.R. Petrocelli. Effects of
Exposure to Heavy Metals on Selected Freshwater Fish; Toxicity of
Copper, Cadmium, Chromium and Lead to Eggs and Fry of Seven Fish
Species. U.S. Environmental Protection Agency, Duluth, MN,
EPA-600/3-76-105, 1976. p. 75.
39. McKim, J.M. and D.A. Benoit. Di ration of Toxicity Tests for Establish-
ing No Effect” Concentrations for Copper with Brook Trout ( Salvelinus
fontinalis) . J. Fish. Res. Bd. Can. 31: 449-452, 1974.
40. Eaton, J.G. , J.M. Mckim and G. . Holcombe. Metal Toxicity to Embryos
and Larvae of Seven Freshwater Fish Species--I. Cadmium. Bull.
Environ. Contam. Toxicol. 19: 95-103, 1978.
532
-------
41. Benoit, L .A., E.N. Leonard, G.M. Christensen, and J.T. Fiandt. Toxic
Effects of Cadmium on Three Geierations of Brook Trout ( Salvelinus
fontinalis) . Trans. Am. Fish. Soc. 105: 550-560, 1976.
42. Benoit, D.A. Toxic Effects of Hexavalent Chromium on Brook Trout
( Salvelinus fontinalis ) and Rainbow Trout ( Salmo gairdneri) . Water
Res. 10: 497-500, 1976.
43. Biesinger, K.E. and G.M. Christensen. Effects of Various Metals on
Surviva’, Growth, Reproduction, and Metaboflsm of Daphnia magna . J.
Fish. Res. Bd. Can. 29: 1691-1.700, 1972.
44. Trabalka, J.R. and C.W. Gehrs. An Observation on the Toxicity of
Hexavalent Chromium to Daphnia n agna. Toxicology Letters 1: 131-134,
1977.
45. Holcombe, G.W. , D.A. Benoit, E.N. Leonard, and J.M. McKim. Long Term
Effects of Lead Exposure on Three Generations of Brook Trout
( Salvelinus fontinalis) . J. Fish. Res. Bd. Can. 33: 1731-1741, 1976.
46. Davies, P.H., J.P. Goettl, Jr., J.R. Sinley, and N.E. Smith. Acute and
Chronic Toxicity of Lead to Rainbow Trout Salnio gairdneri , In Hard and
Soft Water. Water Res. 10: 199-206, 1976.
47. Lind, D.T. Personal communication, 1979.
48. Brungs, W.A. Chronic Toxicity of Zinc to the Fathead Minnow,
Pimephales promelas Rafinesque. Trans. Am. Fish. Soc. 98: 272-279,
1969.
49. Spehar, FLL. Cadmium and Zinc Toxicity to Jordanella floridae . J.
Fish. Res. Bd. Can. 33: 1939-1945, 1976.
50. McKee, JE. and H.W. Wolf. Water Quality Criteria, Second Edition.
State Water Quality Control Board, Sacramento, CA, 1963. P. 548.
51. Pickering, Q.H. Chronic Toxicity of Nickel to the Fathead Minnow. J.
Water Pollut. Control Fed. 46: 760-765, 1974.
52. Litchfield, J.T. , Jr. and F. Wilcoxon. A Simplified Method of Evaluat-
ing Dose-Effect Experiments. J. Pharmacol. and Exp. Therapeutics 96:
99-113, 1949.
53. Sokal, R.R. and F.J. Rohlf. Biometry. W.H. Freemand and Co., San
Francisco, CA, 1969. p. 776.
54. Birge, W.J. and J.A. Black. Aquatic Toxicology of Nickel. In: Nickel
in the Environment, Nriagu, J.O. (ed.). John Wiley and Sons, Inc., New
York, NY, 1979. (in press)
533
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55. Mitre Corporation, Metrek Divi;ion. The Health and Environmental
Effects of Oil Shale Technothgies: A Workshop Summary and Panel
Report, 1979. (in press)
56. Gavis, J. and J.F. Ferguson.
Environment. Water Res. 6: 989-1008, 1972.
The Cycling of Mercury Through the
57. Brungs, W.A. , J.R. Geckler, and M. Gast. Acute and Chronic Toxicity of
Copper to the Fathead Minnow in a Surface Water of Variable Quality.
Water Res. 10: 37-43, 1976.
-r
100
80
L
I ’
(I )
60
40
20
0
• .....
Hg .....
— - Cu-Hg Mixture .......
— Calculated Additive Effects . Synergism
00 1 0•l
METAL CONCENTRATION, mg/I
000 1
V
Figure 1. Effects of mercury-copper mixture on rainbow trout embryos.
Mercury and copper were mixed in equal proportions and
administered from fertilization through hatching (24 days).
534
Antagonism \.
I —- I I
-------
AN ANALYTICAL METHOD FOR ASSESSING THE QUALITY, BY MICROBIAL
EVALUATION, OF AQUEOUS EFFLUENTS OBTAINED FROM AN IN SITU OIL SHALE PROCESS”
W. Kennedy Gauger, Stephen E. Williams
Plant Science Division
University of Wyoming
David S. Farrier
Lararnie Energy Technology Center
U.S. Department of Energy
John C. Adams
Division of Microbiology and Veterinary Medicine
University of Wyoming
Laramie, Wyoming 82071
ABS1 RACT
An analytical method was developed for the enumeration of microorgan-
isms which grow in waste waters (retort water) derived from an in situ oil
shale processing experiment (Laramie Energy Technology Center Rock Springs
Site 9 Experiment, Omega-9 retort water). These waters are high in hydro-
carbon components which may be inimical in the environment, but subject to
degradation by microorganisms.
Growth of indigenous microbial populations occurred rapidly in the
retort water. A culture medium was developed for the appraisal of microbial
proliferation which was compared with, and found to be superior to, standard
media for the enumeration of pollution indicators.
INTRODUCTION
Determining environmental interactions and fate of, and devising treat-
ment and control systems for aqueous effluents derived from in situ oil
shale processing are areas of active research (Earner et al. 1978a; Farrier
et al. 1978b).’’ 2 Previous studies have shown that high aerobic and anaer-
obic heterotrophic bacterial populaUon densities occur concomitantly with
an increase in the turbidity of freshly filtered (0.4 pm) Omega-9 retort
water after a few days incubation at room temperature (Farrier et al.
1977). Prolireration of these microorganisms significantly alters the
nature and concentrations of dissolved organic (Felix, Earner and Poulson,
*published with the approval of the Director of the Wyoming Agricultural
Experiment Station, Laramie, Wyoming, 82071, as paper No. SR 929.
535
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1977; Pellizza ’i, 1978) ’ and inorganic (Fox, 1978)6 constituents. Thus,
microbial evaluation of effluents derived from in situ oil shale processing
is warranted ic order to completely characterize such waters. This paper
presents the development of new microbial analytical methods suitable to
these waters and compares such methods with standard procedures.
MATERIALS AND METHODS
Omega-9 Retort Water
The acquisition, processing and storage of Omega-9 retort water has
been previously reported (Farrier et al., 1977) Fox, Farrier and Poulson
(1978); Farrier, Fox and Poulson (1979)7 8 have detailed elemental and water
quality analyses and Leenheer and Farrier (1978) the qualitative organic
analysis of th Omega-9 sample. All Omega-9 retort water described in this
study was stored at 2±1C.
Preparation of Retort Water Agar (RWA )
Retort water samples for microbial evaluation were aseptically with-
drawn from a 114 liter, polypropylene lined, storage drum in 1-1.5 liter
aliquots as needed. The retort water was centrifuged (90 mm, 5C, 5500 x G)
to remove suspended materials and rendered aseptic by passing through three
sterile membrane filters stacked in series (1.2, 0.45, 0.22 pm), under 0.68
atm (10 lb/in 2 ) N 2 pressure. Filter sterilization was repeated and flasks
containing the retort water were placed on a rotary shaker (128 rpm) for
18-36 hour incubation at 20-25C. Flasks which became visually turbid
(indicative of the presence of microorganisms) were presumed contamfnated
and discarded. Nitrogen (25 mM KNO 3 ), phosphorus (5 mM K 2 HPO 4 ), calcium (5
mM CaCl 2 ), and magnesium (5 mM MgC1 2 H 2 O) were added to water agar (3% WIV
agar in deionized water) and sterilized by autoclaving. Equal volumes of
autoclaved water agar (cooled to 50C) and filter-sterilized retort water
(warmed to 50C) were mixed, resulting in a final agar concentration of 1.5%.
The Retort Water Agar (RWA) was dispensed into sterile petri dishes. Addi-
tion of the nutrients was to obviate the possibility that insufficient
quantities would limit microbial growth. Justification for addition of
nutrients was predicated on our findings and that of other investigators
(Ossio et al., 1978)’° which showed increased growth with the addition of
nitrogen, phosphorus, calcium and magnesium. The protocol detailing the
handling and sterilization of 0mega 9 is presented in Figures 1 and 2.
Evaluation of Microbial Growth Kinetics
An experiment was performed to assess the growth kinetics exhibited by
indigenous Omega-9 retort water microorganisms. One liter of Omega-9 retort
water was placed on a rotary shaker (128 rpm) and incubated at 22-25C.
Microbial growth was measured spectrophotometrically (Beckman Model 25) at
660 mu by measuring the absorbance of three ml aliquots of retort water at
two-hour intervals until a stationary growth phase was established. Absorb-
ance in these aliquots was measured against a filter-sterilized retort water
blank. The growth rate constant (Mandelstam and McQuillan, 1973)” and
aeneration time (Stanier, Doudoroff and Adelberg, 1970)12 were calculated.
536
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Figure 1. PRE—STERILIZATION PREPARATIVE SCHEME FOR OMECA—9 RETORT WATER
STORED OMEGA- 9
RETORT WATER
(3—5°C, ll3.62 drum)
I
ASEPTICALLY TRANSFERRED
TO SAMPLING FLASK
01
CENTRI FUGAT ION
(90 mm., 5°C, 5,500 x C)
PELLET discarded or FILTER STERILIZATION
resuspended in suit-
able growth medium
for isolation of auto-
chthonous microbes
-------
Figure 2. FILTER STERILIZATION PROTOCOL FOR OMEGA—9 RETORT WATER
FILTER STERILIZATION
(N 2 gas; 1.2 pm, 0.45 pm, 0.2 pm,
membrane filters, stacked in series)
1p
FILTER STERILIZATION
(again as above)
ROTARY SHAKER
(128 rpm, 2—3 days,
ambient room temperature)
AGAR MEDIUM BROTH MEDIUM
-------
Evaluation of Standard Media for the Enumeration of Retort Water
Microorgani sms
Standard methods for evaluating microorganisms in domestic and recrea-
tional waters involve the appraisal of total and fecal coliforms, fecal
streptococci arid total viable microbial populations. Coliforms and fecal
streptococci bacteria were enumerated from sewacje effluent and a turbid
retort water culture by standard membrane filtration procedures (APHA,
1975).13 Microorganisms in these inocula were also counted on Plate Count
Agar, PCA, Modified Henrici Agar, Ha (Stark and McCoy, 1938)’ and RWA. PCA
is the standard recommended medium for total population counts (APHA,
1975).’ HA has been shown to be superior to PCA in the enumeration of
bacterial populations derived from alpine water sources (Skinner et al.,
1974a; Skinner et al. , 1974b) .’ 5 ’’ 6
Control plates of all media were inoculated with filter-sterilized
retort water to obviate the possibility that components in the Omega-9
process water might interfere with the indicator systems of the standard
media (i.e. , MFC broth for fecal coliforms, M-Endo broth for total coliforms
and KF agar for fecal streptococci).
Sewage effluent was obtained from the last in a series of three lagoons
which constitute the Laramie, Wyoming sewage treatment system. A one liter
sample was aseptically colleted. One- and ten—ml volumes of sewage effluent
were eluted through sterile Millipore HC 0.45 pm membrane filters and plated
on the standard media. These filters are efficacious for cultivating micro-
organisms on their surface (Green, Clausen and Litsky, 1975).17 The sewage
sample was plated in duplicate on PCA, HA and RWA. One hundred ml aliquots
of turbid retort water culture were eluted through sterile HC filters and
plated in duplicate on the various standard media. This inoculum was also
plated in dupl 4 cate on PCA, HA, and RWA. All PCA, HA and RWA plates were
incubated for one week at 20C. This temperature was chosen to obviate the
possibility that some microbes (e.g. , facultative psychrophiles) would be
excluded if the standard 35C incubation temperature was used. The experi-
mental design is depicted in Figure 3.
RESULTS
Evaluation of Microbial Growth Kinetics
The growth curve (Figure 4) illustrates the kinetics of microbial
growth by autochthonous retort water microorganisms. The exponential growth
phase began after four hours incubation and lasted ten hours, at which point
the organisms entered stationary growth phase. A generation time of 110.6
minutes (growtI rate constant of 0.376) was computed for the exponential
growth period.
Evaluation of the Growth of Microorganisms on Selected Media
The results of the study evaluating growth of microorganisms on stan-
dard media (Figure 5) suggest that standard indicator systems are, indeed,
539
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Figure 3. DESIGN OF EXPERIMENT COMPARING MEDIA FOR STANDARD EXAMINATION
OF WASTE WATER AND RETORT WATER.
ENUMERATION METHOD
OPULAT ION
‘\ ND
“ DIUM
INOCULUN \
FECAL
COLIFOENS
(MFc)
TOTAL
COLIFORMS
(M—ENDO)
FECAL
STREPTOCOCCI
(Kr AGAR)
TOTAL
COUNT
(PCA)
TOTAL
COUNT
(HA)
TOTAL
COUNT
(RWA)
LARAMI E
SEWAGE
EFFLUENT
0
0
0
0
0
0
TUR3ID
OMEGA 9
RW
Q
0
0
0
0
0
FILTER-
STERILIZED
OMEGA-9
RW
0
010
0
0
0
MEMBRANE FILTRATION PROCEDURE
STANDARD
C T ?
PLATE COUNT PROCEDURE
-------
0 1
3c
m
C I ) C ’ —
= = cy
(I) -
•_-.- - c CD
c
—
cnC)
C T ’
Q, ,)< CD
I—
&
200
200 -
100
80
‘260
x
40
3o
0
20
LU
C
z
4
0
U,
4
3
2
GENERATION TIME 110.6 MIN
GROWTH RATE CONSTANT = 0.376
I I I I I I I • I I I
INCU8ATION TIME, HOURS
Figure 4. GROWTH CURVE OF AUTOCMTHONOUS MICROORGANISMS IN RETORT WATER.
A8SORBANCE IS DIRECTLY PROPORTIONAL 10 MG DRY WEIGHT OF MICROORGAN 1SMS.
8-
6-
4.-
0 246 8 10 12 4 16 18 ?O 22 24 2628 3032
-------
Figure 5. COMPARISON OF t€DIA FOR STANDARD EXAMINATION OF WASTE WATER AND RETORT WATER.
0 ’
a
N )
-------
appropriate for counting microorganisms in sewage effluent. Conversely,
bacteria native to retort water do not grow on coliform or streptococci
enumeration megia. HA was approximately sixfold better than PCA in evalu-
ating total heterotrophic populations from the sewage inoculum. This cor-
roborates the findings of Skinner et al., 15 ’ 16 who compared these media in
alpine waters (1974a, 1974b). By comparison, RWA was not adequate for
enumerating microorganisms from sewane sources. PCA and HA are appro i-
mately equal for counting retort water microorganisms; whereas, RWA is three
to four times better than PCA or HA in culturing retort water microbes.
Retort water did not affect the indicator systems in the standard media.
DISCUSSION AND CONCLUSIONS
Conclusions from this study can he summarized as follows:
1. Growth of microorganisms in Ornega-9 retort water is rapid.
2. Sewage microorganisms do not grow substantially on retort
water agar.
