oral
ORNL/TM-9120
OAK RIDGE
NATIONAL
LABORATORY
Environmental Risk Analysis
for Indirect Coal Liquefaction
L. W. Barnthouse
G. W. Suter II
C. F. Baes III
S. M. Bartell
M. G. Cavendish
R. H. Gardner
R. V. O'Neill
A. V. Rosen
Environmental Sciences Division
Publication No. 2309
uPtnATEO BY
MARTIN MARIETTA ENERGY SYSTEMS, INC.
FOR THE UNITED STATES
DEPARTMENT OF ENERGY
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necessarily state or reflect those of the United States Government or any agency
thereof.
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ORNL/TM-9120
ENVIRONMENTAL SCIENCES DIVISION
ENVIRONMENTAL RISK ANALYSIS FOR INDIRECT COAL LIQUEFACTION
Authors
L. W. Barnthouse1
G. W. Suter II1
C. F. Baes III
S. M. Bartell
M. 6. Cavendish
R. H. Gardner
R. V. O'Neill
A. E. Rosen
ORNL Project Manager
S. G. Hildebrand
Environmental Sciences Division
Publication No. 2309
Principal Investigators
Date of Issue - January 1985
EPA Project Officer
A. A. Moghissi
Prepared for
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20460
Interagency Agreement No. DW 8993 0292-01-0
(DOE 40-740-78)
Prepared by the
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
operated by
MARTIN MARIETTA ENERGY SYSTEMS, INC.
for the
U.S. DEPARTMENT OF ENERGY
under Contract No. DE-AC05-840R21400
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DISCLAIMER
Although the research described in this report has been funded wholly
or in part by the U. S. Environmental Protection Agency (EPA) through
Interagency Agreement Number DW 8993 0292-01-0 to the U.S. Department
of Energy, it has not been subjected to EPA review and therefore does
not necessarily reflect the views of EPA and no official endorsement
should be inferred.
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TABLE OF CONTENTS
Page
LIST OF FIGURES v
LIST OF TABLES vii
SUMMARY xi
ABSTRACT xv
1. INTRODUCTION 1
2. SOURCE TERMS AND EXPOSURE 4
2.1 Source Terms 4
2.2 Aquatic Exposure Assessment 5
2.2.1 Stream Characteristics 5
2.2.2 Contaminant Characteristics 7
2.2.3 Results 10
2.3 Atmospheric Dispersion and Deposition 10
3. AQUATIC ENDPOINTS 17
3.1 Quotient Method 17
3.2 Analysis of Extrapolation Error 21
3.2.1 Methods 22
3.2.2 Results 23
3.2.2 Toxcity of the Whole Effluent 26
3.3 Ecosystem Uncertainty Analysis 28
3.3.1 Explanation of Method 28
3.3.2 Results of Ecosystem Uncertainty Analysis 29
3.3.3 Comparison of Risks Across RACs 35
3.3.4 Comparison of Risks Between Technologies 36
4. TERRESTRIAL ENDPOINTS 39
4.1 Vegetation 39
4.2 Wildlife ..... 44
5. EVALUATION OF RISKS .' 48
5.1 Evaluation of Risks to Fish 48
5.2 Evaluation of Risks of Algal Blooms 50
5.3 Evaluation of Risks to Vegetation and Wildlife 50
5.4 Validation Needs 51
6. ACKNOWLEDGMENTS 53
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Page
7. REFERENCES 54
. 69
. 83
. 97
APPENDIX A.
APPENDIX B.
APPENDIX C.
APPENDIX D.
APPENDIX E.
Aquatic Toxicity Data
Terrestrial Toxicity Data
Common and Scientific Names of Animals and Plants
Species-Specific Results of the Analysis
of Extrapolation Error 103
Detailed Methods and Assumptions for
Ecosystem Uncertainty Analysis 121
E.I Organizing Toxicity Data 123
E.2 Transforming Toxicity Data 124
E.2.1 The General Stress Syndrome (6SS) .... 124
E.2.2 The MICROCOSM Simulations 125
E.2.3 Choosing Unvertainties 125
IV
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LIST OF FIGURES
Figure Page
3.3.1 Risk estimates for three heavy metals over a
range of environmental concentrations 31
3.3.2 Risk estimates for mercury over a range of
environmental concentrations 32
3.3.3 Risk estimates for ammonia and cadmium over
a range of environmental concentrations 33
3.3.4 Maximum risk estimates for nine RAUs 37
3.3.5 Comparison of risks between technologies 38
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LIST OF TABLES
Table Page
1-1 Risk Analysis Categories (RACs) 2
2.1-1 Aqueous source terms (kg/h) for two indirect coal
liquefaction technologies, control option 1 6
2.2-1 Stream characteristics for the eastern reference site ... 8
2.2-2 Contaminant characteristics 9
2.2-3 Estimated ambient contaminant concentrations, eastern
reference stream, Lurgi/Fischer-Tropsch process 11
2.2-4 Estimated ambient contaminant concentrations, eastern
reference stream, Koppers-Totzek/Fischer-Tropsch
process 12
2.3-1 Maximum ambient atmospheric and soil concentrations
of RACs for the Lurgi/Fischer-Tropsch process 15
2.3-2 Maximum ambient atmospheric and soil concentrations
of RACs for the Koppers-Totzek/Fischer-Tropsch process . . 16
3.1-1 Toxicity quotients for toxicity to fish and algae
(ambient contaminant concentration/toxic benchmark
concentration) for Lurgi/Fischer-Tropsch process 19
3.1-2 Toxicity quotients for toxicity to fish and algae
(ambient contaminant concentration/toxic benchmark
concentration) for Koppers-Totzek/Fischer-Tropsch
process 20
3.2-1 Ranges of predicted geometric means of maximum
allowable toxicant concentrations (P6MATC):
ratios of ambient concentrations to PGMATC, and
probabilities of exceeding the PGMATC for the
Lurgi/Fischer-Tropsch process 24
3.2-2 Ranges of predicted geometric means of maximum
allowable toxicant concentrations (PGMATC):
ratios of ambient concentrations to PGMATC, and
probabilities of exceeding the PGMATC for the
Koppers-Totzek/Fischer-Tropsch process 25
3.2-3 Estimated acute LC5Q for largemouth bass and ratio
of upper 95th percentile of the ambient concentration
to the LCso for the Lurgi/Fischer-Tropsch and
Koppers-Totzek/Fischer-Tropsch processes 27
3.3-1 Values of LCcn/ECKQ (mg/L) used to calculate
effects/matrix for SWACOM 30
3.3-2 Deterministic results of EUA 34
4.1-1 Toxicity quotients for terrestrial plants for the
Lurgi/Fischer Tropsch process 40
VII
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Table Page
4.1-2 Toxicity quotients for terrestrial plants for the
Koppers-Totzek/Fischer-Tropsch process 41
4.2-1 Toxicity quotients for terrestrial animals for the
Lurgi/Fischer-Tropsch process 45
4.2-2 Toxicity quotients for terrestrial animals for the
Koppers-Totzek/Fischer-Tropsch process 46
5.1-1 RACs determined to pose potentially significant
risks to fish populations by one or more of three
risk analysis methods 49
A-l Acute toxicity of synfuels chemicals to aquatic
animals 71
A-2 Chronic toxicity of synfuels chemicals to aquatic
animals 78
A-3 Toxicity of synfuels chemicals to algae 80
B-l Toxicity of chemicals in air to vascular plants 85
B-2 Toxicity of chemials in soil or solution to
vascular plants 88
B-3 Toxicity of chemicals in air to animals 92
D-l Predicted geometric mean maximum allowable
toxicant concentrations (PGMATCs) for each RAC
and each species of fish 105
D-2 Probabilities of chronic toxic effects on fish
populations due to RAC 4 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 106
D-3 Probabilities of chronic toxic effects on fish
populations due to RAC 5 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 107
D-4 Probabilities of chronic toxic effects on fish
populations due to RAC 9 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 108
D-5 Probabilities of chronic toxic effects on fish
populations due to RAC 31 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 109
D-6 Probabilities of chronic toxic effects on fish
populations due to RAC 32A at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 110
D-7 Probabilities of chronic toxic effects on fish
populations due to RAC 33 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process ... Ill
D-8 Probabilities of chronic toxic effects on fish
populations due to RAC 34 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process ... 112
vm
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Table Page
D-9 Probabilities of chronic toxic effects on fish
populations due to RAC 35 at annual median ambient
concentrations for the Lurgi/Fischer-Tropsch process . . . 113
D-10 Probabilities of chronic toxic effects on
fish populations due to RAC 4 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 114
D-ll Probabilities of chronic toxic effects on
fish populations due to RAC 5 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 115
D-12 Probabilities of chronic toxic effects on
fish populations due to RAC 9 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 116
D-13 Probabilities of chronic toxic effects on
fish populations due to RAC 31 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 117
D-14 Probabilities of chronic toxic effects on
fish populations due to RAC 33 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 118
D-15 Probabilities of chronic toxic effects on
fish populations due to RAC 34 at annual
median ambient concentrations for the
Koppers-Totzek/Fischer-Tropsch process 119
IX
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SUMMARY
The Environmental Sciences Division, Oak Ridge National
Laboratory, is analyzing the potential environmental risks associated
with commercial-scale synthetic liquid fuels (synfuels) technologies.
The overall objective of this environmental risk analysis project,
which is funded by the Office of Research and Development, U.S.
Environmental Protection Agency, is to guide research on environmental
aspects of synfuel technologies by identifying the most hazardous
synfuel-derived contaminants and the most important sources of
scientific uncertainty concerning the fate and effects of these
contaminants.
The general strategy adopted for the project involves (1) grouping
the contaminants present in effluents and products of commercial-scale
processes into 38 categories termed Risk Analysis Categories (RACs),
(2) defining generalized reference environments with characteristics
representative of regions in which synfuels plants may be sited, and
(3) assessing risks of five distinct, adverse ecological effects:
reductions in fish populations, development of algal blooms that
detract from water use, reductions in timber yield or undesirable
changes in forest composition, reductions in agricultural production,
and reductions in wildlife populations.
This report analyzes the risks associated with two indirect coal
liquefaction technologies: Lurgi gasification with Fischer-Tropsch
synthesis and Koppers-Totzek gasification with Fischer-Tropsch
synthesis. The plant configurations evaluated were adapted from design
information provided by the developers of the technologies. Both
configurations reflect a feed coal capacity of 2.72 x 10 kg
(30,000 tons) per day. Source terms for atmospheric and aqueous waste
streams were based on published process conceptual designs and test
data obtained from bench-scale, pilot, or demonstration units. Control
technology efficiencies were extrapolated from similar applications in
other industries.
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A reference environment resembling eastern Kentucky or West
Virginia was employed in the risk analyses. Estimates of
concentrations of released contaminants in the air, soil, and surface
water of the reference environment, were obtained, using a simple
Gaussian-plume atmospheric dispersion and deposition model and a
steady-state surface water fate model.