3. Standard media for the enumeration of bacteria from domestic
and recreational water sources do not support the growth of
microorganisms derived from Omega-9 retort water.
4. Autochthonous retort water microorganisms grow well on a
medium containing Omega-9 retort water as the only source of
carbon and energy.
The kinetics of microbial growth study provided evidence that native
microorganisms are capable of rapid growth in the process water. One can
infer that the original constituents of the retort water are being altered
as the result of microbial growth since the water provided the sole carbon
and energy sources. Current studies are assessing this hypothesis.
Based on the results of experimentation set forth above, one might be
prompted to question why standard analyses should be considered at all in
the analysis of oil shale waste waters. As pointed out in a recent article
in Environmental Science and Technology (Miller, 1978),18 a goal of the
Environmental Protection Agency’s Quality Assurance program is the standard-
ization of analytical procedures for water quality. Where this is concep-
tually a practical approach to quality assurance, it might not immediately
be feasible for oil shale process wetters or domestic sources contaminated
with oil shale waste effluent. Where microbial populations are concerned,
methodologies for assessment of water quality should detail a specific group
or type of pollutant especially where rapid microbial growth has been demon-
strated, as in this study. The standard indicators, fecal coliforms and
fecal streptococci, appear to be adequate for evaluating fecal pollution.
One would not expect to find these indicators in oil shale waste waters, but
the necessity to look for them may be required in waters where there has
been an admixture of oil shale waste effluent with domestic or recreational
scurces. Total heterotrOphiC bacteri3 enumerated on PCA might provide data
543
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which are indicative of changes in total populations with time, but fail to
reflect potential population shifts; e.g., from an environmentally diverse
population to one selected for by the type of pollutant. If retort water
comes into contact with soil or other waters, its presence may selectively
enrich microorganisms that can grow in it, thereby excluding other micro-
organisms whose role in nature might be uniquely important. The RWA medium
constitutes an analytical method for evaluating such effects on microbial
populations when coupled with PCA or HA.
The Omega-9 retort water used ir this study may not be representative
of retort waters in general. However, the medium described here could be
prepared using any type of retort water. Therefore, we report a method
which can be used to assess the microbiological quality of oil shale waste
waters or sources contaminated with these effluents.
ACKNOWLEDGEMENTS
This research was supported in part by U.S. Department of Energy con-
tracts EY-77-C-04-3913 and ET-77-S-03-1761.
REFERENCES
1. Farrier, U.S., J.E. Virgona, I.E. Phillips and R.E. Poulson. 1978a.
Environmental research for in situ oil shale processing. Proc. 11th
Ann. Oil Shale Symp. Colorado School of Mines Press, pp. 81-99.
2. Farrier, U.S., L.W. Harrington and R.E. Poulson. 1978b. Integrated
compliance and control technology research activities for in situ
fossil fuel processing experiments. Proceedings U.S.D.O.E. Environ-
mental Control Symposium, Washington, D.C., Nov. 28-30; In Press (manu-
script available from authors).
3. Farrier, U.S., R.E. Poulson, Q.C. Skinner, J.C. Adams and J.P. Bower.
1977. Acquisition, processing and storage for environmental research
of aqueous effluents derived from in situ oil shale processing. Proc.
Second Pacific Chem. Eng. Cong. 2: 1031-1035.
4. Felix, W.D. , U.S. Farrier and R.E. Paulson. 1977. High performance
liquid chromatographic characterization of oil shale retort waters.
Proc. Second Pacific Chem. Eng. Cong. 1: 480-485.
5. Pellizzari, E.D. 1978. Identification of components of energy-related
wastes and effluents. EPA Pubi. #EPA-600/7-78-004, Nati. Tech. Info.
Service. Springfield, VA, 22161. pp. 289-291, 407-413.
6. Fox, J.P. 1978. The partitioning of major and minor elements during
simulated in situ oil shale retorting. Ph.D. Dissertation, Univ. of
California, Berkeley.
7. Fox, J.P., D.S. Farrier and R.E. Poulson. 1978. Chemical characteri-
zation anc analytical considerations for an in situ oil shale process
544
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water. Lararnie Energy Technoloqy Center Report of Investigations.
Publ. #LETC/RI-78/7.
8. Farrier, D.S., J.P. Fox and R.E. Poulson. 1979. Interlaboratory,
multimethod study of an in situ produced oil shale process water. This
symposi urn.
9. Leenheer, J.A. and D.S. Earner. 1979. Applications of dissolved
organic carbon fractionation analysis to the characterization of oil
shale processing waters. This symposium.
10. Ossio, E..A. , J.P. Fox, J.F. Thomas and R.E. Poulson. 1978. Anaerobic
fermentation of simulated in situ oil shale retort water. Abs. Pap.
ACS 175: 63-64.
11. Mandeistam, J. and K. McQuillan. 1973. Biochemistry of Bacterial
Growth . J. Wiley and Sons, New York. pp. 137-159.
12. Stanier, R.Y. , M. Doudoroff and E.A. Adelberg. 1970. The Microbial
World , 3ra ed. Prentice-Hail, Inc. Englewood Cliffs, New Jersey pp.
298-324.
13. American Public Health Association. 1975. Standard Methods for the
Examination of Water and Wastewater . 14th ed. Am. Public Health
Assoc. , Inc. New York, New York.
14. Stark, W.H. and E. McCoy. 1938. Distribution of bacteria in certain
lakes of northern Wisconsin. 2entralbl. Bakteriol. Parasitenk.
Infektionskr. Abt. 11. 98: 201-209.
15. Skinner, W.D. , J.C. Adams, P.A. Rechard and A.A. Bettle. 1974a.
Enumeration of selected bacterial populations in a high mountain water-
shed. Can. 3. Microbiol. 20: 1487-1492.
16. Skinner, W.D. , J.C. Adams, P.A. Rechard and A.A. Beetle. 1974b.
Effect of summer use of a mountain watershed on bacterial water quali-
ty. 3. Environ. Qua]. 3: 329-335.
17. Green, G.L. • E. Clausen and W. Litsky. 1975. Comparison of the new
Millipore HC with conventional membrane filters for the enumeration of
fecal coliform bacteria. Appi. Microbial. 30: 697-699.
18. Miller, S. 1978. Federal environmental monitoring: Will the bubble
burst? Environ. Sci. Technol. 12: 1264-1269.
545
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MONITORING OF RETORTED OIL SHALE EFFECTS ON SURFACE SOIL NITROGEN
FIXATION PROCESSES: A RESOURCE FOR DESIGN AND
MANAGEMENT OF LAND RECLAMATION PROGRAMS
D.A. Klein, L.E. Hersman and S-Y. Wu
Department of Microbiology
Colorado State University
Fort Collins, Colorado 80523
ABSTRACT
Nitrogen fixation, both by free-living soil microbes and by legume-
Rhizobium type associations, has been found to be particularly sensitive to
material in retorted shales or in aqueous shale leachates, in comparison
with other more general measurements of microbiological activity. With the
important role which nitrogen fixation might play in the development of
resources requiring revegetation and rehabilitation programs, the measure-
ment of shale component effects on this process would appear to represent a
convenient, inexpensive means of monitoring biological effects of oil shale
residues during both the design and conduct of rehabilitation projects. To
allow the most efficient utilization of this approach in the deisgn and
monitoring of an oil shale rehabilitation project, quality assurance factors
(QA) which should be considered, including site selection and sampling,
measurement techniques, and interpretation considerations are discussed.
With the relatively simplicity of these procedures, which have been used in
a wide range of environmental applications, it is suggested that site selec-
tion and sampling are points where greater care should be given, to assure
maximum usefulness of this parameter in relation to QA concerns.
INTRODUCTION
The importance of establishing and maintaining a functional microbio-
logical community in a plant—soil system has been well documented
(Alexander 1 Aspiras et al. 2 ; Bayer, Gardner and Gardner, 3 Harris et al. 4 ),
and these processes become especially important when establishing plant
growth on raw or retorted oil shale materials. An important characteristic
of retorted oil shale materials is the large amount of leaching which is
required to allow plant growth (Harbert and Berg, 5 Ward, Marghiem and Löf 6 ),
and the range of potentially inhibitory materials which are present Schmehl 7
and McCaslin, 6 Shendrikar and Faudel 8 ). With the potential to restabilize
extensive areas which may be used for disposal of raw shale or retorted
shale, even with in situ or especially with modified in situ processing,
where a volume of shale equal to that of 25% of the volume processed may
have to be disposed of on the surface, the need to monitor and design
546
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reclamation systems to minimize the potential effects of these materials or
their leachates on plant—soil systems will be important.
As an additional aspect of this work, the production of large volumes
of water in the retorting process makes it possible that these process
materials may enter existing aquifers, and eventually mix with surface
waters in particular geological situations. In this regard, biological
nitrogen fixation has been found to be particularly sensitive to the
presence of retorted shale (Hersman and Klein 9 10) and retorted shale leach-
ates (unpublished data). The best known of these relationships is the one
between legumes and bacteria of the genus Rhizobium . In addition to these
plant-associated nitrogen fixing relationships, there are a number of free-
living bacteria which fix nitrogen, including the cyanobacteria, and the
genera Clostridium, Kiebsiella , and Azotobacter (Brill’ 1 ). The use of
acetylene reduction as a means of assessing nitrogen fixation potential is
widely used and accepted (Hardy et al.;’ 2 Hardy, Burns and Holsten,’ 3
Kapustka and Rice 14 ). In our experiments, we have been investigating the
effects of retorted oil shale additions on the nitrogen fixation potential
of a Western Colorado surface soil collected from the Piceance Basin of
Colorado. In addition, the effects of shale extracts on nitrogen fixation
by a free-living Rhizobium species have been evaluated to detect possible
changes in plant-associated nitrogen fixation which might be useful in a
field monitoring program, where leachate characterization and control would
be essential.
MATERIALS AND METHODS
Soils . All soils were derived from the intensive study area in the
Piceance Basin area, and samples were sieved with a 2 mm mesh screen, mixed
in a Patterson-kelly twin steel dry blender (Patterson-Kelly Co. , East
Stroudsburg, PA), returned to individual plastic bags, and stored at 6°C
until used.
Retorted oil shale samples were taken from materials used to build
soil-oil shale plant growth testing panels at the intensive study area. The
retorted shale was produced by the Paraho process at Anvil Points, Colorado
by Development Engineering Inc. Nitrogen fixation potential measurements
for these soil samples were carried out using procedures described by
Hardy, Burns and Holsten. 13 Specific equipment used in the laboratory
included a gas chromatograph (Varian Aerograph; Walnut Creek, California)
and an integrater recorder (Omniscribe Recorder, Houston Instrument Co.
Austin, Texas) to allow calculation of peak areas. Acetylene and ethylene
standards were obtained from the Applied Science Laboratories, State
College, Pennsylvania. The separation of acetylene from ethylene was car-
ried out using a 3-mm diameter x 183-cm length stainless steel column filled
with Poropak Q (Waters Associates, Milford, Massachusetts), with a column
bath temperature of 70°C.
Ten grams of soil were placed in serum bottles and brought to 60 per-
cent of moisture holding capacity with a solution of 0.5 percent (w/v)
glucose in water. The bottles were sealed with serum caps and flushed with
547
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N 2 gas for 5 minutes. Then using a 5 ml syringe, 5 ml of gas from each
bottle was replaced with 5 ml of acetylene. The bottles were incubated in
the dark for 48 hours at 25°C. Using a 1 ml syringe, 1 ml of gas was with-
drawn and injected into the chromatograph. Nitrogen fixation was expressed
as nanomoles of ethylene produced per g soil’ 48 hours’.
The surface soil was mixed with either ethylene chloride extracted
retorted oil shale, retorted oil shale, or sterile glass beads in a
Patterson-Kelly dry soil blender to give control soils and mixtures of oil
shale and soil at 10, 20, 30, and 40% by weight of added shale, or with an
equivalent volume of glass beads as controls.
Rhizobium was grown in pure culture under conditions where nitrogen
could be fixed, using procedures described by Keister,’ 6 Keister and
Evans,’ 6 Kurz and LaRue,’ 7 and Pagan et al.’ 8 Shale was extracted using
distilled water in a 2-1 ratio with retorted shale, and the leachate was
separated by centrifugation after 3 hours of shaken incubation at 22°C. The
leachate or retorted shale was added directly to a 24-hour culture of the
active nitrogen-fixing Rhizobium , and monitored for relative acetylene
reduction rates in comparison with control cultures.
RESULTS
Nitrogen fixation by organisms of the free-living Azotobacter type was
found to be especially sensitive to the presence of retorted shale, and this
effect was not simply due to the dilution of the soil with the shale mater-
ials, as the effects were distinctl , greater than when soils were diluted
with equivalent volumes of glass beads (Figure 1). Similar N 2 fixation
responses were observed with normal and extracted shales, both of which were
markedly different from the glass bead mixtures or the plain soil controls.
The data suggest that retorted oil shale and retorted oil shale extracted
with ethylene chloride contained substances inhibitory to the asymbiotic
nitrogen fixing process, and that the observed reductions cannot be attribu-
ted to a dilution effect, since the responses of the shale mixtures at the
higher shale concentrations were much lower than that for similar concen-
trations of glass beads.
In studies of retorted shale and retorted oil shale aqueous extract
effects on nitrogen fixation by Rhizobium 32111, similar distinct effects
were observed (Figure 2). It is of interest to note that with the addition
of the lower levels of retorted shale extract, that stimulation of nitrogen
fixation occurred, which also has been noted to occur under specific test
conditions using intact legume nodules (Hersman and Molitoris’ 9 ). In these
particular Rhizobium studies, the pH of the microbial suspensions did not
vary more than 0.1-0.2 unit with the various additions, making it unlikely
that these effects were due only to changes in the pH of the test systems.
DISCUSSION
Nitrogen fixation by soil microorganisms appears to be sensitive to the
presence of retorted shale and shale leachates, and this procedure may
548
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O Soil - glass beads
Soil - retorted shale
D D Soil- extracted
retorted shale
10 20 30
Percent retorted shale or gloss beads present in mixture
40
Figure 1. Ethylene production in soil-retorted oil shale, soil-extracted
retorted shale, and soil-glass bead mixtures. Standard deviations
are shown.
549
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I4r
•QO.lml
extract
Control
-• 1.0 ml extract
•QO.I g shale
l.Og shale
345
TIME-DAYS
figyre 2. Retorted shale extract affects on acetylene reduction by
Rhl2obj_um 32H1 cultivated ir> the absence of a host plant.
550
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provide a useful technique for monitoring the potential effects of these
materials, and to assist in the aesign and monitoring of reclamation
programs.
This approach to effects monitoring could be applied to provide the
following information:
o lo monitor the biological effects of different retorting
processes under controlled laboratory conditions, at a mini-
mum cost. This could be used to evaluate the relative
effects of varied processing conditions on an easily measured
biological process, and provide a means of developing corre-
lations with chemical analysis information.
o To determine the effects of leachate movement through a
compacted bed of retorted (or raw) oil shale during the
testing and design phases for a particular stabilization
program. This could be used to predict effects, especially
when a possibility might exist of having water movement
through a particular shale system. This would be especially
critical in the design of capillary barriers which might be
required to separate surface soil from a large volume of
retorted shale.
o In areas where poor plant growth might occur after establish-
ment of vegetative cover, the soils across a plant response
gradient could be assayed for nitrogen fixation potential (in
comparison with other biological and chemical measurements).
Based on this type of information, corrective measures might
be recommended, including additional barriers against
moisutre movement, or the placement of additional soil to
isolate leachate influenced materials.
In the use of this type of monitoring approach, the quality assurance
(QA) considerations which have been discussed in a recent report in Environ-
mental Science and Technology 20 should be considered; included the follow-
i ng:
o Site selection error
o Sampling error
o Measurement and reference sample error
o Data handling errors
For the use of a nitrogen fixation potential leachate toxicity assay
under laboratory test conditions, site and sampling error effects should be
able to be minimized, especially if 5-10 aliquots of 0.5-1.0 ml might be
able to be taken from a particular leachate sample.
As the assays will be run at varied times and under different test
conditions, it will be necessary to compare relative changes in nitrogen
fixation rates under controlled conditions where the culture age, cell
551
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density and physiological condtions could be duplicated. An approach which
could be used to minimize within sample variability would be to freeze
leachate samples, and then to analyze the effects of a larger number of
these samples, perhaps taken over a series of times, or from a range of
locations simultaneously in the laboratory. To do this, it would be neces-
sary to establish that freezing did riot have specific effects on the anti-
microbial characteristics of soil samples, shales or leachates.
As noted by Hardy et al., 12 and Hardy, Burns and Holsten,’ 3 the measur-
ing and reference sample error problems can be managed, based on the exten-
sive prior use of this technique, and the ease of standardizing this
analytical procedure.
Field sampling of soils will be more critical, especially with plant
material established on particular sit.es. Sampling, soil mixing and statis-
tical replication problems should be considered, and procedures which can be
used for designing a sampling strategy have been described by Parkinson,
Grey and Williams. 2 ’ It is essential that time be minimized as a function
in such sampling, as abiotic and biotic changes can occur at particular
sites which might mask the effects of leachates on these nitrogen fixation
processes. In a similar manner, through even minor changes in soil water
content at particular sites, the rates of nitrogen fixation might be marked-
ly influenced, making it difficult to analyze and interpret data.
If proper consideration of QA is given in the use of this technique, it
would appear that this will provide information useful for better management
of environments which are disturbed by oil shale processing.
ACKNOWLEDGMENTS
This research was supported by the Department of Energy under contract
EY-76-5-02-4018, awarded to Colorado State University, Department of Range
Science. Dr. Joseph C. Burton, The Nitragin Corporation, Milwaukee,
Wisconsin, kindly supplied the culture of Rhizobium 32111 used in this study.
REFERENCES
1. Alexander, M. 1977. Introduction to Soil Microbiology. 2nd Ed. John
Wiley and Sons, Inc. New York. pp. 467.
2. Aspiras, R.B., O.N. Allen, R.F. Harris and G. Chesters. The Role of
Microorganisms in the Stabilization of Soil Aggregates. Soil Biol.
Biochem. 3:347-353. 1971.
3. Bayer, L.O., W.H. Gardner and W.R. Gardner. Soil Physics, 4th Ed. John
Wiley and Son, Inc. 1972. New York.
4. Harris, R.F., O.N. Allen, G. Chesters and D.F. Attoe. Evaluation of
Microbial Activity in Soil Aggregate Stabilization and Degradation by
the Use of Artificial Aggregates. Proc. Soil Sci. Soc. Am. 27:542-545.
1963.
552
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5. Harbert, H.P., III and W.A. Berg. Vegetative Stabilization of Spent
Oil Shales. Vegetation, Moisture, Salinity and Runoff--1973-1976.
Report EPA 600/7-78-921. USEPA Cincinnati, Ohio. 1978. PP. 183.
6. Ward, J.C. , G.A. Margheim and G.O.G. Löf. Water Pollution Potential of
Rainfall on Spent Oil Shale Residues. EPA Grant #14O3OEDB. Department
of Civil Engineering, Colorado State University. 1971. pp. 117.
7. Schmehl, W.R. and B.D. McCaslin. Some Properties of Spent Oil Shales
Significant to Plant Growth. In: R. Hutnik and G. Davis (ed.) Ecology
and Reclamation of Devastated Land. Vol. 1. Gordon and Breach, New
York. 1973. p. 27-43.
8. Shendrikar, A.D. , and G.B. Faudel. Distribution of Trace Metals During
Oil Shale Retorting. Env. Sci. Tech. 12(3):332-334. 1978.
9. Hersman, L.E. and D.A. Klein. Microbial Activities in Soil--Retorted
Oil Shale Mitxures. Abstracts, Ann. Mtg., Am. Soc. Microbiology, Las
Vegas, Nevada, May, 1978.
10. Hersman, L.E. and D.A. Klein. Retorted Oil Shale Effects on Soil
Microbiological Characteristics. J. Env. Quality. (In press). 1979.
11. Brill, W.J. Biological Nitrogen Fixation. Scientific Amer. :68-80.
1977.
12. Hardy, R.W.F., R.D. Holsten, E.K. Jackson and R.C. Burns. The
Acetylene-Ethylene Assay for N 2 Fixation: Laboratory and Field Evalua-
tion. Plant Physiol. 43:1185-1207. 1968.
13. Hardy, R.W.F. , R.C. Burns, and R.D. Holsten. Applications of the
Acetylene-Ethylene Assay for Measurement of Nitrogen Fixation. Soil
Biol. Biochem. 5:47-81. 1973.
14. Kapustka, L.A. and E.L. Rice. Acetylene Reduction (N 2 fixation) and
Soil and Old Field Succession in Central Oklahoma. Soil Biol. Biochem.
8: 497-503. 1976.
15. Keister, D.L. and W.R. Evans. Acetylene Reduction by Pure Cultures of
Rhizobia . J. Bacteriol. 123:1265-1268. 1975.
16. Keister, D.L. and W.R. Evans. Oxygen Requirement for Acetylene Reduc-
tion by Pure Cultures of Rhizobia . J. Bacteriol. 129:149-153. 1976.
17. Kurz, W.G.W. and T.A. LaRue. Nitrogenase Activity in Rhizobia in
Absence of Plant Host. Nature (London) 256:407-408. 1975.
18. Pagan, J.D.J., J. Child, W.R. Scowscroft and A.H. Gibson. Nitrogen
Fixation by Rhizobium Cultures on a Defined Medium. Nature (London).
256:406-407. 1975.
553
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19. Hersman, L.E. and E. Molitoris. Effects of a Retorted Oil Shale on
Nonpiant Associated and Leguminous Nitrogen Fixation. Abstracts, Ann.
Mtg., Am. Soc. Microbiol. 1979.
20. Anonymous. Federal Environmental Monitoring: Will the Bubble Burst?
Env. Sci. Technology 12(12): 1264—1269. 1978.
21. Parkinson, D., T.R.G. Gray and S.T. Williams. Methods for Studying the
Ecology of Soil Microorganisms. IBP Handbook No. 19. Blackwell Sci.
Publications. London. 1971. pp. 115.
554
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THE EFFECTS OF SOIL PHOSPHORUS ON GROWTH AND ENDOMYCORRHIZAL
DEVELOPMENT IN PLANT SPECIES NATIVE TO COLORADO’S OIL SHALE REGION*
Jean E. Kiel
Stearns-Roger
Environmental Sciences Division
Denver, Colorado
ABSTRACT
Two grass species and three shrub species native to the oil shale
region of western Colorado were evaluated in terms of mycorrhizal infection,
phosphorus content and growth. These results were correlated with levels of
soil phosphorus available for plant use. Mycorrhizal infection appears to
be dependent upon three factors (i.e., species, time, and plant-available
soil phosphorus). Whereas most species become mycorrhizal very early when
soil phosphorus levels are low and will eventually develop moderately heavy
infections even when soil phosphorus levels are high, certain traditionally
nonmycorrhizal species may become infected only under specialized condi-
tions. Growth responses to mycorrhizal infection differ from species to
species, but it is postulated that endomycorrhizal fungi benefit plant
growth and survival in at least some species when phosphorus is a limiting
factor. Reclamation programs on oil shale lands may be benefited by inoc-
ulation with certain species of mycorrhizal fungi.
INTRODUCTION
In light of the latest foreign oil supply crisis, our need for domestic
oil sources will undoubtedly necessitate the utilization of shale oil at
some future date. Disturbances accompanying kerogen retrieval and subse-
quent reclamation--rehabilitation efforts have generated at considerable
amount of concern in many sectors. Integrated studies aimed at identifying
rehabilitation potentials of areas rich in oil shale are presently being
conducted by a group of researchers based at Colorado State University. An
area located between the C-a and C-b oil shale tracts in the Piceance Basin
of western Colorado has been designated as an “intensive study site,” with a
variety of fertility and disturbance studies being carried out by
researchers from several disciplines.
*This work was supported by ERDA Contract EY-76-S-02-4018 and is based on a
thesis submitted by J.E. Kiel to the Academic Faculty of Colorado State
Un i vers i ty.
-------
The microbiological components of belowground ecosystems are among the
least studied and most poorly understood aspects of land rehabilitation and
it is with the role these organisms play in ecosystem stability that the
present study was concerned. A knowledge of these organisms appears to be a
prerequisite to accurate assessment of rehabilitation potentials.
Among the microorganisms which have drawn the most attention and have
been shown to be the most important in terms of plant growth, are a group of
fungal symbionts known as mycorrhizal fungi. A mycorrhiza is a two-membered
association consisting of root tissue and a specialized fungus. The rela-
tionship is mutually beneficial with the plant supplying carbohydrates to
the fungus and the fungus aiding the plant in the uptake of mineral nutrients
--primarily phosphate (P). It has also been suggested that mycorrhizae may
reduce moisture stress in plants (Sat ir, Boyer and Gerdemann’).
Two types of mycorrhizal associations have traditionally been recog-
nized. These are the ecto- and endotrophic forms. The fungal component of
an ectomycorrhiza forms a mantle around the exterior of the root. Hyphac
penetrate only into spaces between individual plant cells. These mycer-
rhizae are common in the family Pinacaea.
Endomycorrhizal fungi do penetrate the cortical cells of the host. A
loose hyphal net extends into the soil surrounding the root but no mantle is
formed. Most angiosperms have endomycorrhizal associations of the
vesicular-arbuscular type, although certain families have traditionally been
found to be nonmycorrhizal. The tern vesicular-arbuscular mycorrhiza refers
to fungal structures occurring within the cortex of infected roots.
Vesicles are primarily fungal storage organs whereas arbuscules are sites of
nutrient exchange. Chlamydospores are formed extracellularly and are a
means of asexual reprodution.
Moorman and Reeves 2 have shown that reductions in VA endosymbiont
populations are correlated with certain land disturbances. It has been
suggested that nonmycorrhizal species may colonize disturbed areas due to
lack of mycorrhizal inoculum and that this lack of inoculum may profoundly
affect the stability of plant communities established by reclamation pro-
grams (Reeves, Wagner, Moorman and Kiel 3 ). Reclamation of disturbed lands
may be enhanced by the presence of mycorrhizal associations of both the
ectotrophic and endotrophic type (Daft and Nicolson; 4 Daft, Hacskaylo and
Nicolson; 5 Marx 6 ).
A greenhouse growth study was undertaken in order to evaluate the
effect of a native fungal endosymbicnt ( Glomus fasciculatus ) on the growth
of several plant species native to the Piceance Basin. Since phosphorus (P)
and mycorrhizae appear to be so intimately linked (Gerdemann; 7 Nicolson; 8
Mosse 9 ), it was determined that each species should be given varying amounts
of phosphorus in both the mycorrhizal (M+) and nonmycorrhizal (M-) treat-
ments. Figure 1 shows the experimental design of the study. Two shrubs
species, Atriplex canescens (fourwing saltbush) and Eurotia lanata (winter-
fat) are members of the Chenopodiaceae which has traditionally been desig-
riated as a nonmycorrhizal family (Gerdemann 7 ). Recently, however, several
556
-------
chenopods have been found to be infected with mycorrhizal fungi (Williams,
Wollum and Aldon; 1 ° Aldon; 1 ’ Williams and Aldon;’ 2 Reeves et al. 3 ). For
this reason, and because of conflicting reports on the effect and incidence
of mycorrhizal infection in chenopods. the decision was made to use Atriplex
and Eurotia in this growth study. Arternisia tridentata (big sage) was
chosen because it is the dominant .hrub throughout much of the Piceance
Basin. The two qrass species. Stipa viridula (green needlegrass) and
Agropyron smithii (western wheatgrass) were chosen because they are common
at the study site.
METHODS
Topsoil was obtained from an undisturbed area of the “intensive study
site” previously mentioned. All soil was steamed for 20 hours, air dried
and then reinoculated with washings of saprobic soil microorganisms isolated
from Piceance Basin soil. The purpose of this was to parallel natural soil
conditions as closely as possible, with the exception of the mycorrhizal
element. Soil analysis results are shown in Table 1.
TABLE 1. RESULT5 OF SOIL ANALYSIS
pH
8.2
N0 3 -N
ppm
3.0
E.C.
1.0
P
ppm
9.0
Lime
High
K
ppm
212.0
% 0.M.
2.6
Zn
ppm
1.7
SAR
0.3
Fe
ppm
6.8
No phosphorus was added to the soil of the low phosphorus regime.
Monocalcium phosphate was used as fertilizer and was applied at the rates of
56 kg/ha or 25 ppm phosphorus in the medium phosphorus regime and 112 kg/ha
or 50 ppm phosphorus in the high phsophorus regime (Figure 1).
Two kg of soil were measured into each pot. Two and one half g of corn
roots heavily infected with Glonius fasciculatus , an endomycorrhizal fungas,
were added to the mycorrhizal (M+) treatments as inoculum. Pots were seeded
and watered to field capacity each day throughout the experiment. Pots were
randomized on greenhouse benches and were rotated periodically. Half of the
grasses were harvested at 90 days. The remaining grasses and the shrubs
were harvested at 180 days. Height was measured on shrubs only. Tops were
clipped just above the soil surface, oven dried at 75°C for 48 hours and
weighed immediately on being taken from the oven. Roots from each pot were
extracted from the soil, fixed in formyl acetic acid (FAA) and kept sepa-
rately until they could be stained. One hundred 1 cm root sections randomly
taken from eac i pot were stained in lactophenol-trypan blue (Phillips and
Hayman 13 ) and assessed for infection. Phycomycetous hyphae and either
pelatons, arbuscules or vesicles were the criteria used for determining
whether or not a root section was mycorrhizal.
557
-------
i triplex canescens
tia lanata
rteinisia tridentata
pa viridula
sr?jthij
7 reps
6 reps
8 reps
9 reps
9 reps
7 reps
6 reps
8 reps
9 reps
9 reps
_____ M —
7 reps A triplex canescens 7 reps
6 reps Eurotia lanata 6 reps
8 reps Arternisia tridentata 8 reps
9 reps Stipcz viridula 9 reps
9 reps Aqrop ron srnithii 9 reps
Atriplex canescens 7 reps
Euro tia lanata 6 reps
Artemisia tridentata 8 reps
Stipa viridula 9 reps
__________________ Aqropz ’ron smithii 9 reps
Atriplex canescens 7 reps
Eurotia lanata 6 reps
Arternisia tridentata 8 reps
Stipa viridula 9 reps
___________________ Aqrop ron smithii 9 reps
Figure 1. Experimental design of endomycorrhizal growth study.
‘triplex canescens
tia lanata
rnisia tridentata
pa viridula
.3nnth ii
triplex canescens
?tia lanata
lrternisia tridentata
viridula
iron smithii
558
-------
The plant tops of species found to be mycorrhizal were assessed for
phosphorus content. A perchioric acid digestion was performed on 1 gram
samples of the dried plant material (Olsen and Dean’ 4 ) and colorimetric
determiantion of phosphorus was done by means of the vanadomolybophosphoric
yellow color method (Jackson 15 ).
RESULTS
Although winterfat and fourwing were inoculated in exactly the same
manner as the other three species which developed extensive infections,
neither of these chenopods became mycorrhizal. Since no infection was found
to be present, a one-way analysis of variance was run on the height and
weight data from these two species to determine whether or not the addition
of phosphate fertilizer had an effect on their growth. The only significant
difference found was a decrease in dry weight of winterfat in the high
phosphorus regime (Table 2).