Risk to the five ecological endpoints were estimated using one or
more of three techniques: the quotient method, analysis of
extrapolation error, and ecosystem uncertainty analysiSi in the
quotient method, estimated environmental concentrations were simply
*
compared to toxicological benchmarks such as LC 's available for
standard test organisms. In analysis of extrapolation error,
statistical relationships between the sensitivities to contaminants of
the various taxa of fish and between acute- and chronic-effects
concentrations were used to estimate, with appropriate error bounds,
chronic-effects thresholds for reference fish species characteristic of
the reference environment. Taxonomic extrapolations were used to
express the acute effects of all RACs in terms of a common unit, the
96-h LCg0 for largemouth bass. The extrapolated LCr 's and the
source term estimates were then combined and used to assess the acute
toxicities of the whole effluents from the two technologies. In
ecosystem uncertainty analysis, an aquatic ecosystem model was used to
compute risk estimates that explicitly incorporate biological phenomena
such as competition and predation, which can magnify or offset the
direct effects of contaminants of organisms.
With respect to fish, nine RACs were determined to be significant
for one or both technologies. RAC 5 (ammonia) and RAC 34 (cadmium)
were the only RACs found to be significant for both technologies and
all risk analysis methods. RAC 4 (acid gases) was significant for both
technologies, according to the quotient method and analysis of
extrapolation error; however, this RAC could not be addressed using
ecosystem uncertainty analysis. The whole effluent from the
= lethal does to 50% of population exposed.
XII
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Lurgi-based technology appeared to be somewhat more acutely toxic than
the corresponding effluent from the Koppers-Totzek technology. For
both technologies, conventional pollutants such as ammonia, cadmium,
and hydrogen sulfide appear to be substantially more hazardous to fish
than the complex organic contaminants usually associated with synfuels.
Algal toxicity data were available for only ten RACs. Because of
the diversity of experimental designs and test endpoints used in algal
bioassays, it was not possible to rank the RAC using the quotient
method. However, most of the toxicity quotients calculated for algae
were lower than the corresponding quotients for fish. Only RACs 33
(nickel) and 34 (cadmium) would be judged significant for any
technology using the quotient method. Ecosystem uncertainty analysis
suggested greater risks of effects on algae than did the quotient
<
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ABSTRACT
BARNTHOUSE, L. W., 6. U. SUTER II, C. F. BAES III,
S. M. BARTELL, M. G. CAVENDISH, R. H. GARDNER,
R. V. O'NEILL, and A. E. ROSEN. 1984. Environmental
risk analysis for indirect coal liquefaction.
ORNL/TM-9120. Oak Ridge National Laboratory, Oak Ridge,
Tennessee. 142 pp.
This report presents an analysis of the risks to fish, water
quality (due to noxious algal blooms), crops, forests, and wildlife of
two technologies for the indirect liquefaction of coal: Lurgi and
Koppers-Totzek gasification of coal for Fischer-Tropsch synthesis.
A variety of analytical techniques were used to make maximum use of
the available data to consider effects of effluents on different levels
of ecological organization. The most significant toxicants to fish
were found to be ammonia, cadmium, and acid gases. An analysis of
whole-effluent toxicity indicated that the Lurgi effluent is more
acutely toxic than the Koppers-Totzek effluent. Six effluent
components appear to pose a potential threat of blue-green algal
blooms, primarily because of their effects on higher trophic levels.
The most important atmospheric emissions with respect to crops,
forests, and wildlife were found to be the conventional combustion
products S02 and NO^. Of the materials deposited on the soil,
arsenic, cadmium, and nickel appear of greatest concern for
phytotoxicity.
xv
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1. INTRODUCTION
Environmental risk analysis is the process of identifying and
quantifying the probabilities of adverse changes in the environment
resulting from human activities. This includes explicit incorporation
and, to the extent possible, quantification of scientific uncertainties
relating to the adverse effects being considered. The Environmental
Sciences Division, Oak Ridge National Laboratory, has been developing
and demonstrating methods for environmental risk analysis for the
Office of Research and Development, U.S. Environmental Protection
Agency (USEPA). The methods being used in this project were described
by Barnthouse et al. (1982). Although the concept of risk is
applicable to many types of environmental problems, this project
focuses on risks associated with toxic environmental contaminants
derived from synthetic liquid fuels (synfuels) technologies. The
overall objective of the project is to guide research on environmental
aspects of synfuel technologies by identifying the most hazardous
contaminants (or classes of contaminants) and the most important
sources of scientific uncertainty concerning the fate and effects of
contaminants. The analyses, results, and conclusions of this research
are intended to be generic and are not estimates of actual impacts of
specific plants at specific sites.
For purposes of risk analysis, the thousands of potentially
significant contaminants in waste streams and products of synthetic
liquid fuels technologies have been grouped into the 38 categories,
termed Risk Analysis Categories (RACs), listed in Table 1-1. Five
ecological endpoints are used: (1) reductions in fish populations,
(2) development of algal populations that detract from water use,
(3) reductions in timber yield or undesirable changes in forest
composition, (4) reductions in agricultural production, and
(5) reductions in wildlife populations. Rather than descriptions of
specific sites, the risk analyses employ generalized reference
environments, with characteristics representative of regions in which
synfuels plants may be sited. Two reference environments are being
used in the research for USEPA: an eastern environment resembling
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ORNL/TM-9120
Table 1-1. Risk Analysis Categories (RACs)
RAC Number
Name
Description
1
2
3
4
5
6
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
CO
SOX
NOX
H2S, HCN
NH3
Methane through butanes,
acetylene, ethene
through butenes; C-|-C4 alkanes, alkynes
7 Formaldehyde
8 Volatile organochlorines
9 Volatile carboxylic acids
10 Volatile 0 & S heterocyclics
11 Volatile Nheterocyclics
12 Benzene
13 Aliphatic/alicyclic
hydrocarbons
14 Mono- or diaromatic hydro-
carbons (excluding
benzene)
15 Polycyclic aromatic
hydrocarbons
16 Aliphatic amines (excluding
Nheterocyclics)
17 Aromatic amines (excluding
• Nheterocyclics)
18 Alkaline N hetero-
cyclics ("azaarenes")
(excluding "volatiles")
19 Neutral N, 0, S hetero-
cyclics (excluding
"volatiles")
20 Carboxylic acids
(excluding "volatiles")
21 Phenols
22 Aldehydes and ketones
("carbonyls") (excluding
formaldehyde)
23 Nonheterocyclic organo-
sulfur
24 Alcohols
25 Nitroaromatics
26 Esters
27 Amides
28 Nitriles
29 Tars
30 Respirable particles
31 Arsenic
32 Mercury
33 Nickel
34 Cadmium
35 Lead
36 Other trace elements
37 Radioactive materials
38 Other remaining materials
and cyclocompounds; bp
HCHO
To bp -vl20°C; CH2C1?, CHC13
To bp -\.120°C " -
To bp %120°C
CC14
formic and acetic acids only
furan, THF, thiophene
To bp ^120°C; pyridine, piperidine,
pyrrolidine, alkyl pyridines
Benzene
C$ (bp 'v40°C) and greater; paraffins,
olefins, cyclocompounds, terpenoids, waxes,
hydroaromatics
Toluene, xylenes, naphthalenes, biphenyls,
alkyl derivatives
Three rings and greater; anthracene, BaA,
BaP, alkyl derivatives
Primary, secondary, and tertiary nonhetero-
cyclic nitrogen, MeNH2, diMeNH, triMeN
Anilines, napthylamines, amino pyrenes;
nonheterocyclic nitrogen
Quinolines, acridines, benzacridines
(excluding pyridines)
Indoles, carbazoles, benzofurans, dibenzo-
thiophenes
Butyric, benzoic, phthalic, stearic
Phenol, cresols, catechol, resorcinol
Acetaldehyde, acrolein, acetone,
benzaldehyde
Mercaptans, sulfides, disulfides,
thiophenols, CS2
Methanol, ethanol
Nitrobenzenes, nitropyrenes
Acetates, phthalates, formates
Acetamide, formamide, benzamides
Acrylonitrile, acetonitrile
As, all forms
Hg, all forms
Ni, all forms
Cd, all forms
Pb, all forms
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3 ORNL/TM-9120
eastern Kentucky or West Virginia, and a western environment resembling
the western slope of the Rocky Mountains in northern Colorado or
southern Wyoming. Descriptions of the meteorology, hydrology,
demography, land-use patterns, and biota of these two reference
environments have been developed by Travis et al. (1983). The indirect
coal liquefaction plants are assumed to be located in the east.
This report analyzes risks associated with two indirect coal
liquefaction technologies: Lurgi gasification with Fischer-Tropsch
synthesis and Koppers-Totzek gasification with Fischer-Tropsch
synthesis. The analyses assumed commercial-scale facilities, with
identical feed coal capacities and similar environmental control
technologies, sited in the eastern reference environment. The
objectives of the risk analyses were (1) to identify the RACs of
greatest concern for each technology; (2) to compare, as far as
possible, the risk associated with different technologies;(3) to
compare the risks of the indirect coal liquefaction technology to the
five ecological endpoints described above; and (4) to compare the
magnitudes of uncertainty concerning risks of different RACs and
different components of risk for each RAC.
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ORNL/TM-9120 4
2. SOURCE TERMS AND EXPOSURE
This section presents (1) estimates of aqueous and atmospheric
source terms for four commercial-scale indirect coal liquefaction
plants, and (2) estimates of exposure concentrations for aquatic and
terrestrial biota hear a hypothetical plant site with environmental
characteristics that roughly correspond to those of proposed sites for
coal liquefaction factilities in eastern Kentucky and West Virginia.
2.1 SOURCE TERMS
Under a subcontract with Oak Ridge National Laboratory, TRW Energy
Technology Division (TRW 1983) described commerical-scale plant
configurations for two indirect coal liquefaction processes: Lurgi
gasification with Fischer-Tropsch synthesis and Koppers-Totzek
gasification with Fischer-Tropsch synthesis. The plant configurations
evaluated by TRW were adapted from design information provided by the
developers of the two technologies. The source term estimates
developed by TRW were based largely on published process conceptual
designs and test data obtained from bench-scale, pilot, or
demonstration units. Control technology efficiencies were extrapolated
from similar applications in other industries.
Both plant configurations reflect a feed coal capacity of
2.72 x 10 kg (30,000 tons) per day. TRW estimated quantities and
compositions of all uncontrolled and controlled waste streams, expressed
in Risk Analysis Categories (RACs), (Sect. 1). For aqueous waste
streams, two alternative control options were considered:
1. Phenol extraction, ammonia recovery, biological
oxidation, chemical precipitation, and carbon
adsorption.
2. Option 1, followed by forced evaporation and
surface impoundment. This implies zero discharge
to surface water.
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5 ORNL/TM-9120
Because of the large number of atmospheric effluent sources associated
with each technology, the atmopheric source terms are not presented in
this report. They are given in Tables 2-8 and 3-8 of the report by
TRW (1983). The aqueous source terms for option 1 are summarized in
Table 2.1-1. They include process-generated wastewaters, coal pile
runoff, and cooling tower blowdown. Control option 2 is a
zero-discharge control strategy; consequently, no source terms are
presented.