TABLE 2. WEIGHT AND HEIGHT DATA OF NONMYCORRHIZAL FOURWING SALTBUSH
(A. CANESCENS) AND WINTERFAT (E. LANATA) GROWN UNDER
CONDITiONS OF VARYING SOIL PHOSPHATE LEVELS*
Atriplex
CaneEcens
Eurotia
Lanata
Weight
Height
Weight
Height
(g)
(cm)
(g)
(cm)
NO
P
14.35a
59.7a
4.41a
61.4a
25
ppm
P
13.73a
64.la
4.57a
65.2a
50
ppm
P
12.83a
58.9a
2.22b
44.9a
*Values are means of 7 replicates (A. canescens ) and 6 replicates (E.
lanata) . Means in the same column followed by the same letters are not
significantly different at the 0.05 level of probability.
In contrast to winterfat and fourwing saltbush, the M+ treatments of
the grasses became heavily infected with mycorrhizal fungi. At both 90 and
180 days, infection levels in green needlegrass were greatly inhibited by
both the medium and high phosphate fertilization rates (Table 3). This
inhibition of rnycorrhizal infection, apparently due to increased soil phos-
phorus concentrations, occurred consistently throughout the experiment.
There were no significant differences in weight among any of the treatments.
At 90 days there was actually a 24 percent increase in the M+, no phosphorus
treatment over the M-, no phosphorus treatment. This increase was not,
however, significant at the 5 percent level and was greatly minimized at 180
days. Close observation of the plants revealed that leaves of mycorrhizal
plants were somewhat more succulent. This was true of western wheatgrass
and big sage as well.
559
-------
TABLE 3. THE EFFECTS OF PHOSPHATE FERTILIZATION ON SHOOT DRY WEIGHT,
P CONCENTRATION AND ROOT INFECTION LEVELS IN GREEN NEEDLEGRASS
( S. viridula)*
Weight
(q)
% Infection
pg
P/a Plant Matter
90
days 180 days
90 days
180
days
90 days 180
days
M
No
P
4.57a
8.88a
61.8c
67.5d
12.Oa
7.3a
M-
No
P
3.69a
8.31a
O.Oa
O.Oa
1O.9a
6.8a
M+
25
ppm
P
4.44a
8.55a
20.Ob
49. Sb
14.Oc
8.6a
,, -
25
ppm
P
3.87a
8.37a
O.Oa
O.Oa
13.8bc
9.Oa
M+
50
ppm
P
4.31a
8.O4a
8.8ab
54.Oc
15.4c
8.8a
M-
50
ppm
P
3.84a
7.42a
O.Oa
O.Oa
18.ld
9.Oa
*Values are means of 5 replicates (90 days) and 4 replicates (180 days).
Means in the same column followed by at least one of the same letters are
not significantly different at the 0.05 level of probability.
Infection levels in western wheatgrass exhibited the same trends as
seen in green needlegrass, with infect.ion being inhibited by the addition of
phosphate fertilizer. At 90 days the M+, no phosphorus treatment exhibited
a 35 percent increase over the M-, no phosphorus treatment. This treatment
was also significantly greater than t.he medium phosphorus M+ and M- treat-
ments. At 180 days, however, there were no significant differences between
those treatments (Table 4).
The analysis of plant material for phosphorus revealed that the
presence of mycorrhizae neither increased nor decreased the amount of phos-
phorus in the plants (Tables 3 and 4.). The results were quite consistent
for both grasses. Generally, an increase in pg phosphorus/g of plant mater-
ial was correlated with increased rates of phosphate fertilization. Sig-
nificantly greater amounts of plant tissue phosphorus were present in the
high phosphorus regimes than in the ow phosphorus regimes with the medium
phosphorus regimes being intermediate to both. At 180 days green needle-
grass did not show significant differences in the amount of phosphorus
present in plant tissue of any treatm€nts.
560
-------
TABLE 4. THE EFFECTS OF PHOSPHATE FERTILIZATION ON SHOOT DRY WEIGHT,
P CONCENTRATION AND ROOT INFECTION LEVELS IN WESTERN WHEATGRASS
(A. smithii)*
Weight
(g)
% Infection
ug
P/a Plant Matter
90
days
180 days
90 days
180 days
90 days
180
days
No P 7.05a 12.36ab 40.8c 75.Od 11.2ab 8.Oab
No P 5.21a 13.50b O.2a O.Oa 9.6a 7.la
25 ppm P 5.46a 13.56b 22.6b 41.Ob 13.8bc 8.5ab
N—
25 ppm P 5.35a 12.Olab O.Oa O.3a 12.8abc 7.6ab
50 ppm P 6.O5ab 11.36a 5.4a 52.5c 14.7c 9.9c
50 ppm P 6.O8ab 11.13a 0.Oa O.Oa 16.Oc 9.Obc
*Values are means of 5 replicates (90 days) and 4 replicates (180 days).
Means in the same column followed by at least one of the same letters are
not significantly different at the 0.05 level of probability.
Big sage ( Artemisia tridentata ) was the only one of the five species
tested to exhibit growth responses typically observed in VA endosymbiont
associations. An increase in shoot dry weight and height was accompanied by
increased phosphate uptake in big sage plants subjected to the low phosphor-
us regime. A lack of such fungus-induced responses was evident at higher
soil phosphorus concentrations. Table 5 points out that all parameters
measured were significantly greater in the M+ versus the M- treatment where
no fertilizer was applied to the soil. The increase in weight, height and
phosphorus between M+ and M- treatments at the No phosphorus level were
readily evident visually. Weight was increased 143 percent, height 81
percent and phosphorus content 51 percent.
DISCUSSION
At least two explanations exist for the lack of mycorrhizal infection
development in fourwing saitbush and winterfat. The first and most obvious
is that the wrong species of fungus was used as inoculum. This is very
likely as a certain amount of host/symbiont specificity is known to occur.
561
-------
TABLE 5. THE EFFECTS OF PHOSPHATE FERTILIZATION ON SHOOT DRY WEIGHT,
P CONCENTRATION AND ROOT INFECTION LEVELS IN BIG SAGE
( A. tridentata)*
Weight
(g)
Artemi sia
Height
(cm)
Tridentata
pg P/g
Plant Mat’
1
%
Infection
M+
No
P
163b
1O.llb
58.lbc
62.5d
M-
No
P
O.67a
5.60a
38.6a
0.Oa
M+
25
ppm
P
1.l6ab
8.83b
69.9cd
40.8d
M-
25
ppm
P
1.51b
9.84b
68.4bcd
O.Oa
M+
50
ppm
P
1.32ab
9.63b
57.lb
11.Ob
N-
50
ppm
P
1.l7ab
7.97ab
70.4d
O.Oa
*Values are means of 8 replicates. Means in the same column followed by
at least one of the same letters are not significantly different at 0.05
level of probability.
Lindsey, Cress and Aldon’ 6 found no infection in fourwing saltbush inocu-
lated with G. fasciculatus . G. mosseae was the endosymbiont found by
Williams and Aldon’ 2 to increase growth of fourwing saltbush. A second
explanation has to do with a host plant mechanism suggested by Woolhouse’ 7
which might allow the plant to guard against infection by VA mycorrhizae
when phosphate is adequate. Since neither species responded to the addition
of phosphate, this, too, may explain the lack of infection in fourwing and
winterfat. Whatever the case, more studies need to be carried out before
anything definitive may be concluded as to the nature of the relationship
between mycorrhizal fungi and members Chenopodiaceae.
Green needlegrass and western wheatgrass developed mycorrhizal infec-
tions, but no persistent increases in biomass occurred. It would appear
that endosymbiont effectiveness rather than specificity was the critical
factor in the grasses tested. Although the fungus did not aid the grasses
in the uptake of phosphate, the relationship did not degenerate to one of
host/pathogen. Thus, although plant growth was not increased, neither was
562
-------
it decreased by the presence of G. fasciculatus . Certain interactions
between the grass plants and the fungus remain unexplained and will require
more research. The possibility exists that the fungus actually increased
root biomass. Since separation of very fine roots from the compacted soil
was not effected, any measurement of root biomass would have provided a very
inaccurate approximation. Therefore, none was attempted.
Significant increases in biomass, height and phosphorus content were
observed in the M+, low phosphorus treatment of big sage. The strain of
endosymbiont with which all species were inoculated was originally taken
from soil beneath big sage. This may account for the fact that the only
persistant pos-itive response to VA endosymbiont infection was exhibited by
big sage. The general consensus of research done in recent years is that
certain endosymbiont strains are indeed more effective in phosphorus uptake
(and thus growth stimulation) than others (Jackson, Franklin and Miller;’ 8
Mosse;’ 9 Powell 20 21). The specificity and varying effectiveness of fungal
endosymbionts found in association with native plants may therefore regulate
the extent of plant growth enhancement and nutrient uptake.
The findings of this study do not conclusively prove that VA mycor-
rhizae are essential to the establishment of stable ecosystems, but it is
essential that microbiological aspects of ecosystems be evaluated along with
aboveground vegetation. Without such evaluations reestablishment of truly
stable ecosystems may never be assured. More studies of this nature may
reveal the presence of a variety of endosymbiotic fungi which infect a
variety of species. Should this prove to be the case, the need for phos-
phate fertilization on disturbed areas could be greatly reduced by intensive
soil management and maintenance of mycorrhizal populations.
REFERENCES
1. Safir, G.R. , J.S. Boyer and J.D. Gerdemann. Nutrient Status and Mycor-
rhizal Enhancement of Water Transport in Soybeans. Plant Physiol. 49:
700-703. 1972.
2. Moorman, LB. and F.B. Reeves. The Role of Endomycorrhizae in Revege-
tation Practices in the Semiarid West. II. A Bioassay to Determine
the Effect of Land Disturbane on Endomycorrhizal Populations. Am. J.
Bot. 66: 14-18, January 1979.
3. Reeves, F.B. , 0. Wagner, I. Moorman and J Kiel. The Role of Endomycor-
rhizae in Revegetation Practices in the Semiarid West. I. A Compari-
son of Incidence of Mycorrhizae in Severely Disturbed vs. Natural
Environments. Am. J. Bot. 66: 6-13, January 1979.
4. Daft, M.J. and T.H. Nicolson. Arbuscular Mycorrhizas in Plants Colo-
nizing Coal Wastes in Scotland. New Phytol. 73: 1129-38, 1974.
5. Daft, P4.3., E. Hacskaylo and T.H. Nicolson. Arbuscular Mycorrhizas in
Plants Colonizing Coal Spoils in Scotland and Pennsylvania. In:
563
-------
Endomycorrhizas, F.E. Sanders, B. Masse and P.8. Tinker (eds.).
Academic Press, London, New York, San Francisco. 1975. p. 561-580.
6. Marx, D.H. Mycorrhizae and Establishment of Trees on Strip Mined Land.
Ohio 3. Sci. 75: 288-297, 1975.
7. Gerdemann, J.W. Vesicular-Arbuscular Mycorrhiza and Plant Growth.
Arinu. Rev. Phytopathol. 6: 397-418, 1968.
8. Nicolson, T.H. Vesicular—Arbuscular Mycorrhiza--A Universal Plant
Symbiosis. Sd. Prog., oxf. 55: 561-581, 1967.
9. Masse, B. Advances in the Study of Vesicular-Arbuscular Mycorrhiza.
Annu Rev. Phytopathol. 11: 171-196, 1973.
10. Williams, S.E., A.G. Wollum, II and E.F. Aldon. Growth of Atriplex
canescens (Pursh) Nutt. Improved by Formation of Vesicular-Arbuscular
Mycorrhizae. Soil Sd. Soc. Amer. Proc. 38: 962-965, 1974.
11. Aldori, E.F. Endomycorrhizae Enhance Survival and Growth of Fourwing
Saitbush on Coal Mine Spoils. USDA For. Serv. Res. Note RM-294, 1975.
12. Williams, S.E. and E.F. Aldon. Endornycorrhiza] Associations of Some
Arid Zone Shrubs. Southwest Nat. 20: 537-444, 1976.
13. Phillips, J.M. and 0.5. Hayman. Improved Procedures for Clearing Roots
and Staining Parasitic and Vesicular-Arbuscular Mycorrhizal Fungi for
Rapid Assessment of Infection. Trans. Br. Mycol. Sec. 55:. 158-160,
1970.
14. Olsen, S.R. and L.A. Dean. Phosphorus. In: Methods of Soil Analysis,
Part 2, No. 9. C.A. Black (ed.). Am. Soc. Agron. Madison, Wisconsin.
1965. p. 1036-1037.
15. Jackson, &L. Soil Chemical Analysis. Prentice-Hall, Inc. Englewood
Cliffs, New Jersey. 1958.
16. Lindsey, D.L. , W.A. Cress and E.F. Aldon. The Effects of Endomycor—
rhizal on Growth of Rabbitbrush, Fourwing Saitbush and Corn in Coal
Mine Spoil Material. U.S.D.A. t:or. Serv. Res. Note RM-343, 1977.
17. Woolhouse, H.W. Membrane Structure and Transport Problems Considered
in Relation to Phosphorus and Carbohydrate Movements and the Regulation
of Endotrophic Mycorrhizal Associations. In: Endomycorrhizas, F.E.
Sanders, B. Mosse and P.B. Tinker (eds.). Academic Press, London, New
York, San Francisco. 1975. p. 209-239.
18. Jackson, N.E., R.E. Franklin and R.H. Miller. Effects of Vesicular-
Arbuscular Mycorrhizae on Growth and Phosphorus Content of Three
Agronomic Crops. Soil Sd. Am. Proc. 36: 64-67, 1972.
564
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19. Mosse, B. Specificity in VA Myc:orrhizas. In: Endomycorrhizas, F.E.
Sanders, B. Mosse and P.B. Tinker (eds.). Academic Press, London, New
York, San Francisco. 1975. p. 469-484.
20. Powell, C.L. I. Mycorrhizas in Hill-Country Soils. II. Effects of
Several Mycorrhizal Fungi on Clover Growth in Sterilized Soils. N.Z.
Journal of Ag. Res. 20: 59-62, 1977a.
21. Powell, C.L. I. Mycorrhizas in Hill-Country Soils. III. Effect of
Inoculation on Clover Growth in Unsterile Soils. N.Z. Journal Ag. Res.
20: 343-348, 1977b.
565
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THE EFFECT OF RETORTED OIL SHALE ON VA MYCORRIIIZA FORMATION
IN SOIL FROM THE PICEANCE B(iSIN OF NORTHWESTERN COLORADO
Suzanne Schwab and F. Brent Reeves
Department of Botany and Plant Pathology
Colorado State University
Fort Collins, Colorado 80523
INTRODUCTION
Processing of oil shale in northwestern Colorado for recovery of fuel
oil will result in the generation of vast amounts of waste retorted shale.
Cundell’ estimated that the minimum sized economically feasible shale plant
would generate aoout 50,000 tons of waste shale per day. Although improved
technology may reduce this figure, the disposal of waste shale will present
a major problem in reclamation since these wastes have a number of proper-
ties detrimental to plant growth. For example, these wastes are highly
alkaline (pH = 9 - 10), are deficient in nitrogen and phosphorus, and have a
very high sodium content (Schmell arid McCaslin, 2 Cundell,’ Redente 3 ). In
addition most of the heavy metals contained in oil shale are retained in the
solid wastes (Shendrikar 4 ), and the fine texture of the wastes is unfavor-
able to water infiltration and aeratic.n.
Schmell and McCaslin 2 have shown that when oil shale wastes comprised
liore than 50% of a soil-shale mixture, growth of Russian wild rye ( Elymus
junceus ) and Alkar tall wheat grass ( Agropyron elongatum ) was severly
reduced. Microbial activity, as measured by ATP concentrations and by
nonsymbiotic nitrogen fixation, has also been shown to be reduced by
increasing amounts of retorted oil shale added to soil (Klein 5 ). Although
the activity o both ecto-and endo-mycorrhizal fungi has been investigated
on coal wastes (Schram, 6 Daft and Nicholson, 7 Daft, Hacskaylo, and
Nicholson, 8 Marx, 9 Khan 10 ) little is known about the effects of oil shale
wastes on mycorrhiza formation. Since the presence of mycorrhizal associa-
tions has been shown to be beneficial to the growth of many plant species
(Gerdemann, 1 ’ Flosse 12 ), including species native to northwestern Colorado
(Aldon,’ 3 Kiel 14 ), and appears to be an important factor in revegetation of
disturbed lands (Reeves et al., 15 Moorman and Reeves 16 ) the effectof adding
oil shale wastes to topsoil on mycorrhiza formation must be considered.
This paper reports the results of experiments designed to test the effect of
retorted oil shale amendments to torsoil on the formation of mycorrhizae
using corn as a test plant.
566
-------
METHODS
Paraho retorted oil shale used in this study was obtained from oil
shale succession plots in the Piceance Basin in Rio Blanco County and the
topsoil tested was collected from the surrounding midelevation sage communi-
ty. Collections for the two replicates used in this study were made in June
and October, 1978.
For each replicate, mixtures of soil and retorted shale were prepared
in the following V:V proportions: 10, 25, 50, and 75% retorted shale along
with controls of 100% retorted shale and 100% soil. Since retorted oil
shale contains no viable propagules of mycorrhizal fungi it can affect
mycorrhiza formation when added to the soil in two ways: (1) by acting as a
dilutant of propagules present in the soil and, (2) by physically and/or
chemically altering the soil. To separate the physical/chemical effects
from dilution effects, mixtures of soil and sand in proportions correspond-
ing to the soil-shale proportions were prepared. Each mixture was then used
to fill five cisinfected 400 ml patE. Surface sterilized corn seeds were
planted in each pot and grown in the greenhouse for 30 days. At the end of
t.nis growing period the corn plants were uprooted and 100 1 cm sections of
each root system were randomly selected, stained, and assessed for relative
degree of mycorrhizal infection following the method of Phillips and
Hayman. 17
Comparisons were made of average percent infection in each treatment
using a two-way analysis of variance for each replicate and for the combined
data of the two replicates.
In a second set of experiments oil shale was mixed with autoclaved
topsoil in the same proportions as in the previous experiment. Equal vol-
umes of sand from pot cultures of a mycorrhizal syrnbiont ( Glornus
fasciculatus ) on corn were then addea to each mixture to serve as an inocu-
lum of mycorrhizal fungi. Since equal inoculum was added to each treatment
in this study, any change in rnycorrhiza formation could be attributed to
properties of the shale se rather than dilution. Four corn plants were
grown in each mixture for 30 days in the greenhouse, then uprooted and
assessed for relative amounts of mycorrhizal infection in each treatment, as
in the previouE experiment. Two replicates were done of this study, one in
November, 1978, and the second in February, 1979, both using soil and shale
collected in October, 1978.
RESULTS
The average percent infection and range of percent infection for the
five plants in each treatment in b3th replicates are shown in Table 1.
Mixtures of up to 25% sand or oil shale did not decrease mycorrhizal infec-
tion compared to the undiluted soil. As the amount of amendment increased
beyond 25% however, the average amount of rnycorrhiza formation decreased,
with the oil shale mixtures decreasing more rapidly than the sand mixtures.
In the first replicate the percent infection for both the sand and the oil
shale mixtures decreased significantly at a = 0.1 as the percent amendmer t
-------
TABLE 1. PERCENT VA MYCORRHIZAL INFECTION IN CORN ROOTS
GROWN IN SOIL-SAND OR SOIL-OIL SHALE MIXTURES
Percent
Amendment
First
Replicate
Percent Infection
Sand
Oil
Shale
Ave.
%
Range
%
Ave.
%
Range
%
10
25
50
75
100
None
91
93
60
47
0
Ave.
87%
84-98
84-100
48-70
42-52
0
Range:
93
86
46
18
0
72-96%
88-100
80-92
0-88
12-24
0
Second
Replicate
Percent
Amendment
Percent Infection
Sand
Oil
Shale
Ave.
%
Range
%
Ave.
%
Range
%
10
25
50
75
100
None
66
64
69
37
0
Ave.
=
78%
61-75
55-75
61-76
24-53
0
Range:
93
83
49
13
0
57-88%
84-97
75-90
11-69
4-24
0
increased but there was no significant difference between the sand and the
oil shale amended soils. In the second replicate both the 50% and the 75%
oil shale mixtures showed significantly less infection than the unamended
soil at a 0.05, while the sand mixtures showed no significant decrease
until sand comprised 75% or more of the mixture. Also of interest is the
great variation in percent infection in the five plants in each of the 50%
oil shale treatments compared to the other treatments. The comparatively
wide range of values at this point may indicate that 50% oil shale was near
the threshold of tolerance for mycorrhiza formation so that small variatior s
568
-------
between samples at this point created large differences in mycorrhiza forma-
tion.
Figures 1 and 2 show the results of this experiment graphically.
Figure 1 shows the percent infection plotted against percent amendment for
the individual replicates, and Figure 2 shows the averages of the two repli-
cates combined. Both figures show little effect of dilution by sand on
mycorrhiza formation until it exceeds 25% to 50% of the substrate. The
decrease in mycorrhiza formation is more pronounced when retorted oil shale
is added to soil than when an equal volume of sand is added, indicating that
the effect of retorted oil shale canr,ot be attributed solely to dilution of
propagules.
The results of the second study, in which sterile mixtures were reinoc-
ulated with equal amounts of propagules, are shown in Table 2 and Figure 3.
In both replicates mycorrhiza formation was inhibited when retorted oil
shale comprised more than 50% of the substrate. However, the mixtures with
lesser amounts of oil shale gave more equivocal results. In the first
replicate small amounts of retorted shale appeared to enhance mycorrhiza
formation, while the second replicate did not show this effect. Because of
these differences at low concentrations of retorted shale, one way analysis
of variance failed to show significant decreases in mycorrhiza formation as
the concentration of oil shale increased. However, the linear correlation
between percent infection and percent retorted oil shale of -0.52 was sig-
nificant at = 0.01, indicating that there is some relationship between the
two factors.
TABLE 2. PERCENT VA MYCORRHIZAL INFECTION IN CORN ROOTS
GROWN IN REINOCULATED AUTOCLAVED SOIL-OIL SHALE MIXTURES
Percent
Amendment
Percent Infection
Rep.
1
Rep.
2
Ave.
%
Range
%
Ave.
%
Range
%
0
33
11-48
38
14-50
10
55
44-65
27
19-33
25
46
25-61
39
26-63
50
36
23-60
17
15-20
75
17
1-51
6
4-10
Uninoc.
0
0
0
0
Soil
Unioc.
0
0
0
0
Oil Shale
569
-------
100'
75
u
UJ
- 50
tu
o
oc
25
0
T
•f' *
0 10
REP 1
V
~ Oi! Shjle
•••••Sand
_L
J.
25 50
PERCENT AMENDMENT
75
100'
z 75
g
$-
UJ
u.
- 50
i-
z
UJ
U
ec
yj
Q- 25
0
1 r i i •
^ REP 2 —- OH Shale
€ "\
""•-.... "x •••••••.
i ^
$t i i
0 10 25 50 /b
figure I;
PERCENT AMENDMENT
Percent VA my'-orrhiza! infection in corn roots
grown in soil amended with various amounts of
retorted oil shale or sand.
570
-------
Figure 2.
100
O
t-
u
LL)
u
cc
£25
0
L.
Oil Shale
Sand
\.
\ '-I
0 10 25 50 75
PERCENT AMENDMENT
Percent VA mycorrhizal infection in corn roots
grown in soil amended with various amounts of
sand or retorted oil shale, averages and ranges
of two replicates
571
-------
100
75 —
O
-50
-25
REP 1
/ *"""'*--
~^-,
' -—
/ "^
0 10
25 50
PERCENT OIL SHALE
75
75
50
25
"T
REP 2
O 10
50
t NT OIL SHALE
75
Figure 3. Pefcent VA inyco! f hi/al :!ift>c!ion m corn tuois
()rown in autocijvet! -.011 amended with vnriOiJ
amounts of feiorU'd ml sf'mle ,md reirtoculiiie d
wild equai amour,!-, of propaguies of mvcorrhsz
f unq i
572
-------
DISCUSSION
Mycorrhizae have been shown to be an integral part of virtually all
plant communities (Mosse,’ 2 Gerdeniann 1 ’) and reduction in the potential of
the soil to form these associations due to various types of disturbance has
been shown to be correlated with often undesirable changes in vegetation
(Reeves et al. Moorman and Reeves 16 ). The data presented in this paper
suggest that mixing large quantities of untreated retorted oil shale with
topsoil can reduce the formation of mycorrhizal associations in plants
growing in that soil. Further studies to determine how various treatments
of shale can affect mycorrhiza formation should provide valuable information
for revegetation of lands involved in retorted shale disposal. For example,
we are currently monitoring a series of oil shale succession plots to deter-
mine how burying shale under different depths of soil will affect mycorrhiza
formation. Other studies on shale that has been leached or otherwise treat-
ed could also provide useful data.
In addition, long term studies on the effect of oil shale wastes on
mycorrhizal activity should be initiated. Presently there are many unan-
swered questions about the nature of the inhibitory effects of retorted
shale on mycorrhiza formation. For example, we do not know if factors in
the shale kill the fungal syrubiont or merely retard its growth, nor do we
know if the inhibition of mycorrhiza formation can be overcome by increasing
inoculum density.
The composition and activity of the soil microflora has been shown to
be influenced by soil pH, water potential, organic matter, degree of distur-
bance, and presence of heavy metals (Brown,’ 8 Griffin, 19 Wei-chu and
Griffin, 20 Ruhling and Tyler, 2 ’ Jordan and LeChevalier, 22 Lawrey 23 ) and
selection of microbial strains tolerant to heavy metals has been observed in
the lab and in nature (Ross, 24 Jordan and LeChevalier 22 ). Some evidence of
different strains or species of mycorrhizal fungi being limited to specific
soils also exists (Worley and Hacskaylo, 25 Mexal and Reid, 26 Daft and
Nicholson, 7 Kruckelmann, 27 Abbot and Tobson, 28 Johnson, 29 Powell 30 ).
Changes in soil properties due to addition of oil shale wastes could,
therefore, lead to selection of certain tolerant strains and elimination of
other strains. Since some host specificity has been observed with mycor-
rhizal fungi (Mosse 31 ) selection of certain strains of fungi could influence
which host plants can become successfully established on lands affected by
oil shale waste disposal.
All of these problems offer fruitful areas for further research. Such
research will require carefully controlled means of comparing mycorrhizal
populations in various soils. The type of bioassay used in this study and
described in the previous paper offers a relatively easy and fast means of
comparing the mycorrhizal potential of different soils or treatments.
However, work in our lab and others suggests that light intensity and quali-
ty, temperature, and watering regimes can affect the results of a bioassay
of this type. Therefore, it is essential that bioassays for mycorrhizal
potential be done under standardized, defined, and easily reproducible
conditions. Research is currently underway in our lab to better determine
573
-------
how various environmental variables can affect the results of mycorrhizal
bioassays.
AC KNOWLEDGEMENTS
This work was supported by DOE Contract No. EY76-S-02-4018.
REFERENCES
1. Cundell, A.M. The role of microorganisms in revegetation of strip
mined lands in the western United States. J. Range Mgt. 30:299-305,
1977.
2. Schmell, W.R. and 8.0. McCaslin.
significant to plant growth. In:
ed land, Hutnik, R.J. and Davis,
Breach, New York. 1973.
Some properties of spent oil shale
Ecology and reclamation of devastat-
G. (eds.). Vol. 1. Gordon and
3. Redente, E. Effects of plant species, soil material, and cultural
practices upon plant establishment and succession. Rehabilitation
potential and practices of Colorado oil shale lands. Progress Report,
Dept. of Range Science, Colorado State University. 1978.
4. Shendrikar, .D. and G.B. Faudel. Distribution of trace metals during
oil shale retorting. Envir. Sci. Tech. 12:332-334, 1978.
5. Klein, D.A. Role of soil microorganisms as indicators and possible
controlling factors in plant succession processes on retorted shale and
distrubed soils. Rehabilitation potential and practices of Colorado
oil shale lands, Progress Report. Dept. of Range Science, Colorado
State University. 1978.
6. Schram, J.R. Plant colonization studies on black wastes of anthracite
mining in Pennsylvania. Trans. Am. Philos. Soc. 56:1-194, 1966.
7. Daft, M.J. and T.H. Nicholson.
izing coal wastes in Scotland.
8. Daft, N.J., E. Hacskaylo,
in plants colonizing coal
Endomycorrhizas, Sanders,
Academic Press, New York.
Arbuscular mycorrhizas in plants colon-
New Phytol. 73:1129-1138, 1974.
and T.H. Nicholson. Arbuscular mycorrhizas
spoils in Scotland and Pennsylvania. In:
F.E., Mosse, B., and Tinker, P.B. (eds.).
1975.
9. Marx, D.H. Mycorrhizae and establishment of trees on strip mined land.
Ohio J. Sci. 75:288-297, 1951.
10. Khan, A.G. Vesicular-arbuscular mycorrhizas in plants colonizing black
wastes from bituminous coal mining wastes in the Illawarra region of
New South Wales. New Phytol. 81:53-63, 1978.
574
-------
11. Gerdemann, J.W. Vesicular-arbuscular mycorrhizae. In: The Develop-
ment and Function of Roots, Torrey, J.G. and Clarkson, D.T. (eds.).
Academic Press, New York. 1975. p. 575-591.
12. Mosse, B. Advances in the study of vesicular-arbuscular mycorrhiza.
Ann. Rev. Phytopathol. 11:171-196, 1973.
13. Aldon, E.F. Endomycorrhizae enhance survival and growth of fourwing
saltbush on coal mine spoils. USDA Forest Service Research Note
RM-294. 1975.
14. Kiel, J. Soil phosphorus effect on growth and endomycorrhizal develop-
ment in native plants. Master’s Thesis. Colorado State University,
Fort Collins. 1978.
15. Reeves, F.B. , D. Wagner, 1. Moorman, and J. Kiel. The role of endomy-
corrhizae in revegetation practices in the semiarid west. I. A
comparison of incidnece of mycorrhizae in severely disturbed vs.
natural environments. Amer. 3. Bot. 66:6-13, 1979.
16. Moorman, T.B. and F.B. Reeves. The role of endornycorrhizae in revege-
tatiori practices in the semiarid west. II. A bioassay to determine
the effect of land disturbance on endomycorrhizal populations. Amer.
J. Bot. 66:14-18, 1979.
17. Phillips, J.M. and D.S. Hayman. Improved procedures for clearing roots
and staining parasitic and vesicular-arbuscular mycorrhizal fungi for
rapid assessment of infection. Trans. Br. Mycol. Soc. 55:158-160,
1970.
18. Brown, J.C. Soil fungi of some British sand dunes in relation to soil
type and succession. J. Ecol. 46:641-664, 1958.
19. Griffin, D.M. Soil physical factors and the ecology of fungi. III.
Activity of fungi in relatively dry soils. Trans. Br. Mycol. Soc.
46:373-377, 1963.
20. Wei-Chu, A. and D.M. Griffin. Soil physical factors and the ecology of
soil fungi. III. Further studies in relatively dry soil. Trans. Br.
Mycol. Soc. 49:419-426, 1963.
21. Ruhling, A. and G. Tyler. Heavy metal pollution and decomposiiton of
spruce needle litter. Oikos 24:402-416, 1973.
22. Jordan, M.J. and M.P. LeChevalier. Effects of zinc smelter emissions
on forest soil microflora. Can. 3. Microbiol. 21:1855-1865, 1975.