2.2 AQUATIC EXPOSURE ASSESSMENT
Estimates of contaminant concentrations in the surface waters of
the eastern reference environment were computed based on the source
terms described in the preceding section. The model used for this
purpose was described by Travis et al. (1983). The model used for
the synfuels risk analyses is similar in concept to the EXAMS model
(Baughman and Lassiter 1978), but is simpler in process chemistry
and environmental detail. A river is represented as a series of
completely mixed reaches. Within each reach, steady-state contaminant
concentrations are computed, based on dilution and on physical/chemical
removal of contaminants from the water column. Ranges and variances
can be placed on all of the environmental and chemical parameters in
the model to compute the frequency distribution of environmental
concentrations. For this analysis, frequency distributions were
computed for all RACs, based on observed variability in environmental
parameters affecting contaminant transport and transformation.
2.2.1 Stream Characteristics
The environmental parameters used in the surface water exposure
o
analysis were stream flow (m/s), stream width (m), reach length (m),
o
sediment load (mg/L), sediment density (g/m ), the depth of the
biologically active sediment (cm), the fraction of organic carbon in
the sediment (unitless), stream temperature (K), current velocity
(m/s), wind velocity (m/s), and the radius of sediment particles (cm).
Estimates of stream flow, temperature, and suspended solids for the
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ORNL/TM-9120 6
Table 2.1-1. Aqueous source terms (kg/h) for two indirect coal
liquefaction technologies, control option 1
RAC Lurgi/Fischer-Tropsch Koppers-Totzek/Fischer-Tropsch
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
31
32
33
34
35
36
4.3
59
0
0
0
35
4.4 E-05
0
4.2 E-03
2.8 E-04
2.3 E-02
3.4 E-02
0
0
0
8.9 E-03
3.5
0.19
6.7 E-05 -
0
1.7 E-02
0
7.6 E-06
0
0
3.8
4.0 E-02
0.40-0.46
4.2 E-02
0.48
8.4
1.8-3.8
18
0
0
0
320
0
0
1.7 E-04
5.9 E-03
3.4 E-04
1.4 E-04
0
0
0
0
0
6.9 E-04
6.3 E-04
0
0.23
0
7.0 E-05
0
0
1.2-1.3
3.6 E-03
0.08-0.22
2.4 E-02
0.10-0.11
200
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7 ORNL/TM-9120
eastern site were set within ranges observed by the U.S. Geological
Survey (US6S) for the Big Sandy River at Louisa, Kentucky, and the
Monongahela River at Braddock, Pennsylvania (USGS 1977, 1979). Values
for the other stream parameters were taken from Southworth (1979).
o
Irradiance values [photons/(cm «s)] for estimating photolysis rates
were obtained from Zepp and Cline (1977).
Probability distributions for flow, temperature, and suspended
solids were generated, based on the means, minima, and maxima of these
parameters observed at the USGS stations. Normal distributions for
particle radius, organic carbon fraction, current velocity, and wind
velocity were derived from ranges used by Southworth (1979). Because
current velocity and sediment load are influenced by stream flow, a
correlation coefficient of 0.7 was specified between flow and velocity
and between flow and suspended solids. All environmental parameters
used in the exposure analysis are presented in Table 2.2-1.
2.2.2 Contaminant Characteristics
For determining the characteristics of organic contaminants
(Table 2.2-2), the chemical properties used were molecular weight
(g/mol), aqueous solubility (g/L), octanol-water partition coefficient
(unitless), quantum yield of direct photolysis (unitless), molar
extinction coefficient [(cm«L)/mol], and vapor pressure (mmHg).
Although microbial degradation rates can be accommodated in the model,
none were used for this assessment. Molecular weights of organic
compounds were obtained from Weast (1980); aqueous solubility data were
obtained from Verschueren (1977); octanol-water partition coefficients
were obtained from Leo et al. (1971) and Briggs (1981). Equations
relating vapor pressure to ambient temperature were generated from data
points reported in Verschueren (1977). These equations are linear
approximations that should provide adequate accuracy over the small
temperature range (280-310 K) involved.
Derived characteristics of organic contaminants were calculated
using functional relationships obtained from the literature. Henry's
Law coefficients were approximated using the method of Dilling (1977).
-------
ORNL/TM-9120
Table 2.2-1. Stream characteristics for the eastern reference site
Environmental
parameter
Stream flow
Reach length
Stream width
Suspended solids
Sediment depth
Solids density
Fraction of organic
carbon
Particle radius
Temperature
Current velocity
Wind velocity
Units
m3/s
m
m
mg/L
cm
g/cm3
cm
K
m/s
m/s
Mean
value
120
1000
40
25
1
1.02
0.1
0.005
298
0.25
1.5
Standard
deviation
75
0
0
20
0
0
0.1
0.0025
3
0.1
0.1
Minimum
value
50
1000
40
1
1
1.02
0.05
0.001
283
0.1
0.25
Maximum
value
600
1000
40
250
1
1.02
0.25
0.01
310
1.0
4.0
-------
ORNL/TM-9120
Table 2.2-2. Contaminant characteristics
RAC
4
5
6
7
8
9
10
11
12
13
14
15
17
19
20
21
22
23
24
25
26
28
31
32
33
34
35
36
Molecular
or atomic
Representative weight3
contaminant (g/mol)
Hydrogen sulfide
Ammonia
Butane
Formaldehyde
Methylene chloride
Acetic acid
Thiophene
Pyridine
Benzene
Cyclohexane
Toluene
Anthracene
Anil ine
Dibenzofuran
Butanoic acid
Phenol
Acrolein
Methanethiol
Methanol
Nitrobenzene
Methyl phthalate
Acrylonitrile
Arsenic
Mercury
Nickel
Cadmium
Lead
Fluorine
34.06
17.03
58.12
30.03
84.93
60.05
84.14
79.10
78.12
84.16
92.15
178.24
93.13
168.21
88.1
94.11
56.07
48.11
32.04
123.11
194.19
53.06
74.92
200.59
58.71
112.40
207.19
19.00
Octanol -water Quantum
Aqueous partition yield of
solubility*3 coefficient photolysis
(g/L) (log P) (unitless)
6.1 E-02
1.67 E+01
3.80 E-02
4.43 E-01
3.00 E-02
1.78 E+00
5.5 E-02
5.15 E-01
7.50 E-05
3.40 E+01
3.00 E-03
5.62 E+01
8.20 E+01
9.74 E-01
4.00 E-05
2.7 E-01
1.9 E+00
5.0 E+00
3.83 E-01
-0.1 7C
1.81C
0.650C
2.13C
4.0°
2.69C
4.45C 0.003d
0.90C
4.12C
0.79C
1.46C
0.906
-0.660C
-0.74°
2.31e
-0.92C
3Weast (1980).
bVerschueren (1977).
cLeo et al. (1971).
Zepp and Schlotzhauer (1979).
eBriggs (1981).
-------
ORNL/TM-9120 10
Mass transfer rates and dissolved fractions were calculated using the
method of Southworth (1979). Particulate-settling velocities were
calculated from Stoke's Law (Weast 1980). Direct photolysis rate
constants for anthracene were calculated using the method of Zepp and
Cline (1977). Adsorption and desorption coefficients were approximated
using the method of Karickhoff et al. (1979).
Because of their complex environmental chemistry, removal processes
for trace elements were not directly modeled. Rates of removal by
sedimentation were estimated using an adsorption-desorption coefficient
of 200. Schell and Sibley's (1982) study of distribution coefficients
for radionuclides suggests that this is probably a conservative
estimate for most trace elements under most environmental conditions.
2.2.3 Results
Model runs were conducted for the reference stream, using the
source terms presented in Table 2.1-1. The means, medians and upper
95% concentrations (i.e., the concentrations equaled or exceeded in 5%
of the Monte Carlo simulations) in 1-km stream reaches immediately
adjacent to the release sites are presented in Tables 2.2-3 and 2.2-4.
For all practical purposes, the concentrations computed using
contaminant-specific removal rates are identical to concentrations
computed from dilution alone. Thus, at least in the immediate vicinity
of contaminant sources located on rivers such as the eastern reference
stream, the environmental removal processes modeled have very little
influence on steady-state contaminant concentrations. It is possible,
however, that some of the processes not modeled (e.g., hydrolysis,
complexation, or microbial degradation) may occur more rapidly than do
photolysis, sedimentation, and volatilization.
2.3 ATMOSPHERIC DISPERSION AND DEPOSITION
The short-range atmospheric dispersion code AIRDOS-EPA (Moore
et al. 1979) was used in the environmental risk analysis to calculate
ground-level atmospheric concentrations and deposition. This code was
summarized by Travis et al. (1983), who also described the method for
-------
11
ORNL/TM-9120
Table 2.2-3. Estimated ambient contaminant concentrations,
eastern reference stream, Lurgi/Fischer-Tropsch
process
RAC
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
31
32
33
34
35
36
Reference compound
Hydrogen sulfide
Ammon i a
Butane
Formaldehyde
Methylene chloride
Acetic acid
Thiophene
Pyridine
Benzene
Cyclohexane
Toluene
Anthracene
Methyl amine
Aniline
Quinoline
Dibenzofuran
Butanoic acid
Phenol
Acrolein
Methanethiol
Methanol
Nitrobenzene
Methyl phthalate
Acetamide
Aery Ion itr ile
Arsenic
Mercury
Nickel
Cadmium
Lead
Fluorine
Mean
(g/L)
9.99 E-06
1.36 E-04
0
0
0
8.10 E-05
1.02 E-10
0
9.72 E-09
6.47 E-10
5.32 E-08
6.94 E-08
0
0
0
2.06 E-08
8.10 E-06
4.40 E-07
1.55 E-10
0
3.93 E-08
0
1.76 E-ll
0
1.2 E-05
8.85 E-06
9.52 E-08
1.06 E-06
9.69 E-08
1.12 E-06
1.96 E-05
Median
(9/L)
8.77 E-06
1.20 E-04
0
0
0
7.11 E-05
8.94 E-ll
0
8.54 E-09
5.68 E-10
4.67 E-08
6.45 E-08
0
0
0
1.81 E-08
7.11 E-06
3.86 E-07
1.36 E-10
0
3.45 E-08
0
1.54 E-ll
0
1.1 E-05
7.78 E-06
8.36 E-08
9.31 E-07
8.51 E-08
9.83 E-07
1.72 E-05
Upper 95%a
(9/L)
1.97 E-05
2.69 E-04
0
0
0
1.59 E-04
2.00 E-10
0
1.91 E-08
1.27 E-09
1.05 E-07
1.19 E-07
0
0
0
4.05 E-08
1.59 E-05
8.65 E-07
3.13 E-10
0
7.73 E-08
0
3.46 E-ll
0
2.5 E-05
1.74 E-05
1.88 E-07
2.09 E-06
1.91 E-07
2.20 E-06
3.85 E-05
aConcentration expected to be equaled or exceeded on 5% of days.
-------
ORNL/TM-9120 12
Table 2.2-4. Estimated ambient contaminant concentrations, eastern
reference stream, Koppers-Totzek/Fischer-Tropsch
process
RAC
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
31
32
33
34
35
36
Reference compound
Hydrogen sulfide
Ammon i a
Butane
Formaldehyde
Methylene chloride
Acetic acid
Thiophene
Pyridine
Benzene
Cyclohexane
Toluene
Anthracene
Methyl amine
Aniline
Quinoline
Dibenzofuran
Butanoic acid
Phenol
Acrolein
Methanethiol
Methanol
Nitrobenzene
Methyl phthalate
Acetamide
Aery Ion itrile
Arsenic
Mercury
Nickel
Cadmium
Lead
Fluorine
Mean
(9/L)
8.9 E-06
4.2 E-05
0
0
0
7.4 E-04
0
0
3.9 E-100
1.4 E-08
7.9 E-10
2.9 E-10
0
0
0
0
0
1.6 E-09
1.5 E-09
0
5.3 E-07
0
1.6 E-10
0
0
3.1 E-06
9.1 E-09
5.2 E-07
6.0 E-08
2.4 E-07
4.7 E-04
Median
(g/L)
7.8 E-06
3.7'E-05
0
0
0
6.5 E-04
0
0
3.5 E-10
1.2 E-08
6.9 E-10
2.7 E-10
0
0
0
0
0
1.4 E-09
1.3 E-09
0
4.7 E-07
0
1.4 E-10
0
0
2.7 E-06
8.0 E-09
4.5 E-07
5.2 E-08
2.2 E-07
4.1 E-04
Upper 95%a
(9/L)
1.8 E-05
8.3 E-05
0
0
0
1.5 E-03
0
0
7.7 E-10
2.7 E-08
1.5 E-09
4.9 E-10
0
0
0
0
0
3.1 E-09
2.9 E-09
0
1.0 E-07
0
3.2 E-TO
0
0
6.1 E-06
1.8 E-08
1.0 E-06
1.2 E-07
4.8 E-07
9.2 E-04
Concentration expected to be equaled or exceeded on 5% of days.
-------
13 ORNL/TM-9120
calculating accumulation in soil. Soil concentrations were calculated
for a 35-year accumulation period, using site-specific values for soil
bulk density, precipitation, evapotranspiration, and irrigation, and
taking into account removal by leaching, biological degradation, and
chemical degradation. This calculation is performed using the food
chain model TERREX.
Because most phytotoxicity studies are done in solution culture,
we have added a calculated concentration in soil solution that is not
described in previous documents. For calculation of the soil solution
concentration, the total accumulation in the soil compartment is first
calculated by summing the material deposited over the lifetime of the
facility and correcting for leaching, degradation, and other removal
processes. The retained material is then partitioned between the solid
and solution phases of the soil compartment, assuming the relationship
C.
r = 1S
Liss K
C. = the concentration of compound i in root zone soil
solution (yg/L),
C. = the concentration of compound i in root zone soil
(vig/kg), and
K. = the distribution coefficient (I/kg).
Because Kd is in the denominator of Eq. (1), the soil solution
concentration C. could take on extremely high values with small values
of K,. To bound the maximum value of C. , it is assumed that the
d iss
upper-bound concentration is represented by the total deposited and
retained material divided by the quantity of water in the root zone
defined by d or
rmax V1 - exp(-Xs1 tfa)]
Liss " 10 p 6 d\ { '
-------
ORNL/TM-9120 14
where
D. = the ground-level deposition rate of
i 2
compound i [ug/(m *s)],
X$i = the sum of all soil removal rate constants (L/s),
tb = the period of long-term buildup in soil, equal to the length
of time that the source term is in operation (s),
2 2
10 = conversion factor for converting g/cm to kg/m
[(10,000 cm2/! m2)
(1 kg/1000 g)],
p = soil bulk density (g/cm ),
6 = volumetric water content (cm3/cm3),
d = the depth of the root zone (cm),
r = soil volumetric water content (mL/cm ).
If Ciss calculated via Eq. (1) exceeds Cmax calculated via Eq. (2),
then Ciss is set equal to Cmax. The value of 6 used in Eq. (2) is
very important in providing a reasonable estimate of Cmax. Since
measured values of K. are usually under saturated conditions, 0 in
Eq. (2) represents total soil porosity.
These calculations generate sector-average ground-level
concentrations in air, soil, and soil solution in 16 directions at
500 m intervals from 1,500 to 50,000 m from the source. The highest
annual average concentrations in air and the highest soil and soil
solution concentrations after 35 years of deposition are presented in
Tables 2.3-1 and 2.3-2.
-------
15
ORNL/TM-9120
Table 2.3-1.
Maximum ambient atmospheric and soil concentrations of RACs for the Lurgi/Fischer-Tropsch
process
RAC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Name
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocycl ics
Volatile Nheterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or diaromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocycl ic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Kespirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
Otner trace elements
Radioactive materials
Annual average
concentration in air
(ug/m3)
4.63 E-01
8.5 E-01
3.69
8.41 E-03
3.65 E-03
39.1
b
b
b
7.86 E-04
b
1.18 E-02
5.34
5.29
1.14 E-02
b
b
b
4.73 E-04
b
4.74 E-03
0.910
8.18 E-04
3.95
b
b
b
b
b
28.9
7.66 E-04
1.91 E-05
1.06 E-03
1.39 E-05
1.28 E-02
7.25 E-03
6.33 E-04
Concentration
in soil
(ng/kg)
a
a
a
a
a
15.7
b
b
b
3.22 E-05
b
4.90 E-03
1.38
3.07
1.02
b
b
b
1.79 E-05
b
1.21
1.51
9.79 E-04
43.7
b
b
b
b
b
a
3700
3.04 E-03
4920
9.90
1.29 E-04
a
a
Concentration in
soil solution
(pg/L)
a
a
a
a
a
16.3
b
b
b
2.69 E-05
b
3.77 E-03
9.86
6.14 E-01
1.58 E-02
b
b
b
4.72 E-06
b
1.79
3.11
4.45 E-04
90.1
b
b
b
b
b
a
18.5
3.04 E-04
32.8
1.52
14.3
a
a
aNo accumulation in soil.
bNo emissions.
-------
ORNL/TM-9120
16
Table 2.3-2.
Maximum ambient atmospheric and soil concentrations of RAUs for the Koppers-Totzek/
Fischer-Tropsch process
RAC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Name
Carbon monoxide
Sulfur oxiaes
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile Nheterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or diaromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocycl ic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
Other trace elements
Radioactive materials
Annual average
concentration in air
(ug/m3)
22.4
6.87
5.92
0.135
3.70 E-05
50.0
b
b
b
b
b
5.71 E-05
6.55
6.52
0.0173
b
b
b
b
b
b
0.986
0.397
10.3
b
b
b
b
b
127
1.52 E-04
4.28 E-03
1.90 E-03
5.68 E-05
1.83 E-03
4.39 E-02
1.18 E-03
Concentration
in soil
(ug/kg)
a
a
a
a
a
20.1
b
b
b
b
b
2.38 E-05
169
3.78
1.55
b
b
b
b
b
b
2.13
0.475
1680
b
b
b
b
b
a
360
6.83 E-01
3000
17.6
976
a
a
Concentration in
soil solution
(ug/L)
a
a
a
a
a
20.8
b
b
b
b
b
1.83 E-05
12.1
0.757
0.0239
b
b
b
b
b
b
4.39
0.216
3450
D
b
b
b
b
a
1.80
6.83 E-02
20
2.71
1.08
a
a
aNo accumulation in soil.
bNo emissions.
-------
17 ORNL/TM-9120
3. AQUATIC ENDPOINTS
3.1 QUOTIENT METHOD
Also known as the "ratio method," this approach to assessing the
relative hazard of several constituents has been used in such fields as
environmental health and epidemiology. The quotient is calculated from
the ratio of the known or estimated concentration of a chemical in the
environment to a concentration of that chemical proven or calculated
(by extrapolation from experimental data) to be toxic to certain
organisms at a particular test endpoint. The endpoint, known as a
toxicological benchmark, may be one of several, among them the USEPA
water quality criteria (USEPA 1980a-p), the effective concentration
causing a designated effect on 20% of the test organisms, (EC2Q),
mean toxic concentration, (MTC), lowest observed toxic concentration
(LOTC), median tolerance limit (TL ), and the concentration required
to kill 50% of the test organisms (IC™).
Since this report compares potential toxic differences between
groups of chemicals (RACs), benchmarks common to as many of the RACs as
possible were preferred. LC™ and TL , the two benchmarks most
\J\J III
frequently found in aquatic toxicological literature, were selected to
represent acute toxicity (Table A-l). Chronic effects are presented as
the geometric mean maximum allowable toxicant concentration (GMATC),
which is the geometric mean of the highest no-observed-effect
concentration and the lowest observed-effect concentration (Table A-2).
In contrast, benchmarks used in algal tests can vary between studies;
therefore, a variety of test endpoints were selected for this report
(Table A-3).
Appendix A does not include all extant data on the responses of
freshwater organisms to the test chemicals. For example, for the heavy
metals, data were excluded for the sake of brevity, but several
representative values are included.
As in the selection of benchmarks, the test species chosen for
tabulation were those that appear most frequently in the literature.
Invertebrates were usually represented by cladocerans (Daphnia species),
with insect data presented when available. The fish species selected
-------
ORNL/TM-9120 18
are those usually used in toxicity testing, namely, fathead minnows
(Pimephales promelas), bluegills (Lepomis macrochirus), and rainbow
trout (Salmo gairdneri). Data for algal assays are sparse, so all
species appearing in the literature, to our knowledge, were included
in Table A-3.
Tables 3.1-1 and 3.1-2 present the highest quotients for each RAC
and category of effect for the two indirect liquefaction technologies.
The acute toxicity quotients were calculated using the upper 95th
percent!le concentration (an estimate of the worst acute exposure,
assuming stable plant operation). The chronic quotients were calculated
using the annual median concentration, and the algal quotients were
calculated for both concentrations, since the distinction between acute
and chronic effects is not clear for algae. The higher the value of
these quotients, the greater the risk of acute effects on organisms in
the reference stream.
Quotients are interpreted according to the best judgment of the
analyst (Barnthouse et al. 1982). A value of 0.01 (1.0 E-02) or less
indicates little apparent environmental significance, 0.01 to 10
suggests possible or potential adverse effects, and greater than 10
describes a chemical of probable environmental concern. The utility of
these screening criteria for risk analysis must be confirmed by further
experience in risk analysis and by field studies.
Ammonia (alkaline gases - RAC 5) and hydrogen sulfide
(acid gases - RAC 4) appear to be the most serious ichthyotoxin in the
effluents of both technologies, with quotients for fish acute toxicity
greater than 1.0 for both. Cadmium (RAC 34) also appears to be a
general problem, with fish quotients greater than 0.1 for acute
toxicity in both technologies. Quotients greater than 0.01 for acute
or chronic toxicity appeared in both technologies for mercury (RAC 32),
lead (RAC 35), and other trace elements (RAC 36). No organic RACs had
quotients greater than 0.01 for either technology. Fewer RACs appear
to be important for algal toxicity due to both the shortage of algal
toxicity data and the relative insensitivity of algae to several
tested RACs. Only nickel (RAC 33) and cadmium (RAC 34) had quotients
greater than 0.01 for either technology.