23. Lawrey, 3.0. The relative decomposition potential of habitats various-
ly affected by surface coal mining. Can. J. Bot. 55:1544-1552, 1977.
575
-------
24. Ross, I.S. Some effects of heavy metals on fungal cells. Trans. Br.
Mycol. Soc. 64:175-193, 1975.
25. Worley, J.F. and E. Hacskaylo. The effect of available soil moisture
on the mycorrhizal association of Virginia Pine. For. Sd. 59:267-269,
1959.
26. Mexal, J. and C.P.P. Reid. The growth of selected mycorrhizal fungi in
response to induced water stress. Can. J. Bot. 51:1579-1588, 1973.
27. Kruckelmann, H.W. Effects of fertilizers, soils, soil tillage, and
plant species on the frequency of Endogone chiamydospores and mycor-
rhizal infection in arable soils. In: Endomycorrhizas, Sanders, F.E.,
Mosse, B. and Tinker, P.B. (eds.). Academic Press, New York. 1975.
28. Abbot, L.K. and A.D. Robson. The distribution and abundance of
vesicular-arbuscular endophytes in some western Austrailian soils.
Aust. J. Bot. 25:512-522, 1977.
29. Johnson, P.N. Mycorrhizal Endogonaceae in a New Zealand forest. New
Phytol. 78:161-170, 1977.
30. Powell, C.L. Mycorrhizas in hill-country soils. I. Spore bearing
mycorrhizal fungi in thirty-seven soils. N.Z. Jour. Agric. Res.
20:53-57, 1977.
576
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APPENDIX A
ABOUT THE AUTHORS
William Barkley
B.S. in Biology from Kentucky State University; graduate work in Indus-
trial Hygiene and Toxicology at the University of Cincinnati, Dept. of
Environmental Health where he is presently a Senior Research Associate
in Research Toxicology.
Wesley J. Birge
B.A. in Biology, 1951 from Eastern Washington State College; M.S. in
Zoology, 1953 from Oregon State University; Ph.D. in Zoology, 1955 from
Oregon State University. Presently Professor of Biology and Toxicology
at University of Kentucky, Lexington, Kentucky. Areas of specializa-
tion: Developmental Biology and Aquatic Toxicology. Served on the
recent interagency (DOE, EPA, HEW) panel on the Health and Environment-
al Effects of Coal Gasification and Liquefaction.
Mary A. Caolo
Research Associate, University of Colorado Chemistry Department.
Dr. Jim Carley
Cornell graduate; taught Engineering for 12 years at the University of
Colorado where his research was primarily concerned with polymers.
Since 1976 he has worked on the Oil Shale Project at Lawrence Livermore
Laboratory. The Project’s mission is to develop the technical base for
modified in situ retorting of oil shale.
Reed Clayson
graduated from Utah State University with degrees in physics and jour-
nalism; he -is presently manager of resource analysis for Science Appli-
cations, Inc. and is currently involved in the analysis and automation
of the oil shale regulatory system.
J.G. Dickson
Utah State University, Department of Civil and Environmental Engineer-
ing.
Morris Engelke, Jr.
USGS Biologist, Cheyenne, Wyoming.
U.S. Earner
Laramie Energy Technology Center, manager of oil shale environmental
studies.
577
-------
Phyllis Fox
Manager of Oil Shale Program at Lawrence Berkeley Laboratory.
J.S. Fruchter
Senior Research Scientist, Battelle Pacific Northwest Laboratory.
Dr. Santosh Gangwal
Chemical engineer; 1977 to present--Research Triangle Institute working
on the environmental assessment of coal gasification, trace analysis
and sampling systems design.
W. Kennedy Gauger
University of Wyoming, Plant Science Department.
D.G. Giruin
Lawrence Berkeley Laboratory, Energy and Environment Division.
Peter Haug
A.B. in English Literature from Hamilton College; M.S. in Wildlife
Biology from Colorado State University; Ph.D. in Systems Ecology from
Colorado State University. Worked as the senior scientist at ERT/Ecol-
ogy Consultants, Inc. for two years where he developed the Oilshale
Tract C-b Conceptual model. Currently working as a systems ecologist
for the Bureau of Land Management.
R.N. “Bob” Heistand
B.S. in Chemistry at Franklin and Marshall College and a master’s
degree in chemistry at Niagara University. Presently manager of engi-
neering and research for Development Engineering, Inc. (DEl). DEl is
the operating company that has been directing research operations for
Paraho Development Corp. at Anvil Points for the past five years.
Larry Hilpert
currently a research chemist with the Organic Analytical Research
Division of the National Bureau of Standards; is responsible for GC-MS
analyses of environmental samples. His particular interest is the
accurate quantitation of individual organic compounds in complex
matrices by GC-MS.
J.M. Holland
degree in Veterinary Medicine from Kansas State University and a Ph.D.
in Veterinary Pathology and Biochemistry from Washington State Univer-
sity. Since 1972, he has served as a scientific staff member and group
leader within the biology division of Oak Ridge National Laboratory,
Oak Ridge, Tennessee.
Jean Kiel
Ecologist, Environmental Sciences Division, Stearns-Roger, Inc.
578
-------
Wesley L. Kinney
Aquatic Biologist, USEPA Environmental Monitoring and Support Labora-
tory, Las Vegas, Nevada.
Don A. Klein
Professor, Microbiology Department, Colorado State University.
Ronald W. Kiusman
B.S. in Chemistry from Indiana University; Ph.D. in Geology from
Indiana University. Currently Professor of Geochemistry at Colorado
School of Mines. Research: oil shale, water quality, geothermal
exploration and earthquake prediction.
Jerry A. Leenheer
Hydrologist, USGS Water Resources Division.
David L. Maase
B.S. in Civil Engineering from Texas Tech. University in 1970; MS. in
Water Resources from University of Cincinnati in 1970. Worked at the
Battelle Columbus Laboratory for five years. Presently working on his
Ph.D. in Environmental Engineering at Utah State University.
Robert Meglen
B.S. from Iowa State; Ph.D. from University of Colorado. Director of
the Analytical Laboratory of the Environmental Trace Substances
Research Program of the University of Colorado.
Paul E. Mills
B.S. in Biochemistry from Michigan State University; graduate work in
Microbiology at Michigan State; MBA, Management, Michigan State. Pre-
sently Quality Assurance Officer for U.S. Environmental Protection
Agency, Industrial Environmental Research Laboratory, Cincinnati.
Daniel A. Netzel
B.S. in Chemistry from University of Illinois; Ph.D. in Physical-
Analytical from Northwestern University.
K.D. Pimentel
Engineer, Environmental Sciences Division, Lawrence Livermore Labora-
tories.
Dick Poulson
M.S. from University of California at Berkeley; Ph.D. in Physical
Chemistry from Michigan State University. Presently manager of the
Environmental Sciences Division of the U.S. Department of Energy,
Laramie Energy Technology Center.
T.K. Rao
Research Associate, Oak Ridge National Laboratory; research in environ-
mental mutagenesis in bacteria and mammalian cells. M.S. and Ph.D. in
Genetics from Florida State University.
579
-------
Steven Reznek
B.S. in Physics from M.I.T. in 1963; Ph.D. in Physics from M.I.T. in
1967. Employed at EPA for 6 years and National Commission on Water
Quality for 2 years.
Mr. Ira Rubin
Currently working at Oak Ridge National Laboratory, Analytical Chemis-
try Division, Bio/Organic Analysis Section.
Peter Russell
Biologist, Lawrence Berkeley Laboratory.
Dr. Thomas G. Sanders
Bachelor of Engineering in Civil Engineering from Vanderbilt University
in 1966; M.S. in Civil Engineering from University of Massachusetts in
1968; Ph.D. in Civil Engineering from University of Massachusetts in
1974. Presently co-principal investigator at Colorado State Universi-
ty, U.S. Department of Transportation, Federal Highway Department,
Project DOT-FH-11-9159, “Hydrology Course for Transportation Engi-
neers.”
Suzanne Schwab
Department of Botany and Plant Pathology, Colorado State University.
David C. Sheesley
Bachelor’s degrees in Chemistry and Physics from Adams State College,
Colorado; graduate work at University of Colorado and Colorado State
University. Senior Scientist, Deputy Program Manager at Northrop
Services, Inc. , Environmental Sciences, Las Vegas, Nevada. Currently a
U.S. delegate for American National Standards Institute to TC146 on Air
Quality.
Douglas M. Skie
M.S. from South Dakota State University in 1971. Since 1975, Quality
Assurance Coordinator for Air and Water, EPA, Region VIII.
G.C. Slawson
Ph.D. in Hydrology from University of Arizona. Presently manager of
Water Resources Program, General Electric-TEMPO. Project manager for
TEMPO’s study for EPA dealing with monitoring of groundwater quality
impacts of oil shale development.
John Steinkamp
B.S.E.E. from Purdue University; M.S. and Ph.D. in Electrical!
Biomedical Engineering, Iowa State University. Present position:
Biomedical engineer, Biophysics and Instrumentation Group, Los Alamos
Scientific Laboratory, Los Alamos, New Mexico.
Agnes Stroud, Ph.D.
Biologist-Cytogeneticist doing research in the fields of environmental
toxicology and radiation biology at the Los Alamos Scientific Labora-
tory, Mammalian Biology Group, Los Alamos, New Mexico.
8O
-------
Terry L. Thoem
B.S. in Chemical Engineering from Iowa State University in 1967; M.S.
in Environmental Engineering from University of Washington in 1973.
Currently Acting Director, EPA ’s Energy Office, Region VIII involved in
oil shale, coal and uranium activities.
T. Wildeman
Ph.D. in Chemistry from University of Wisconsin. Professor of Chemis-
try and Geochemistry at Colorado School of Mines. Research interests:
Trace element chemistry and geochemistry.
John A. Winter
B.S. and M .S. in Microbiology from University of Wisconsin at Madison.
Presently Chief, Quality Assurance Branch of the Environmental Monitor-
ing and Support Laboratory of Cincinnati, EPA.
581
-------
APPENDIX B - LIST OF ATTENDEES
V. Dean Adams
Research Assoc. Professor
Utah Research Lab
Utah State University
Logan, UT 84322
L. E. Amick
Petroleum Engineer
Texaco, Inc.
Box 2100
Denver, CO 80201
Dennis Anderson
Senior Engineer
Water Quality Control Division
Colorado Department of Health
4210 East 11th Avenue
Denver, CO 80220
Steve Archer
Research Engineer
Contract Engineering
Monsanto Research Corporation
1515 Nicholas Road
Dayton, OH 45401
A. Attari
Associate Director
Chemical Research
Institute of Gas Technology
3424 South State Street
Chicago, IL 60616
Robert A. Atwood
Research Chemist
Development Engineering, Inc.
Box A, Anvil Points
Rifle, CC 81650
W. David Balfour
Radian Corporation
8500 Shoal Creek
P.O. Box 9948
Austin, TX 78766
William Barkley
Senior Research Associate
Department of Environmental Health
University of Cincinnati
College of Medicine
Kettering Lab
Cincinnati, OH 45267
Edward R. Bates
Physical Scientist
U.S. EPA
Extraction Technology Branch
5555 Ridge Avenue
Cincinnati, OH 45268
Ron Beck
Senior Ecologist
Energy Systems Division
Energy Resources Company
185 Alewife Bk. Parkway
Cambridge, MA 02134
James R. Beissel
Engineering Advisor
Synthetics
Carter Oil Company
P.O. Box 2180
Houston, TX 77001
William S. Bergen
Project Manager
Mobil Research & Development Corp.
P.O. Box 1026
I1
I I I I . I , •t# ., ,J—f .,
582
-------
Harold Bergmann
Assistant Professor
Zoology Department
University of Wyoming
Box 3166
University Station
Laramie, WY 82071
C. A. Bertelsen
Chevron Research Company
Box 1627
Richmond, CA 94802
Ugo Bilarzado
Professor, F.L. Mech.
Instituto d’ Arte Mineraria
Universita d’ Roma Italy
Rome, ITALY
W. J. Birge
University of Kentucky
T.H. Morgan School of
Biological Sciences
Lexington, KY 40504
Ronald H. Bissinger
Environmental Engineer
Environmental Sciences
Union Oil Company
461 South Boylston Street
Los Angeles, CA 90017
K. L. Blackburn
Toncologi st
HERL
U.S. EPA
26 West St. Clair
Cincinnati, OH 45202
Tom Braidech
Aquatic Biologist
Water Supply
U.S. EPA
Denver, CO 80208
Ray R. Bramhall
Assistant Director
Program Development
SRI International
1611 North Kent Street
Arlington, VA 22209
Charles B. Bray
Environmental Engineer
Environmental Services
Occidental Oil Shale, Inc.
Grand Junction, CO 81501
David L. Brenchley
Energy Systems
Battelle NW
P.O. Box 999
Richiand, WA 99352
Grayson C. Brown
APME, MDL
Pratt & Whitney
P.O. Box Z691
West Palm Beach, FL
J. Thomas Brownrigg
Senior Chemist
Baird Corporation
125 Middlesex Turnpike
Bedford, MD 01730
33401
583
-------
R 055 V. Bulkley
Utah Coop. Fish Research Unit
First & Wildlife Service
UMC 52
Utah State University
Logan, UT 84321
Eugene A. Burns
Program Manager
Chemistry and Chemical Engineering
Systems, Science and Software
P.O. Box 1620
LaJolla, CA 92033
Larry K. Burns
Associate Professor of Geology
Earth Resources
Colorado State university
Fort Collins, CO 80523
Ralph L. Campbell
Supervi sor
Petroleum Tech. Service
Standard Oil Company of Ohio
4440 Warrensvilie Center Road
Cleveland, OH 44128
Mary Ann Caolo
Research Associate
Chemistry Department
University of Colorado
Boulder, CO 80309
Jennings Capellen
Assistant, Chemistry
Ames Laboratory, DOE
Iowa State University
Ames, IA 50011
James F. Carley
Staff Scientist
Oil-Shale Project
Lawrence Livermore Laboratory
P.O. Box 808, L-207
Livermore, CA 94550
E. R. Carnahan
Cleveland Cliffs Iron Company
P.O. Box 1211
Rifle, CO 81650
Willard R. Chappell
Director, Environmental Trace
Substances Research Program
University of Colorado
Ekeley Chemistry M335
Campus Box 215
Boulder, CO 80309
Alden G. Christianson
U.S. EPA
IERL—Ci
5555 Ridge Avenue
Cincinnati, OH 45268
William Shelton Clark
President
SumX Corporation
Post Office Box 14864
Austin, TX 78761
Burnett W. Clay
Energy & Minerals
Bureau of Land Management
1600 Broadway, Room 700
Denver, CO 80202
584
-------
Reed Clayson
Manager, Resource Analysis
Science Applications Inc.
1546 Cole Boulevard, Suite 210
Golden, CO 80401
Henry F. Coffer
Presi dent
C. K. GeoEnergy Corporation
5030 Paradise Road, Suite A103
Las Vegas, NV 89119
David L. Coffin
Senior Science Advisor
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
Clarence D. Council
Sr. Environmental Specialist
Assessment & Integration
U.S. Department of Energy
P.O. Box 26247
Beimar Branch
Lakewood, CO 80227
A. S. Couper
Project Manager, R&D
Amoco Oil
P.O. Box 400
Naperville, IL
Nancy L. Couse
Assistant Professor
Biological Science
University of Denver
University Park
Denver, CO 80208
60540
E. J. Cokal
Staff Chemist
CMB-1
Los Alamos Scientific
Mail Stop 740
Los Alamos, NM 87545
Laboratory
Kenneth J. Covay
Hyarol ogi st
Department of Interior
U.S. Geological Survey
P.O. Box 810
Meeker, CO 81641
David Lee Cosgrove
Chemist
Department of Navy
DTNSRDC/A
Annapolis, MD
21402
Jack E. Cotter
Industry Programs Manager
TRW, R4’-2158
1 Space Park
Redondo Beach, CA 90278
Larry G. Cox
Ana1yti cal Chemist
Envi ronrnental
Colorado School of Mines
Research Institute
P.O. Box 112
Golden, CO 80401
Gregory Cresswell
Operations Manager
Environmental Sciences Division
Camp Dresser & Mckee
11455 West 48th Avenue
Wheat Ridge, CO 80033
585
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William J. Culbertson
Research Engineer
Denver Research Institute
University of Denver
University Park
Denver, CO 80208
Colbert Cushing
Senior Research Scientist
Ecosystems
Battelle-Pacific Northwest Lab
P.O. Box 999
Richiand, WA 99352
William A. Dark
Waters Associates
Maple Street
Milford, MA
01757
K. DeGraeve
Research Associate
Zoology Department
University of Wyoming
Laramie, WY 82070
Jean 1. Delaney
Research Engineer
Contract Engineering
Monsanto Research Corporation
Station B, Box 8
Dayton, OH 45407
0. W. Denney
Research Associate
Chemistry
University of Colorado
Boulder, CO 80309
Clyde J. Dial
Director, Program Operations
Office/IERL
U.S. EPA
5555 Ridge Avenue
Cincinnati, OH 45268
Judy G. Dickson
Civil & Environment Engineering
Utah State University
Logan, UT 84322
Evan Dildine
Technical Secretary
Cob. Water Pollution Control Comm.
Colorado Department of Health
4210 East Eleventh Avenue
Denver, CO 80220
Roy H. Drew
Geologist
Oil Shale Group
Bureau of Land Management
1600 Broadway, Room 600
Denver, CO 80202
Richard Dufford
Botony & Plant Pathology
Colorado State University
Fort Collins, CO 80521
William S. Dunn
Chief Chemist
Colorado State Health Dept.
4210 East 11th Avenue
Denver, CO 80220
586
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Richard Durand
Industrial Hygienist
MSHA, Rocky Mountain District
M/NM
P.O. Box 25367
Denver Federal Center
Lakewood, CO 80215
Robert Edwards
Baird Corporation
Bedford, MA 01730
Morris J. Engelke, Jr.
Biologist
U.S. Geological Survey
Water Resources Division
P.O. Box 1125
Cheyenne, WY 82001
Altay M. Ertugrul
Director
Environmental Sciences
Williams Brothers Engineering Company
6600 S. Yale Avenue
Tulsa, OK 74136
Ted Espinoza
Research Specialist
Denver Research Institute
Chemical Division
University of Denver
2390 South York
Denver, CO 80208
David S. Farrier
Laramie Energy Research Center
U.S. Department of Energy
P.O. Box 3395
University Station
Laramie, WY 82071
J. Phyllis Fox
Program Manager, Oil Shale
Energy & Environment
Lawrence Berkeley Laboratory
1 Cyclotron Road
Berkeley, CA 94704
Ralph E. Franklin
U.S. Department of Energy
Environmental Programs
Div. of Biomedical & Envir. Res.
Washington, DC 20545
Jonathan S. Fruchter
Senior Research Scientist
Physical Sciences
Battelle Northwest Lab
P.O. Box 999
Richiand, WA 99352
Chuck Gale
Denver Research Institute
University of Denver
2390 South York
Denver, CO 80208
Santosh K. Gangwal
Chemical Engineer
Research Triangle Institute
P.O. Box 12194
Research Triangle Park, NC
Thomas R. Garland
Research Specialist
Environmental Chemistry
Battelle Pacific Northwest Labs
P.O. Box 999
Richland, WA 99352
27709
587
-------
Darrel G. Garvis
Tech. Specialist
Environmental Science
Lawrence Livermore Laboratory
Livermore, CA 94550
Rosielea Gash
Director, Environmental Affairs
Rio Blanco Oil Shale Company
9725 East Hampden Avenue
Denver, CO 80013
W. Kennedy Gauger
Soil Microbiologist
Plant Science-Soils
University of Wyoming
P.O. Box 3354
University Station
Laramie, WY 82071
J. E. Gebhart
Gulf South Research Inst.
P .O. Box 26518
New Orleans, LA 70186
Linda Giering
Manager, Applied Research
Baird Corporation
125 Middlesex Turnpike
Bedford, MA 01730
Bill Gilgren
Manager
CDM/Accu- Labs
11485 West 48th Avenue
Wheatridge, CO 80033
Don C. Girvin
Energy & Enviro. Division
Lawrence Berkeley Lab
University of California
1 Cyclotron Road
Berkeley, CA 94720
Gerald Goldstein
U.S. Department of Energy
Office of Health & Env. Research
Room E218
20 Massachusetts Avenue
Washington, DC 20545
Sydney M. Gordon
Senior Chemist
Department of Chemistry
ITT Research Institute
10 West 35th Street
Chicago, IL 60607
Arthur H. Griffiths
Ecologist
Stearns-Roger
Box 5888
Denver, CO 80217
Robert J. Gunter
Industrial Hygienist
National Institute
Occupational Safety & Health
3024 Federal Office Building
19th and Stout Street
Denver, CO 80202
Nancy Gutschall
EEA, Inc.
1111 North 19th Street
Arlington, VA 22209
588
-------
Frank C. Haas
Research Group Leader, R&D
TOSCO Corporation
18200 W. Highway 72
Golden, CO 80401
Charles Habenicht
Research Special i St
Chemical Division
Denver Research Institute
University of Denver
2390 South York Street
Denver, CO 80208
Amelia A. Hagen
Manager, Env ironmental Acticities
TRW Energy Systems
7600 Coishire Drive
Room W1/2683
McLean, VA 22102
John A. Hartley
Consulting Geologist
Ammeralda Resources
7420 N. Dakin, Suite 302L
Denver, CO 80221
Peter T. Haug
System Ecologist
Div. of Env. & Planning Coord.
Bureau of Land Management
3825 East Mulberry
Fort Collins, CO 80524
James E. Hawkins
U.S. Bureau of Mines
Building 20
Denver Federal Center
Denver, CO 80225
Ben Harding
Researcher, Civil Engineering
University of Colorado
ECOT-4- 34
Boulder, CO
80302
Robert Heisler
Civil Engineer
Cleveland Cliffs
P.O. Box 1211
Rifle, CO 81650
Iron Company
Larry W. Harrington
Environmental Coordinator
DOE/Laramie Energy Research Ctr.
P.O. Box 3395
University Station
Laramie, WY 82071
Eugene F. Harris
Chief, Extraction Technology Branch
U.S EPA
I ERL
5555 Ridge Avenue
Cincinnati, OH 45268
Robert N. Heistand
Development Engineering, Inc.
Paraho
Box A
Anvil Points
Rifle, CO
81650
J. Herr
Black Prince Oil Shale Company
867 La Para
Palo Alto, CA 94306
589
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L. E. Hersman
Post Doctoral Fellow
Department of Microbiology
Colorado State University
Fort Collins, CO 80523
J. M. Holland
Pathologist (DVM)
Biology Division
Oak Ridge National Laboratory
Box V
Oak Ridge, TN 37830
Darryl L. Hessel
Manager, Env. Policy Analysis
Env., Health & Safety Program Office
Battelle NW
Battelle Boulevard
Richiand, WA 99352
Lawrence M. Holland
Health Division
Los Alamos Scientific
P.O. Box 1663
Los Alamos, NM 87545
Laboratory
Larry R. Hilpert
Research Chemist
Org. Anal. Res. Div.
National Bureau of Standards
Room A-105
Chemistry Building
Washington, DC 20234
Al Hodgson
Energy & Environment
Lawrence Berkeley Lab
1 Cyclotron Road
Berkeley, CA 94704
Dean C. Hoel
Technical Associate
Chemicals and Minerals
Gulf Science & Technology Company
P.O. Box 2038
Pittsburgh, PA 15230
Eric G. Hoffman
Environmental Spec. - Geology
U.S.G.S. Area Oil Shale Office
131 N. Sixth Street, Suite 300
Grand Junction, CO 81501
Arthur W. Hornig
Director of Research
Baird Corporation
125 Middlesex Turnpike
Bedford, MA 01730
Larry Hottenstein
Associate Project
TRC-Denver
8515 East Orchard Road
Suite 210
Englewood, CO 80111
Arthur W. Hounslow
Senior Project Mineralogist
Exp. and Mining
CSMRI
P.O. Box 112
Golden, CO 80401
Edward W. 0. Huffman, Jr.
President
Huffman Laboratories,
3830 High Court
P.O. Box 77U
Wheat Ridge, CO 80033
Scientist
Inc.
590
-------
Charles Hughes
Multi Mineral Corporation
330 North Belt, Suite 200
Houston, TX 77060
W. David Hughes
Director-Technical Services
Cenref Labs
695 North Seventh
P.O. Box 68
Brighton, CO 80601
John S. Hutchins
President
Energy Development Consultants, Inc.
2221 East Street
Golden, CO 80401
Lee Ischinger
Aquatic Ecologist
U.S. F.W.S.
2625 Redwing Road
Fort Collins, CO
James A. Ives
Environmental Coordinator
Environmental Services
Atlantic Richfield Company
1500 Security Life Building
555 17th Street
Denver, CO 80217
Ken Jackson
Laramie Energy Research Center
P.O. Box 3395
University Station
Laramie, WY 82071
Larry P. Jackson
Division Manager
DOE/LETC
P.O. Box 3395
University Station
Laramie, WY 82071
M. L. Jacobs
Divisional Manager
Instrumental Analysis
Commercial Testing & Eng. Co.
490 Orchard Street
Golden, CO 80401
Don C. Jennings
Project Manager
Union Oil Company of California
461 South Boylston
P.O. Box 7600
Los Angeles, CA 90051
Jackie Jennings
Marketing Assistant
CDM/Accu- Labs
11485 West 48th Avenue
Wheat Ridge, CO 80033
Carla Johnson
Dames & Moore
605 Parfet Street
Denver, CO 80226
Timothy W. Joseph
Manager, Ecological Science
Env. Serv. Department
WBEC/RSC
6600 South Yale
Tulsa, OK 74136
80526
591
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Andrew P. Jovanovich
Chemical Division
Denver Research Institute
University of Denver
2390 South York Street
Denver, CO 80208
Linda A. Joyce
Range Science Department
Colorado State University
Fort Collins, CO 80521
Linda Kenny
Lab Technician
Geokinetics
Vernal, UT
84078
Jean E. Kiel
Ecologist
Environmental Science Division
Stearns-Roger, Inc.
P.O. Box 5888
Denver, CO 80217
Jean E. Kiel
Colorado State University
Dept. of Botany & Plant Pathology
Fort Collins, CO 80523
C. Judson King
Professor & Chairman
Chemical Engineering
University of California
Berkeley, CA 94720
Christine King
Research Biologist, UWRL
Utah State University
Logan, UT 84321
Jeannette W. King
Research Microbiologist
Chemical Division
Denver Research Institute
University of Denver
2390 South York
Denver, CO 80208
Wesley L. Kinney
Aquatic Biologist
U.S. EPAIEMSL
P.O. Box 15027
Las Vegas, NV
89114
Brad Klafehn
Mining Workshop
Colorado Open Space Council
2229 East Colfax Avenue
Denver, CO 80206
Mathilde J. Kland
Tech. Manager
Shale Group/E&E Division
Lawrence Berkeley Lab
1 Cyclotron Road
Berkeley, CA 94704
Donald A. Klein
Professor
Department of Microbiology
Colorado State University
Fort Collins, CO 80521
592
-------
Ronald W. Kiusman
Professor
Dept. Chemistry-Geochemistry
Colorado School of Mines
Golden, CO 80401
Alexandra Krikos
Chemist
ETS R P
University of Colorado
Campus Box 215
Boulder, CO 80309
Faith Krohlow
Industrial Hygienist
Dust Group
MSHA- DTSC
P.O. Box 25367
Denver, CO 80225
Miles 0. LaHue
Environmental Specialist
Air Quality, C-b Shale Oil Project
U.S.G.S. Area Oil Shale Office
131 North Sixth Street, Suite 300
Grand Junction, CO 81501
Nick Lailas
Physical Scientist
Department of Energy
Shale Resources
12th & Penn NW,
Washington, DC
Rolf Lange
I AD
CT&E
Orchard Street
Golden, CO
80401
John Lanning
University of Colorado
Chemistry Department
Boulder, CO 80202
J. C. Laul
Senior Research Scientist
Battelle-Northwest
Box 999
Richland, WA 99352
Mel E. Lebsack
Research Associate
School of Pharmacy
University of Wyoming
Box 3375
University Station
Laramie, WY 82071
Jerry A. Leenheer
Hydrologist
U.S.G.S. Water
Denver Federal
Building 53
P.O. Box 25046
Denver, CO 80225
H. I. Leon
Senior Staff Manager
Resource Development Operations
TRW Energy Systems Planning Div.
7600 Colshire Drive
McLean, VA 22102
Don A. Lewis
Environmental Systems Engineer
The Aerospace Corporation
P.O. Box 92957
Los Angeles, CA 90009
Resources Div.
Center
Room 6432
20261
593
-------
A. L. Lott
Corporate Industrial Hygienist
Standard Oil of Ohio
1550 Midland Building
Cleveland, OH 44115
Ernest Loveless
Geologist/Consultant
Box 238
Monroe City, IN
47557
Carolyn Mangeng
S-2
Las Alamos Scientific Laboratory
Los Alamos, NM 87545
Kevin L. Markey
Colorado Representative
Friends of the Earth
2239 East Colfax Avenue
Denver, CO 80206
Scott Lynn
Department of
University of
Berkeley, CA
Chemical Engi neeri ng
California
94720
Brooks Martin
Biologist
Environmental
CSM Research
Box 112
Golden, CO
Tech.
Insti tute
80401
David 1. Maase
Civil & Environmental
Utah Water Research Lab
Utah State University
Logan, UT 84321
Rees C. Madsen
Manager
Sohio Petroleum Company
1315 West Highway 40
Vernal, UT 84078
A. J. Mancini
Environmental Engineer
Wyoming DEQ-WQD
Hathaway Building
Cheyenne, WV 82001
Russell B. Martin
President
Envi rotechni cs,
P.O. Box 355
Roosevelt, UT
Ron Marty
QA Coordinator
Colorado Department of Health
4210 East 11th Avenue
Denver, CO 80220
William N. McCarthy, Jr.
Sr. Coordinator for Oil Shale R&D
U.S. EPA
OEMI, Room 645 (RD-681)
401 M Street, S.W.
Washington, DC 20460
Inc.
84066
594
-------
Bob McConnell
Water Quality Control Division
Colorado Department of Health
4210 East 11th Avenue
Denver, CO 80220
F. R. McDonald
Section Supervisor
Division of Res. Support
U.S. Department of Energy
Laramie Energy Research Center
P .O. Box 3395
Laramie, WY 82071
T. J. McLaughlin
Research Scientist
Energy Systems
Battelle NW
P.O. Box 999
Richiand, WA
99352
Leslie McMilhion
Hydrologist, Monitoring Systems
Design Analysis Staff
U.S. EPA
P.O. Box 15027
Las Vegas, NV 89114
Robert B. Medz
Monitoring Technical Division
OMTS
Research & Development
Mail Stop 3809
Washington, DC 20001
Robert R. Megler;
Director, Analytical Lab
ETSRP
University of Colorado
Ekeley Chemistry M-335
Cmpus Box 215
Boulder, CO 80309
James A. Meredith
Environmental Biologist
Oil Shale Division
The Superior Oil Company
2750 South Shoshone
Englewood, CO 80110
Joe M. Merino
Resident Manager, Sand Wash Project
Oil Shale Division
TOSCO Corporation
P.O. Box 814
Vernal, UT 84078
Lance J. Mezga
Geologist, PES Division
Dalton Dalton Newport, Inc.