-------
19
ORNL/TM-9120
Table 3.1-1. Toxicity quotients for toxicity to fish and algae (ambient contaminant concentration/toxic
benchmark concentration) for Lurgi/Fischer-Tropsch process
Highest quotient3
RAC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Name
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile Nheterocyclics
Benzene
Al iphatic/al icycl ic hydrocarbons
Mono- or diaromatic hydrocarbons
Polycyclic aromatic
hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocyclic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
Other trace elements
Fish (acute)
95%
b
b
b
2.18 E+00
3.95 E+00
b
b
b
1.81 E-03
c
b
3.61 E-06
9.1 E-08
4.55 E-05
2.97 E-03
b
b
b
c
8.85 E-05
1.12 E-04
6.81 E-06
b
c
b
4.74 E-08
b
b
b
b
1.31 E-03
7.81 E-03
4.54 E-04
1.91 E-01
3.67 E-03
1.67 E-02
Fish (chronic)
b
b
b
c
c
b
b
b
c
c
b
c
c
7
c
b
b
b
c
c
1
6
b
c
b
1
b
b
b
b
1
3
8
5
5
1
Median
.54 E-05
.76 E-04
.47 E-06
.93 E-06
.56 E-03
.64 E-01
.54 E-03
.01 E-02
.18 E-02
.52 E-04
Algae
Median
c
b
b
b
c
c
b
1.63 E-08
c
1.42 E-06
1.14 E-06
b
b
b
c
c
1.93 E-05
c
b
c
b
1.40 E-07
b
b
b
b
3.35 E-03
1.05 E-03
9.31 E-03
1.70 E-02
1.97 E-03
c
3.64
3.17
2.18
b
b
b
c
c
4.33
c
b
c
b
3.15
b
b
b
b
7.51
2.34
2.09
3.81
4.41
c
9-5%
E-08
E-06
E-06
E-05
E-07
E-03
E-03
E-02
E-02
E-03
aTne quotients are calculated using the lowest acute LC$Q or TLm for fish in each RAC (Table A-l), the
lowest chronic response by a fish (Table A-2), and the lowest algal response (Table A-3) with either the
median or upper 95th percentile of the predicted ambient contaminant concentration (Table 2.2-3).
DNo effluent.
cNo toxicity data.
-------
ORNL/TM-9120
20
Table 3.1-2. Toxicity quotients for toxicity to fish and algae (ambient contaminant concentration/toxic
benchmark concentration) for Koppers-Totzek/Fischer-Tropsch process
Highest quotient*
RAC
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Fish (acute)
Name 95%
Carbon monoxide b
Sulfur oxides b
Nitrogen oxides b
Acid gases 1.95 E+00
Alkaline gases 1.22 E+00
Hydrocarbon gases b
Formaldehyde b
Volatile organochlorines b
Volatile carboxylic acids 1.65 E-02
Volatile 0 & S heterocyclics b
Volatile Nheterocyclics b
Benzene 1 .46 E-07
Aliphatic/alicyclic hydrocarbons 1.92 E-06
Mono- or diaromatic hydrocarbons 6.73 E-07
Polycyclic aromatic hydrocarbons 1.22 E-05
Aliphatic amines b
Aromatic amines b
Alkaline N heterocyclics b
Neutral N, 0, S heterocyclics b
Carboxylic acids b
Phenols 4.05 E-07
Aldehydes and'ketones 6.23 E-05
Nonheterocycl ic organosulfur b
Alcohols c
Nitroaromatics b
Esters 4.37 E-07
Amides b
Nitriles b
Tars b
Respirable particles b
Arsenic 4.54 E-04
Mercury 7.43 E-04
Nickel 2.21 E-04
Cadmium 1 .17 £-01
Lead 8.04 E-04
Other trace elements 3.98 E-01
Fish (chronic)
Median
b
b
b
c
c
b
b
b
c
b
b
c
c
1.11 E-06
c
b
b
b
b
b
6.40 E-07
6.10 E-05
b
c
0
1.78 E-05
b
b
b
b
5.4 E-04
3.46 E-02
4.16 E-03
3.08 E-02
1.13 E-02
3.62 E-03
Algae
Median 95%
b
b
b
c
c
b
b
b
c
b
b
6.58
c
2.09
4.88
b
b
b
b
b
7.01
c
b
c
b
1.29
b
b
b
b
1.16
9.94
4.54
1.05
4.30
c
b
b
b
c
c
b
b
b
c
b
b
E-10 1.47 E-09
c
E-08 4.69 E-08
E-09 8.99 E-09
b
b
b
b
b
E-08 1.57 E-07
c
b
c
b
E-06 2.90 E-06
b
b
b
b
E-03 2.61 E-03
E-05 2.23 E-04
E-03 1.02 E-02
E-02 2.34 E-02
E-04 9.64 E-04
c
The quotients are calculated using the lowest acute LC5n or TLm for fish in each RAC (Table A-l), the
lowest chronic response by a fish (Table A-2), and the lowest algal response (Table A-3) with either the
median or upper 95th percentile of the predicted ambient contaminant concentration (Table 2.2-4).
DNo effluent.
cNo toxicity data.
-------
21 ORNL/TM-9120
Barnthouse et al. (1982) discussed the uncertainties involved in
applying the quotient method to environmental data. One of the major
inherent problems is that of comparing results from dissimilar tests.
Although an attempt was made in this analysis to avoid such pitfalls
by comparing, when possible, the same test species and benchmarks,
uncontrolled variables inevitably remain. For example, in tests with
certain metals (RACs 33, 34, and 35), water hardness is important in
determining the concentrations of these metals that are required to
elicit a toxic response (Table 3.1-1), a fact reflected in the
USEPA criteria for each. Usually, the data are insufficient to compare
quotients from tests using the same organisms in both soft and hard
waters. Also, in some instances, the analyst must compare quotients
derived from tests using water of unspecified or inconsistent quality.
This exercise with the quotient method, in addition to suggesting
which of the assigned RACs pose the greatest potential environmental
threat, emphasizes the lack of toxicological research on algae as
important components of the ecosystem and on synfuels-related organic
compounds in general. Despite obvious weaknesses, the method does
provide a useful means of screening data from a variety of sources.
3.2 ANALYSIS OF EXTRAPOLATION ERROR
This method of risk analysis is based on the fact that application
of the results of laboratory toxicity tests to field exposures requires
a series of extrapolations, each of which is made with some error
(Barnthouse et al. 1982). The products of the extrapolation are
estimates of the centroid and distribution of the ambient concentration
of a chemical at which a particular response will occur. The risk of
occurrence of the prescribed response is equal to the probability that
the response concentration is less than the ambient concentration,
given the probability distribution of each. In this section, we
extrapolate from acute toxic concentrations for test species of fish to
chronic responses of the reference commercial and game species
characteristic of the eastern reference site (Travis et al. 1983). The
acute toxicity criterion is the 96-h LCrn. The chronic toxicity
-------
ORNL/TM-9120 22
criterion is the life-cycle maximum allowable toxicant concentration
(MATC), an interval bounded by the highest no-observed-effects
concentration and the lowest concentration causing a statistically
significant effect on growth, survival, or reproduction in a life-cycle
toxicity test (Mount and Stephan 1969). The geometric mean of the
bounds (GMATC) is used as a point estimate of the MATC, as was done in
calculating the national water quality criteria (USEPA 1980a-p).
3.2.1 Methods
The computational methods used for the analysis of extrapolation
error (AEE) were described by Suter et al. (1983). Acute toxicity data
from the Columbia National Fisheries Research Laboratory (Johnson and
Finley 1980) were used for the extrapolation between species.
Life-cycle toxicity data (Suter et al. 1983) were used to develop a
regression relationship between acute toxicity data and chronic
toxicity data. Variances associated with extrapolating acute toxicity
between taxa and acute to chronic toxicity were accumulated to provide
an estimate of the variability associated with the estimate of chronic
toxicity; they were also used in obtaining estimates of risk, given
estimates of the distribution of the ambient contaminant concentrations.
All of the emitted RACs for which 96-h LCcn's could be found
bU
(Table A-l) have been analyzed by the extrapolation error method.
The quotient of the ratio of the ambient concentration of a RAC to its
predicted GMATC (PGMATC) is presented as an estimate of the hazard of
chronic toxicity. Risk, which is defined as the probability that the
ambient contaminant concentration exceeds the GMATC, is also presented.
Both the hazard and risk estimates are based on the mean ambient
concentrations (Tables 2.2-3 and 2.2-4).
In general, the extrapolation between species was done using the
regression relationship between the tested and assessed fish at the
same taxonomic level and having in common the next higher level. For
example, if the fish are in the same family but different genera, the
extrapolation would be made between genera. There were three instances
when our hierarchical approach failed because of the limitation in the
acute toxicity data for the contaminant. The only acute toxicity data
-------
23 ORNL/TM-9120
available for hydrogen sulfide (RAC 4) and for fluoranthene (RAC 15)
were for bluegill sunfish (Lepomis macrochirus), and the only acute
toxicity data available for indan (RAC 13) and for quinoline (RAC 18)
were for fathead minnows (Pimephales promelas). Difficulties arose
with RAC 15 for estimating the acute toxicity of white bass (Morone
chrysops) and with RAC 13 for estimating the acute toxicity of bigmouth
and smallmouth buffalo (Ictiobus cyprinellus and I_. bulbalus). The
problem arose because no fish in the family Percichthyidaea or in the
genus Ictiobus were tested at the Columbia National Fisheries Research
Laboratory. The genus Ictiobus is in the family Catostomidae, which
was tested at the Columbia National Fisheries Research Laboratory, but
the Cyprinidae-Catostomidae relationship had insufficient sample size
(n = 1). Hence, further statistical relationships were developed
comparing bluegill sunfish with all Perciformes other than bluegills
p
(R = 0.91) and fathead minnow with all Cypriniformes other than
fathead minnow (R2 = 0.92).
3.2.2 Results
The species-specific values of the PGMATCs, quotients, and the
risks of exceeding the GMATC for the annual median ambient contaminant
concentrations are presented in Appendix D. These species-specific
values are presented only for those RACs with a hazard greater than or
equal to 0.01. They are summarized in Tables 3.2-1 and 3.2-2 for the
two technologies. Hydrogen sulfide (RAC 4) and ammonia (RAC 5), with
quotients and risks greater than 0.1 for all species and technologies,
appear to present the most consistent threat of chronic toxicity to
fish. For both technologies, the predicted risks of ammonia and
hydrogen sulfide are greater than 0.5 for most or all species.
Volatile carboxylic acids (RAC 9) appear to be as important as ammonia
and hydrogen sulfide for the Koppers-Totzek process. For the Lurgi
process, the quotients and risks for RAC 9 are substantially smaller
than those for RACs 4 and 5, but are still high enough to cause
concern. The only other RACs with hazard or risk values greater than
0.1 for any combination of species and technology are carboxylic acids
(RAC 20), arsenic (RAC 37), mercury (RAC 32), and cadmium (RAC 34).
-------
ORNL/TM-9120 24
Table 3.2-1. Ranges of predicted geometric means of maximum allowable toxicant
concentrations (PGMATC): ratios of ambient concentrations to PGMATC,
and probabilities of exceeding the PGMATC for the
Lurgi/Fischer-Tropsch process
Ratio of Probability of
RAC ambient concentration to PGMATC exceeding the PGMATCa
1
2
3
4b
5b
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
i-i
32b
32AD
33°
34b
35b
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No fish toxicity data
No effluent
No effluent
No effluent
No effluent
No fish toxicity data
No effluent
No fish toxicity data
No effluent
No effluent
No effluent
No effluent
No effluent
0.7580-5.6337
2.7565-6.6493
0.0756-0.3336
0.0000-0.0001
0.0000-0.0000
0.0003-0.0007
0.0003-0.0029
0.0002-0.0050
0.0008-0.0075
0.0000-0.0001
0.0000-0.0000
0.0162-0.1161
0.0024-0.0060
0.0072-0.0187
0.0011-0.0325
0.0011-0.1617
0.0024-0.0183
0.4529-0.7859
0.6832-0.8330
0.0945-0.3098
0.0000-0.0000
0.0000-0.0000
0.0000-0.0005
0.0000-0.0040
0.0000-0.1130
0.0001-0.0152
0.0000-0.0000
0.0000-0.0000
0.0123-0.1721
0.0023-0.0056
0.0101-0.0216
0.0008-0.0670
0.0001-0.1816
0.0004-0.0329
aSpecies-specific values are provided in Appendix D.
bAmbient concentration includes demineralizer regeneration wastewater.
-------
25 ORNL/TM-9120
Table 3.2-2. Ranges of predicted geometric means of maximum allowable toxicant
concentratons (PGMATC), ratios of ambient concentrations to PGMATC,
and probabilities of exceeding the PGMATC3 for the
Koppers-Totzek/Fischer-Tropsch process
RAC
1
2
3
4b
5b
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31b
32b
32Ab
33b
34b
35b
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No effluent
No fish toxicity
No effluent
No effluent
No effluent
No effluent
No effluent
Ratio of
ambient concentration to PGMATC
0.6753-5.0189
0.8527-2.0569
0.6911-3.0498
0.0000-0.0000
0.0001-0.0002
0.0000-0.0000
0.0000-0.0000
0.0000-0.0000
0.0001-0.0005
data
0.0000-0.0000
0.0056-0.0403
0.0002-0.0006
0.0007-0.0018
0.0005-0.0173
0.0007-0.0993
0.0005-0.0040
Probability of
exceeding the PGMATC3
0.4334-0.7701
0.4701-0.6435
0.4256-0.6930
0.0000-0.0000
0.0000-0.0000
0.0000-0.0000
0.0000-0.0000
0.0000-0.0000
0.0000-0.0003
0.0000-0.0000
0.0022-0.0792
0.0000-0.0002
0.0003-0.0008
0.0003-0.0343
0.0000-0.1246
0.0000-0.0034
aSpecies-specific values are provided in Appendix D.
bAmbient concentration includes demineralizer regeneration wastewater.
-------
ORNL/TM-9120 26
The differences in the relative rankings between species is
attributable to variation in three factors: (1) the magnitudes of
the LC 's of different species that have been tested for a
bO
particular chemical, (2) differences in sensitivity that are expressed
as biases in the extrapolation between the test species and site
species, and (3) the variance associated with the extrapolation.
3.2.3 Toxicity of the whole Effluent
Table 3.2-3 presents estimates of the acute toxicity of the whole
effluent. Only acute toxicity is considered because there is no theory
for modeling addition of effects expressed as toxic thresholds such as
GMATCs. The acute effects are expressed in a common unit, the 96-h
LC5Q to largemouth bass, which is generated by taxonomic
extrapolation from LC5Q data for a variety of species (Appendix A),
using the method of Suter et al. (1983).
The possible modes of joint action of chemicals are synergism,
concentration addition, independent action (response addition),
and antagonism (Muska and Weber 1977). Concentration addition is
generally accepted to be the best general model for describing the
combined effects of mixed chemicals on fish (Alabaster and Lloyd 1982;
EIFAC 1980; SGOMSEC, in press). In a recent review, Lloyd (in press)
stated: "There is no evidence for synergism (i.e., more-than-additive
action) between the common pollutants; at toxic concentrations the
joint action is additive and at concentrations below those considered
'safe' there is circumstantial evidence for less-than-additive joint
action." Furthermore, Parkhurst et al. (1981) found that when ammonia
speciation is accounted for, the toxicity of the major components of
two synfuels effluents was concentration additive. Therefore, we used
the concentration addition model to examine the potential toxicity of
the combined RACs.
The analysis was performed by calculating the total toxic units
(ITU) of the effluent, where a toxic unit is the concentration of a
toxicant divided by the threshold LC™ (Sprague and Ramsay 1965). We
used the upper 95th percentile of the predicted concentration, since
the concern in this case is with acute lethality, and we used the 96-h
-------
27
ORNL/TM-9120
Table 3.2-3.
Estimated acute LC^g for largemouth bass and ratio of
upper 95th percent!le of the ambient concentration to the
LCso for the Lurgi/Fischer-Tropsch and Koppers-Totzek/
Fischer-Tropsch processes
RAC
1
2
3
4a
5a
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31a
32a
32Aa
33a
34a
35a
Total
LC50
(mg/L)
No toxicity data
No toxicity data
No toxicity data
36.3
444
5,716,048
No toxicity data
52,048
10,511
No toxicity data
No toxicity data
4815
2324
2296
3310
No toxicity data
No toxicity data
6171
No toxicity data
184,876
14,282
160
No toxicity data
No toxicity data
No toxicity data
601
No toxicity data
9437
No toxicity data
No toxicity data
22,236
321
74.6
4496
1696
20,865
Concentration/LC
50
Lurgi Koppers-Totzek
5.42 E-01
6.05 E-01
No effluent
No effluent
1.52 E-02
3.97 E-06
5.48 E-07
4.56 E-05
3.59 E-05
No effluent
8.62 E-05
6.06 E-05
1.96 E-06
5.76 E-08
No effluent
7.84 E-04
5.84 E-04
2.51 E-03
4.64 E-04
1.12 E-04
1.06 E-04
1.17
4.82 E-01
1.87 E-01
No effluent
No effluent
1.39 E-01
1.61 E-07
1.15 E-05
6.74 E-07
1.48 E-07
No effluent
No effluent
2.20 E-07
1.79 E-05
5.30 E-07
No effluent
2.72 E-04
5.55 E-05
2.39 E-04
2.26 E-04
6.91 E-05
2.31 E-05
0.81
aAmbient concentration includes demineralizer regeneration wastewater.
-------
ORNL/TM-9120 28
LC™ as a reasonable approximation of the threshold LC™ (Ruesink
50 DU
and Smith 1975). The ZTU of 1.17 for Lurgi gasification suggests a
high likelihood of acute toxic effects from its effluent. This is
almost entirely due to RAC 4 (acid gases - hydrogen sulfide) and RAC 5
(alkaline gases - ammonia). The ZTU of 0.81 for Koppers-Totzek
gasification is less than unity, but Alabaster et al. (1972) found that
only "coarse fish" were present when the ZTU based on the upper 95th
percentile concentration and on rainbow trout 48-h LC^'s was above
0.6. Therefore, the total toxicity of this effluent, which is
primarily due to RAC 4, RAC 5, and RAC 9 (volatile carboxylic acids),
also seems worthy of concern.
3.3 ECOSYSTEM UNCERTAINTY ANALYSIS
3.3.1 Explanation of Method
Ecosystem uncertainty analysis (EUA) estimates the risk associated
with both direct and indirect effects of toxicants. It considers data
on a variety of test organisms rather than emphasizing a single
taxonomic group. By integrating effects across trophic levels, EUA
considers components of environmental risk that are not included in
other methods.
The method uses the Standard Water Column Model (SWACOM) (O'Neill
and Giddings 1979; O'Neill et al. 1982). SWACOM is an adaptation of
an earlier model, CLEAN (Park et al. 1974), and considers ten
phytoplankton, five zooplankton, three forage fish, and a game fish
population. The model simulates the annual cycle of a lake and
incorporates temperature, light, and nutrient responses. Changes can
be made to tailor SWACOM for toxicological assessments in a variety of
aquatic ecosystems. The model is designed to simulate a generalized
water column and sacrifices site specificity to emphasize complex
interactions and indirect effects.
Available toxicity data are primarily in the form of mortalities.
Therefore, assumptions about the mode of action of the toxicant are
required to determine changes in model parameters. We have assumed
that organisms respond to all chemicals according to a general stress
-------
29 ORNL/TM-9120
syndrome; that is, they increase their respiration rates, decrease
their photosynthetic and feeding rates, and become more susceptible to
predation. This assumption permits us to define percent changes in
model parameters that cause the same mortality as measured in the
laboratory. This extrapolation of laboratory data involves considerable
uncertainty. In our analysis, the uncertainties are preserved by
associating each parameter change with a probability distribution. In
calculating risk, parameter values are selected from the distributions
and a simulation is performed with SWACOM. The process is repeated
500 times. The risk associated with an undesirable effect, such as a
significant reduction in game fish, is estimated by the frequency of
simulations that showed this effect. Further details of the method
are given in Appendix E and by O'Neill et al. (1982).
The data used for the EUA are shown in Table 3.3-1. Estimates of
risk can be made for only ten RACs. These RACs were the only chemical
groups for which adequate data exist.
3.3.2 Results of Ecosystem Uncertainty Analysis
Results of the EUA for the direct liquefaction technologies are
shown in Figs. 3.3.1 to 3.3.3. Deterministic results are shown on
Table 3.3-2. None of the technologies produces measureable amounts of
quinoline (RAC 18), so this risk assessment unit was not considered in
the analysis. Environmental concentrations of benzene (RAC 12),
naphthalene (RAC 14), and phenol (RAC 21) were very low and did not
result in significant risks; therefore, results for these three
chemicals are not shown on the graphs.
Two endpoints were considered: a quadrupling of the peak biomass
of noxious blue-green algae and a 25% decrease in game fish biomass.
These endpoints were chosen as indicative of minimal effects that could
be quantified in the field. Risk estimates were calculated for these
endpoints across a range of environmental concentrations that
encompasses the 5th to 95th percentile exposures. The range of
exposures for each technology is shown at the bottom of the figures.
-------
Table 3.3-1.
Trophic
level
Algae
Zooplankton
Forage fish
Game fish
Values3 of LC50/EC5o (mg/L) used to calculate
Model
species
1-3
4-7
8-10
11
12
13
14
15
16
17
18
19
Ammon i a
420.0
420.0
420.0
8.0
8.0
8.0
8.0
8.0
1.1
8.2
23.7
0.41
Benzene
525.0
525.0
525.0
450.0
380.0
300.0
233.8
17.6
33.0
22.0
34.0
5.3
Naphthalene
33.0
33.0
33.0
8.6
8.6
6.5
4.5
2.5
6.6
78.3
150.0
2.3
ro
O
effects matrix for SWACOM
Quinoline
25.0
25.0
117.0
57.2
28.5
48.2
39.3
30.3
1.5
1.5
1.5
11.0
Phenol
258.0
20.0
95.0
300.0
36.4
58.1
157.0
14.0
36.0
16.4
34.9
9.0
Arsenic
2.32
2.32
2.32
4.47
5.28
1.35
2.49
0.51
15.6
41.8
26.0
13.3
Nickel
0.50
0.50
0.50
9.67
0.85
1.93
4.91
0.15
4.87
5.27
4.45
0.05
Cadmium
0.16
0.06
0.06
0.5
0.0099
0;14
0.25
0.0035
0.63
1.94
1.63
0.002
Lead
0.50
0.50
0.50
40.8
0.45
27.4
14.0
0.67
4.61
23.8
31.5
1.17
Mercury
0.01
0.01
0.01
0.78
0.005
0.53 c
0.27
0.01
0.15
0.24
0.50
0.25
aValues taken from following Water Quality Criteria documents: ammonia - Hohreiter (1980); benzene - USEPA (1980c);
naphthalene - USEPA (1980e); quinoline - O'Neill et al. (1982); phenol - USEPA (1980g); arsenic - USEPA (19801);
nickel - USEPA (1980n); cadmium - USEPA (1980o); lead - USEPA (1980p); and mercury - USEPA (1980m).