3605 Warrensville Center Road
Cleveland, OH 44122
Fred Milanovich
Lawrence Livermore
P.O. Box 808
Livermore, CA 94550
Glen A. Miller
Hydrologist
U.S.G.S. Area Oil Shale Office
131 North Sixth Street
Suite 300
Grand Junction, CO 81501
Paul E. Mills
Quality Assurance Officer
Program Operations Office
U.S. EPA
IERL-Ci
5555 Ridge Avenue
Cincinnati, OH 45268
Laboratory
595
-------
Maria Moody
Engi neer
Environmental Sciences
Lawrence Livermore Laboratory
P.O. Box 5507
Livermore, CA 94550
Robert Moran
Geochemi st/Hydrologist
Science Applications, Inc.
1546 Cole Boulevard, Suite 210
Golden, CO 80401
Don L. Morris
Fuel Chemical Supervisor
CDM/Accu- Labs
11485 West 48th Avenue
Wheatridge, CO 80033
Larry L. Morriss
Chemist
Geokinetics
582 North Vernal
P.O. Box 889
Vernal, UT
84078
Swain Munson
YIN Colorado
2600 South Parker Road
Aurora, CO 80014
Denis Nelson
R&D Representative
U.S. E.P.A. Region 8
1860 Lincoln Street
Denver, CO 80208
Daniel A. Netzel
Supervi sor
Spectros copy Secti on
DOE/LETC
Box 3395
University Station
Laramie, WY 82071
T. D. Nevens
Senior Research Engineer
Chemical Division
Denver Research Institute
University of Denver
Denver, CO 80208
David Nochumson
S-2
Los Alamos Scientific Lab
Los Alamos, NM 87545
W. J. O’Brien
Regional Coordi nator
Envi ronment
U.S. Department of Energy
P.O. Box 26500
Lakewood, CO 80226
Howard Olson
Sup. Chemist
Chemistry Department
Colorado Department of Health
4210 East 11th Avenue
Denver, CO 80220
Lucy Pacas
Energy & Environment
Lawrence Berkeley Laboratory
Berkeley, CA 94720
596
-------
L ilita Palekar
Senior Project Scientist
Health Effects Department
Northup Services
P.O. Box 12313
Research Triangle Park, NC
Graham B. Parker
Research Engineer
Atmos. Science
Battelle-Pacific NW Laboratory
P.O. Box 999
Richiand, WA 99352
Ronald K. Patterson
Research Chemist
Aerosol Research Branch
U.S. EPA
Environmental Research Center
Research Triangle Park, NC 27711
Peter Persoff
Chemical Engineer
Energy & Environment Division
Lawrence Berkeley Laboratory
University of California
Berkeley, CA 94720
Bruce Peterson
Data Analyst
Air Resources Center
Oregon State University
Corvallis, OR 97330
Francis Wahl Pierce
Geologist
Department of Interior
U.S. Bureau of Land Management
1600 Broadway, Room 700
Denver, CO 80202
Kenneth D. Pimentel
Engi neer
Environmental Science Division
Lawrence Livermore Laboratory
University of California
P.O. Box 5507, L-453
Livermore, CA 94550
Richard E. Poulson
Manager
Environmental Science Division
U.S. Department of Energy
Laramie Energy Research Center
P.O. Box 3395
University Station
Laramie, WY 82071
Thomas 3. Powers
Environmental Engineer
U.S. EPA
Industrial Research Lab
5555 Ridge Avenue
Cincinnati, OH 45268
Robert Pressey
Head, Chemical Division
Denver Research Institute
University of Denver
2390 South York
Denver, CO 80208
Charles H. Prien
Senior Research Fellow
Chemical Division
Denver Research Institute
University of Denver
University Park
Denver, CO 80208
Richard C. Ragaini
Deputy Division Leader
Environmental Science Div. L-453
Lawrence Livermore Laboratory
P.O. Box 808
Livermore, CA 94550
27709
597
-------
T. K. Rao
Research Associate
Biology Division
Oak Ridge National Laboratory
P.O. Box V
Oak Ridge, TN 37830
Gary D. Rawlings
Program Manager
Monsanto Research Corporation
Station B, Box 8
Dayton, OH 45407
F. Brent Reeves
Botany & Plant Path. Dept.
Colorado State University
Fort Collins, CO 80523
John Reiss, Jr.
Senior Hydrogeologist
Envi rosphere Company
1658 Cole Boulevard
Suite 150
Golden, CO 80401
I. B. Remsen
Dames & Moore
605 Parfet Street
Denver, CO 80215
Stanley J. Reno
Regional Consultant
NIOSH-USPHS-HEW
1961 Stout Street
Room 1194
Denver, CO 80294
Lynn Richards
EEA, Inc.
1111 North 19th
Arlington, VA
Ralph Riggin
Chemist
Battelle
505 King Avenue
Columbus, OH 43216
Sonja Ringen
Laramie Energy Research Center
P.O. Box 3395
University Station
Laramie, WY 82071
Don Rosebrook
Program Manager
Radian Corporation
P.O. Box 9948
Austin, TX 78766
Ira B. Rubin
Research Associate
Anal. Chem. Division
Oak Ridge National Laboratory
P.O. Box X
Oak Ridge, TN 37830
Peter Paul Russell
Lawrence Berkeley Laboratory
1918 Haste Street
Berkeley, CA 94704
Street
22209
598
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Inc.
Walter J. Ruzzo
Colorado State University
Department of Range Science
Reclamation Research Lab
Fort Collins, CO 80523
Ola M. Saether
Research Assistant/Geologist
Geological Sciences
University of Colorado
Ekeley Chemistry
M- 335
Boulder, CO 80309
Thomas G. Sanders
Assistant Professor
Civil Engineering
Colorado State University
Foothills Campus
Fort Collins, CO 80523
Josef J. Schmidt-Collerus
Research Chemist &
Professor of Chemistry
Denver Research Institute
Chemical Division
University of Denver
University Park
Denver, CO 80208
E. J. Schneider
Eng. Geologist
Environmental Science Division
Stearns-Roger, Inc.
725 Niagara Street
Box 5888
Denver, CO 80217
George R. Schottler
U.S. Department of Interior
Bureau of Mines
Building 20
Denver Federal Center
Denver, CO 80225
Suzanne Schwab
Colorado State University
Fort Collins, CO 80523
Richard B. Schwendinger
President
Schwendinger Associates, Inc.
3314 So. Oneida Way
Denver, CO 80224
Michael Shaffron
Analytical Chemist
Chemical Division
Denver Research Institute
2390 South York Street
Denver, CO 80208
David C. Sheesley
Northrup Services,
1293 Patrick Lane
Las Vegas, NV 89120
Henry L. Short
Terrestrial Ecologist
WELUT
U.S. Fish & Wildlife Service
2625 Redwing
Fort Collins, CO 80521
Robert J. Shukie
Industrial Waste Consultant
Colorado Department of Health
Water Quality Control Division
4210 East 11th Avenue
Denver, CO 80220
599
-------
Robert Sievers
Professor
Department of Chemistry
University of Colorado
Campus Box 215
Boulder, CO 80309
James R. Sims
Manager-Systems Assurance
Fossil Energy Operations
TRW Energy Systems Group
7600 Coishire Drive
McLean, VA 22102
Clyde J. Sisemore
Physici St
Earth Sciences ‘ K” Division
Lawrence Livermore Laboratory
P.O. Box 808, L-207
Livermore, CA 94550
Gary M. Sitek
Systems Analyst
Government Programs
Vitro Laboratories Division
14000 Georgia Avenue
Silver Spring, MD 20910
Douglas M. Skie
Regional QA Coordinator
US Environmental Protection Agency
Region VIII
1860 Lincoln
Denver, CO 80203
Deborah Skiarew
Research Scientist
Physical Sciences
Battelle NW
P.O. Box 999
Richiand, WA 99352
Haven S. Skogen
Chief Chemist
Oxy Oil Shale
P.O. Box 2999
Grand Junction, CO
R. K. Skogerboe
Professor
Department of Chemistry
School of Natural Sciences
Colorado State University
Fort Collins, CO 80523
G. C. Slawson, Jr.
Manager
Water Resources Program
General Electric - TEMPO
816 State Street
P.O. Drawer QQ
Santa Barbara, CA 93102
Steven Snider
Public Health Engineer
Water Quality Control Division
Colorado State Health Dept.
4210 East 11th Avenue
Denver, CO 80220
W. Dale Spall
Health Division
Los Alamos Scientific Labs
MS 890
Los Alamos, NM 87545
Thomas Spedding
DOE/LETC
P.O. Box 3395
Laramie, WY 80271
81501
600
-------
Hilding Spradlin
Geokinetics, Inc.
582 North Vernal Avenue
Vernal, UT 84078
Sarah Stackhouse
Environmental Geologist
EEA, Inc.
1111 North 19th Street
Arlington, VA 22209
John Stanley
Research Assistant
University of Colorado
Department of Chemistry
Environmental Trace Substances
Research Program
Boulder, CO 80301
Jake Strohman
Water Quality Control Eng. Super.
Dept. of Environmental Quality
Water Quality Division
Hathaway Building
Cheyenne, WY 82001
Nancy Strong
Mining Workshop
Colorado Open Space Council
2239 East Colfax
Denver, CO 80206
Agnes N. Stroud
Staff Mammalian Biologist
Cytogeneti cs
Los Alamos Scientific Lab.
MS-880
P.O. Box 1663
Los Alamos, NM
87545
John A. Steinkamp
Biomedical Engineer
Biophysics H-1O
Los Alamos Scientific
Mail Stop 888
P .O. Box 1663
Los Alamos, NM 87545
Laboratory
Harold A. Stuber
Chemi St
U.S. Geological Survey
Denver Federal Center
Box 25046, Stop 407
Water Resources Division
Denver, CO 80225
Kenneth Stollenwerk
Research Assistant
Department of Geology
University of Colorado
ETSRP
Boulder, CO 80309
Carole Sue Stov€r
Research Chemist
Oil Shale Department
Occidental Research
P.O. Box 19601
Irvine, CA 92713
Daniel H. Stuermer
Environmental Scientist
Lawrence Livermore Laboratory
P.O. Box 808
Livermore, CA 94550
Charles W. Sullivan
Information Specialist
Superior Oil Company
2750 South Shoshone
Englewood, CO 80110
601
-------
Terry Surles
EES Division
Argonne National Laboratory
Argonne, IL 60439
Vickie Sutherland
Research Meteorologist
Air Quality
North American Weather Consultants
2895 South Main
Salt Lake City, UT 84101
Sandra L Sweeney
Assistant Manager
Instrumental Analysis Division
Commercial Testing & Engineering Co.
490 Orchard Street
Golden, CO 80401
Fred J. Tanis
Research Engineer
Environmental Research Institute
of Michigan
P. 0. Box 8618
Ann Arbor, MI 48107
Terry L. Thoem
Director
Energy Office
U.S. EPA
Region VIII
1860 Lincoln Street, 900
Denver, CO 80 O3
Robert C. Thurnau
Physical Scientist
I ERL
U.S. EPA
5555 Ridge Avenue
Cincinnati, OH
45268
Marvin Tillery
Los Alamos Scientific Laboratory
P.O. Box 1663
Los Alamos, NM 81545
Michael F. Torpy
Research Engineer
EES Division
Argonne National Laboratory
Argonne, IL 60439
Roger Tucker
Air Division, UaUb
U.S.G.S. Area Oil Shale Office
131 North Sixth Street
Grand Junction, CO 81501
Marc Tugeon
Env. Scientist
Hazardous Waste Management Div.
U.S. EPA (WH-565)
401 M Street SW
Washington, DC 20460
Steve Utter
Supervisory Mining Engineer
U.S. Bureau of Mines
Denver Federal Center
Building 20
Denver, CO 80225
Dean Venardos
Research Engineer
Water Conservation
AMOCO Oil
Box 400, H-6
Naperville, IL
60540
602
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Will Wakamiya
Engi neer
Water and Land Resources
Batelle NW
Richiand, WA 99352
John Wallace
Chemist
Denver Research Institute
Chemical Division
2390 South York
Denver, CO 80208
Patsy L. Wanek
Chemical Tech.
CMB—8
Los Alamos Scientific Lab
P.O. Box 1663
Los Alamos, NM 87545
Denis W. Weeter
Associate Professor
Civil Engineering
University of Tennessee
73 Perkins Hall
Knoxville, TN 37916
Paul A. Westcott
Research Chemist
Denver Research Institute
Chemical Division
University of Denver
2390 South York
Denver, CO 80208
R. H. Wiebener
Chemist-Lab Manager
Cenref Labs
695 North Seventh
P.O. Box 68
Brighton, CO 80601
Thomas R. Wildeman
Chemi stry Department
Colorado School of Mines
Golden, CO 80801
Connie L. Wilkerson
Scientist
Physical Sciences
Battelle Pacific Northwest Lab
P.O. Box 999
Richland, WA 99352
Stephen E. Williams
Assistant Professor
Plant/Soil Science
University of Wyoming
Box 3354
Laramie, WY 82071
Charles R. Wilson
Lab Supervisor
CT&E
490 Orchard
Golden, CO 80401
Francis J. Winslow
Director, R&D Marketing
Monsanto Research Corp.
Station B, Box 8
Dayton, OH 45407
John A. Winter
Chief, Quality Assurance Branch
EMS L
U.S. EPA
Quality Assurance Branch
26 West St. Clair
Cincinnati, OH 45268
603
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John York
Black Prince Oil Shale
1 City Boulevard West
Orange, CA 92666
Mark L. Zoller
Project Manager
Environmental Sciences Division
Stearns-Roger, Inc.
P.O. Box 5888
Denver, CO 80217
604
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TECHNICAL REPORT DATA
(Please read J, sJnictions on the relerse before completing)
1. REPORT NO. 12. 3. RECIPIENT’S ACCESSIOr.NO.
EPA-600/9-80-022
4. TITLE AND SUBTITLE
Oil Shale Symposium: Sampling, Analysis arid.
Quality Assurance— March 1919
5. REPORT DATE
June 1980 Issuing Date
6.PERFORMINGOROANIZATIONCODE
7. AUTHOR(S)
Charles Gale (Editor)
8. PERFORMING ORGANIZATION REPORT NO.
10. PROGRAM ELEMENT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Denver Fesearch Institute
University c f Denver
University Park
Denver, CO 80208
11. CONTRACT/GRANT NO.
R806156-0l
12. SPONSORING AGENCY NAME AND ADDRESS
I1Idustr a1 Dnvircn centa1 Des ear h Iabora oi’v
Office . f Fesearch anD DcveLnjnent
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
Symposium proceedings
14. SPONSORING AGENCY CODE
EPA/60 0/ 12
15. SUPPLEMENTARY NOTES
Project Officer, Paul Mills, U.S. EPA —IERL, Cincinnati, OH 5268
51 —68 — 42l6
16. ABSTRACT
The objective of this symposium was to provide a forum for the statement of
the state—of—the--art in sampling, analysis, and Quality assurance of the oil shale
industry pollutants. Ovinions from overnmenta1 and industrial research organiza-
tions were solicited as to the future needs in these areas.
The symposium was held March 26—28, 1919 in Denver, Colorado. 260 registered
attendees were present. Papers from industry, government, and academic research-
ers were presented and discussed. This is a report of the proceedings at the
symposium.
17. KEY WORDS AND DOCUMENT ANALYSIS
.DESCRIPTORS b.IDENTIFIERS/OPEN ENDED TERMS C. COSATI Field!Group
Oil Shale
Symposium
Proceedings
Quality Assurance
Sampling
Analysis
Health Effects
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (misReport)
Unclassified
21. NO. OF PAGES
615
20. SECURITY CLASS (This page)
Unclassified-
22. PRICE
EPA Form 2220.1 (9-73)
605
U.S. GOVERNI ENT PRINTING OFFICE: 1980--e57-166/ 002
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