-------
31
ORNL/TM-9120
10
,-1
10'
I I I I I M | I I
NICKEL
ALGAE.
,K
H _
l i i i i i i I
i l l l l i ll
: ARSENIC
ORNL-DWG 83-16213
K
H
l i i i l i l i I
i i i i i i ii T i
LEAD
,K
-\ -
icr3 icr3 io~2 io~4
CONCENTRATION (mg L'1)
10
-3
Fig. 3.3.1. Risk estimates for three heavy metals over a range of
environmental concentrations. The 5th percentile, mean,
and 95th percentile concentrations associated with the
Lurgi/Fischer-Tropsch (L) and Koppers-Totzek/Fischer-Tropsch
(K) processes are shown at the bottom of each graph. The
plotted values are the probability of a quadrupling of the
blue-green algal bloom and a 25% reduction in game fish
biomass.
-------
ORNL/TM-9120
32
ORNL-DWG 83-16212
10"
MERCURY
ALGAE
K
L
10'
,-5
10"
r4
CONCENTRATION (mg L"1)
Fig. 3.3.2. Risk estimates for mercury over a range of environmental
concentrations. The 5th, mean, and 95th percentile
concentrations associated with the Lurgi/Fischer-Tropsch
(L) and Koppers-Totzek/Fischer-Tropsch (K) processes are
shown at the bottom of the graph. The plotted values are
the probability of a quadrupling of the blue-green algal
bloom and a 25% reduction in game fish biomass.
-------
ORNL-DWG 83-12717
1CT -
_ I
I I
ALGAE
CADMIUM
,L -J
.K
I I I
co
oo
10'
CONCENTRATION (mg L~1)
Fig. 3.3.3. Risk estimates for ammonia and cadmium over a range of
environmental concentrations. The 5th, mean, and
95th percentile concentrations associated with the
Lurgi/Fischer-Tropsch (L) and Koppers-Totzek/Fischer-Tropsch
(K) processes are shown at the bottom of each graph. The
plotted values are the probability of a quadrupling of the
blue-green algal bloom and a 25% reduction in game fish
biomass.
ro
O
-------
ORNL/TM-9120
34
Table 3.3-2.
Deterministic results of EUA (values are percent
increases in maximum algal bloom and percent decrease in
game fish biomass at the mean environmental concentration
for each of the indirect liquefaction technologies)
Ammonia
Benzene
Naphthalene
Phenol
Arsenic
Mercury
Nickel
Cadmium
Lead
Endpoint
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Algae
Fish
Lurgi/
Fischer-Tropsch
+306
-61
a
a
a
a
a
a
+16
-1
+102
-6
+89
-5
+368
-21
+15
-2
Koppers-Totzek/
Fischer-Tropsch
+15
-32
a
a
a
a
a
a
+6
-1
+7
-1
+32
+297
-15
+3
a
aPercent change is less than
-------
35 ORNL/TM-9120
The lines on the graph do not pass through the origin because
there is a risk of an increase in algae (0.086) or a decrease in fish
biomass (0.038) even as the environmental concentrations of the
toxicants approach zero. This reflects residual uncertainty in
simulating ecosystem effects. For example, there is always some
probability of a small decrease in fish biomass due to natural
variability.
Results for the four heavy metals show a similar pattern. In all
of these cases, there is an upturn in the risk curves, showing
significant risks at the higher concentrations generated by one or
both of the technologies. The increased risk of an effect to game fish
populations seems intuitively reasonable. However, the increasing
risk of a blue-green algal bloom with increasing concentration is
counterintuitive. Even though each of the chemicals is toxic to
the algae, the reduction in sensitive grazing organisms more than
compensates for the direct effect on phytoplankton. This is an
example of the indirect effects that EUA is capable of showing.
Results for ammonia and cadmium show considerably higher risk
values across the full range of environmental concentrations. The
high values occur for both endpoints and both technologies. The
results indicate that these two RACs should be of primary concern in
evaluating the environmental hazards of indirect coal liquefaction.
All of the graphs illustrate the complexity of the ecosystem
responses simulated by EUA. The relationship between concentration of
toxicant and risk is not simply linear or exponential. The complexity
of these responses results from the nonlinear interactions considered
in the analysis.
3.3.3 Comparison of Risks Across RACs
The importance of cadmium and ammonia is further emphasized in
Fig. 3.3.4. The graph shows the maximum risk associated with each
of the nine RACs. The maximum risk is defined as the risk associated
(1) with the upper 95th percentile concentration for whichever
technology showed the highest concentrations and (2) with either algal
blooms or a reduction in game fish biomass, whichever showed the higher
-------
ORNL/TM-9120 36
risk. Thus, the maximum risk attempts to separate RACs that never show
a significant risk from those that are significant in at least one of
the relevant calculations.
The figure shows that there is a very reasonable probability
that cadmium and ammonia could significantly affect the aquatic
ecosystem. In addition, the graph indicates that the other heavy
metals (RACs 31-35) could also cause problems, although these
probabilities are associated only with the highest concentrations
produced by the Lurgi/Fischer-Tropsch process.
3.3.4 Comparison of Risks Between Technologies
Figure 3.3.5 compares risks across the nine RACs for the two
technologies. The risk values are those associated with the upper
95th percentile concentrations. For each RAC, moving in a clockwise
direction, results are given first for the risk of algal blooms and
then for the risk of a reduction in game fish.
Because of consistently lower environmental concentrations, the
Koppers-Totzek technology shows slightly lower risks. However, because
of the large risks associated with ammonia (RAC 5) and cadmium (RAC 34)
and the smaller, but not insignificant, risks that appear for the other
heavy metals (RAC 31-35), neither technology can be considered to be
free of environmental risk.
-------
ORNL-DWG 83-12715
1.0 0.8 0.6 0.4 0.2 0 0.2 0.4
0.8 1.0
Fig. 3.3.4. Maximum risk estimates for nine RACs (indicated by numbers). The risk values are
associated with algal blooms or reductions in fish biomass, whichever was larger,
at the 95th percentile concentration of the technology with the higher
concentration.
oo
o
•73
-------
ORNL/TM-9120
38
ORNL-DWG 83-12714
LURGI
1.0 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 1.0
KOPPERS
1.0 0.8 0.6 0.4 0.2
0.2 0.4 0.6 0.8 1.0
Figure 3.3.5.
Comparison of risks between technologies. Risks at the
95th percentile concentration are shown first for the
algae and then for game fish, for each of nine RACs
(indicated by numbers).
-------
39 ORNL/TM-9120
4. TERRESTRIAL ENDPOINTS
The quotient method, as discussed in Sect. 3.1, consists of
dividing the ambient concentrations of toxicants by the concentration
at which some toxic effect is induced. It is used in this section to
provide an indication of the likelihood of effects due to emissions of
the individual RACs. The other risk analysis methods are not readily
applicable to terrestrial organisms because of the small toxicological
data base for most terrestrial taxa, the lack of standard tests and
toxicological benchmarks in the data base, and the lack of agreed-upon
standard responses for terrestrial biota.
4.1 VEGETATION
The phototoxicity data for the gaseous and volatile RACs are
presented in Table B-l, the concentrations in ambient ground-level air
are in Tables 2.3-1 and 2.3-2, and the quotients of the ratios of these
values are in Tables 4.1-1 and 4.1-2. The ambient concentrations are
the increment of the entire RAC to the background concentration at the
point of maximum ground-level concentration (Sect. 2.3). It is assumed
that the RAC is composed entirely of the representative chemical and
that the background concentration is zero. Quotients were calculated
from two classes of data: (1) the lowest toxic concentration found in
the literature for any flowering plant species, as an indication of
maximum toxic potential of the RAC, and (2) the range across studies of
the lowest concentrations causing effects on growth or yield of the
whole plant or some plant part. The latter set of responses is
relatively consistent and closely related to crop and forest yield.
The worst atmospheric toxicants in the emissions of both
technologies are hydrocarbon gases (RAC 6). This rank is biased, since
the worst-case representative chemical (ethylene) is a plant hormone,
whereas most members of this RAC are essentially inert (National
Research Council 1976). However, since atmospheric ethylene has caused
significant damage to crops near urban areas and near petrochemical
plants (National Research Council 1976), the emission rate of this gas
-------
Table 4.1-1. Toxicity quotients for terrestrial plants for the Lurgi/Fischer Tropsch process. Ambient concentrations in air (annual, median, ground-level)
and soil (soil solution or whole dry soil basis) are divided by concentrations causing reductions in growth, yield, or other toxic
responses.3
Air concentration/ Range of air concentration/ Soil concentration/ Range of soil concentration/
RAC RAC name lowest toxic concentration growth-effects concentration lowest toxic concentration growth-effects concentration
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile Nheterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or diaromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline nitrogen heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocyclic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
Other trace elements
Radioactive materials
2.57 E-04 4.21 E-08
1.31 E-01 2.18 E-02 - 6.54 E-02
1.76 E-02 9.23 E-04 - 1.76 E-02
3.00 E-05 3.00 E-05
1.74 E-06
34 1.64 E-02 - 5.71 E-02
c c
c c
c c
d d
c c
3.93 E-07
4.77 E-12
2.81 E-05
c c
c c
c c
c c
3.64 E-03
3.03 E-07 1.67 E-06
c c
c c
c c
c c
c c
1.91 E-06
b
b
b
b
b
c
c
d
c
3.91 E-04
6.14 E-06
1.02 E-01e
c
c
c
4.72 E-10
c
8.95 E-07
3.11 E-05
2.33 E-09e
9.01 E-08
c
c
c
c
c
b
1.23e
3.04 E-07
9.84 E-02e
7.6 E-03
2.58 E-02e
b
b
b
b
b
b
b
c
c
d
c
6.14 E-06
3.16 E-02 -
c
c
c
4.72 E-ll
c
3.11 E-05
c
c
c
c
c
b
5.78 E-02e
2.79 E-09 -
1.17 E-04 -
1.69 E-04 -
2.31 E-04 -
b
b
1.02 E-01e
- 4.72 E-10
- 1.23e
3.04 E-07
9.84 E-02e
7.6 E-03
2.58 E-02e
O
70
ro
o
-P.
o
aAir, soil, and soil solution concentrations are presented in Table 2.3-1. Toxic concentrations are presented in Appendix B.
"No accumulation in soil.
GNo emissions.
dNo phytotoxicity data.
eQuotients calculated from concentrations in soil and results of tests performed in soil. Quotients without superscript e were calculated from
concentrations in soil solution and results of tests performed in nutrient solution.
-------
Table 4.1-2. Toxicity quotients for terrestrial plants for the Koppers-Totzek/Fischer-Tropsch process. Ambient concentrations in air (annual, median,
ground-level) and soil (soil solution or whole dry soil basis) are divided by concentrations causing reductions in growth, yield, or other
toxic responses.3
RAC RAC name
1 Carbon monoxide
2 Sulfur oxides
3 Nitrogen oxides
4 Acid gases
5 Alkaline gases
6 Hydrocarbon gases
7 Formaldehyde
8 Volatile organochlorines
9 Volatile carboxylic acids
10 Volatile 0 & S heterocyclics
11 Volatile Nheterocyclics
12 Benzene
13 Aliphatic/alicyclic hydrocarbons
14 Mono- or diaromatic hydrocarbons
15 Polycyclic aromatic hydrocarbons
16 Aliphatic amines
17 Aromatic amines
18 Alkaline N heterocyclics
19 Neutral N, 0, S heterocyclics
20 Carboxylic acids
21 Phenols
22 Aldehydes and ketones
23 Nonheterocyclic organosulfur
24 Alcohols
25 Nitroaromatics
26 Esters
27 Amides
28 Nitriles
29 Tars
30 Respirable particles
31 Arsenic
32 Mercury
33 Nickel
34 Cadmium
35 Lead
36 Other trace elements
37 Radioactive materials
Air concentration/
lowest toxic concentration
1.24 E-02
1,06 E-01
2.82 E-02
4.82 E-04
1.76 E-08
43.5
c
c
c
c
c
1.90 E-09
5.85 E-12
3.47 E-05
c
c
c
c
c
c
3.94 E-03
2.47 E-04
c
c
c
c
c
4.28 E-04
Range of air concentration/
growth-effects concentration
2.04 E-06
1.76 E-02 - 5.28 E-02
1.48 E-03 - 2.82 E-02
4.82 E-04
2.09 E-02 - 7.30 E-02
c
c
c
c
c
c
c
c
c
c
c
8.10 E-04
c
c
c
c
c
Soil concentration/
lowest toxic concentration
b
b
b
b
b
c
c
c
c
c
4.80 E-04
7.57 E-06
4.78 E-02
c
c
c
c
c
c
4.39 E-05
1.13 E-06d
3.45 E-06
c
c
c
c
b
0.12d
6.83 E-05
6.0 E-02d
1.36 E-02
1.95 E-03d
b
b
Range of soil concentration/
growth-effects concentration
b
b
b
b
b
c
c
c
c
c
7.57 E-06
4.78 E-02 -
c
c
c
c
c
c
4.39 E-05
c
c
c
c
b
5.63 E-03d -
6.27 E-07 -
7.12 E-05 -
3.01 E-04 -
1.74 E-05 -
b
b
7.75 E-02b
0.12d
6.83 E-05
6.0 E-02d
1.36 E-02
1.95 E-03d
aAir, soil, solution concentrations are presented in Table 2.3-2. Toxic concentrations are presented in Appendix B.
bNo accumulation in soil.
cNo emissions.
dQuotients calculated from concentrations in soil and results of tests performed in soil. Quotients without superscript d were calculated from
concentrations in soil solution and results of tests performed in nutrient solution.
o
•yo
10
_^
ro
o
-------
ORNL/TM-9120 42
should be specifically considered in the future. The most serious
phytoxicants in air (ignoring ethylene) are S0x and N0x. The
maximum annual average concentrations predicted for S02 (RAC 2) from
Lurgi and Koppers-Totzek are within a tenth of those that cause visible
injury to needles of sensitive white pines, and both SO,, and N0x
(RAC 3) concentrations are greater than a hundredth of those that
reduce growth or yield of several plant species.
Because of its ubiquity and importance as a phytotoxicant, SO,,
(RAC 2) has been well studied for its effects on crop yield.
Mclaughlin and Taylor (in press) proposed the following dose-response
relationship for yield reduction in beans as a function of SOp
exposure:
% yield reduction = -17.4 + 29.2 (log dose in ppmh).
This empirical relationship is based on a regression of 20 points from
five field experiments on soybeans and snap beans. Eighty percent of
the variation in yield reduction was associated with variation in
dosage, and the equation was significant at a = 0.0001.
Because S0? appears to be the most serious phytotoxic air
pollutant, we use this relationship to examine the potential effects
of full-growing-season exposure to S0« from Koppers-Totzek on
crop yield. If we assume a 200-d growing season for soybeans on
the eastern site and a 12-h exposure day, the SO,, dose at
3
6.87 ug/m S02 is 6.25 ppmh. That dose results in a 5.8%
reduction in yield by Mclaughlin and Taylor's formula.
This predicted effect is remarkable in that it results from an
S02 concentration that is more than 10 times lower than the lowest
concentration reported to affect yield. This anomaly is due to the
great length of a growing season relative to the length of experiments.
The longest fumigation available to Mclaughlin and Taylor was 337 h.
Thus, use of their formula for a full growing season requires an
extrapolation of almost a factor of 10 in the duration component of the
dose. Because the experimental field fumigations are typically carried
out in the most sensitive stage (assumed to be the pod-fill in the case
of beans), use of the formula for the full growing season probably
overestimates effects.
-------
43 ORNL/TM-9120
We might place a lower bound on the level of effect by assuming
that effects occur only during pod-fill. If that stage is assumed to
last 30 d, the dose is 0.99 ppmh. This is less than a quarter of the
threshold dose for effects on yield (3.92 ppmh).
For an actual synfuels plant, this S0? emission would be added
o
to a background S02 concentration that may reach 80 u9/m under
the current annual average ambient air quality standard and would
interact with ozone, which reaches phytotoxic levels in many areas of
the United States. This analytical exercise demonstrates the need for
the full-season field experiments on effects of S0? and S0? + 0~
originally planned for the USEPA's National Crop Loss Assessment
Network.
The phytotoxicity of materials deposited on the landscape is a
more complex phenomenon than that of gases and vapors. Because the
atmospheric transport model AIRDOS-EPA has a deposition velocity of
zero for inorganic gases and does not model the formation of aerosols,
it is assumed that RACs 1 through 5 do not accumulate in the soil.
This assumption is likely to be acceptable except in the case of SO.
deposition in forests with acid soils. The effects of SO, deposition
in forests result from regional-scale emissions and atmospheric
processes and are therefore beyond the scope of this report. Deposited
nongaseous RACs were assumed to accumulate in the soil over the 35-year
life of the liquefaction plant. Losses due to decomposition
and leaching from the root zone were calculated by the terrestrial food
chain model (Sect. 2.3). The toxicity data (Table B-3) were primarily
derived from exposure of plants or plant parts to solutions of the
chemicals rather than to contaminated soil because few data are
available on toxicity in soil. Whereas the results of tests done in
soil can be directly compared with concentrations in whole soil,
results of tests done in solution must be compared with a calculated
concentration in soil solution. Because the concentration in soil
solution is more difficult to model than concentration in whole soil
and requires more simplifying assumptions, solution concentrations are
less reliable. In addition, as with the gases and vapors, the toxicity
-------
ORNL/TM-9120 44
data are from a wide variety of tests and measured responses that
are not equivalent. Finally, for most of the RACs, only one or two
chemicals have been tested. We cannot determine if the chemicals used
are representative of the entire RAC.
The most phytotoxic RACs deposited in soil are polycyclic aromatic
hydrocarbons (PAHs) (15), arsenic (31), cadmium (34), nickel (33), and
lead (35). The high rank of RAC 15 is suspect because benzo(a)pyrene
and some other PAHs appear to act as plant hormones and can stimulate
growth at very low concentrations. Thus, while PAHs can modify plant
growth at concentrations as low as 0.5 ng/g soil, there is no evidence
that they reduce plant growth, even at relativity high experimental
concentrations (Edwards, 1983). Therefore, heavy metals appear to be
the most serious soil pollutants, and methods for predicting their
effects require attention.
4.2 WILDLIFE
Tables 4.2-1 and 4.2-2 present the lowest toxicity quotients for
terrestrial animals for the two technologies. The quotients are
calculated from the lowest lethal concentration for any species and
from the lowest concentration producing any toxic effect (Table B-3)
divided by the highest annual average ground-level concentration in
air. Data from all species were pooled because there were not enough
data on the nonmammalian taxa for separate treatment. Carcinogenesis
and other genotoxic effects were not included.
Lethality is considered because it is a consistent and frequently
determined response that has clear population implications, but all
predicted concentrations are well below lethal levels. The lowest
toxic concentrations include a diversity of endpoints, most of which
cannot be readily related to effects on wildlife populations but which
occur at concentrations that are as low as a ten-thousandth of lethal
concentrations. These responses range from increased airway resistance
in 1-h exposures of guinea pigs to impaired lung and liver function
in human occupational exposures. The most toxic RACs by this sublethal
criterion are the conventional combustion products sulfur oxides
-------
45
ORNL/TM-9120
Table 4.2-1.
Toxicity quotients for terrestrial animals for the
Lurgi/Fischer-Tropsch process. Concentrations in air
(annual, median, ground-level) are divided by lethal
concentrations and the lowest toxic concentrations.3
RAC
Name
Lowest lethal
concentration
Lowest toxic
concentration
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile Nheterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or di aromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocyclic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
5.03 E-10
4.72 E-04
1.60 E-04
4.00 E-08
5.21 E-06
b
b
b
2.62 E-ll
b
6.21 E-08
5.80 E-08
3.53 E-06
b
b
b
b
5.06 E-05
5.45 E-09
3.04 E-06
b
b
b
b
b
4.42 E-09
2.78 E-09
1.08 E-05
8.5 E-02
3.93 E-03
1.20 E-07
2.81 E-07
1.06 E-07
b
b
b
2.62 E-ll
b
6.21 E-08
3.81 E-06
6.70 E-05
b
b
b
b
1.78 E-03
8.18 E-08
5.27 E-05
b
b
b
b
b
6.28 E-02
3.06 E-05
1.12 E-07
4.42 E-09
1.39 E-06
2.56 E-05
aAmbient air concentrations are presented in Table 2.3-1
concentrations are presented in Appendix B.
Toxic
bNo emissions.
-------
ORNL/TM-9120
46
Table 4.2-2.
Toxicity quotients for terrestrial animals for the
Koppers-Totzek/Fischer-Tropsch process. Concentrations in
air (annual, median, ground-level) are divided by lethal
concentrations and the lowest toxic concentrations.3
RAC
Name
Lowest lethal
concentration
Lowest toxic
concentration
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile Nheterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or di aromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones
Nonheterocyclic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
2.43 E-08
3.82 E-04
2.57 E-04
6.43 E-07
5.29 E-ll
b
b
b
b
b
3.01 E-10
7.12 E-08
4.35 E-06
b
b
b
b
b
b
5.48 E-05
2.65 E-06
7.92 E-06
b
b
b
b
b
7.92 E-09
1.14 E-08
5.21 E-04
6.87 E-02
6.30 E-03
2.93 E-06
2.85 E-09
1.35 E-07
b
b
b
b
b
3.01 E-10
4.68 E-06
8.25 E-05
b
b
b
b
b
b
1.93 E-03
3.97 E-05
1.37 E-04
b
b
b
b
b
2.76 E-01
6.08 E-06
2.52 E-05
7.92 E-05
5.68 E-06
3.66 E-06
aAmbient air concentrations are presented in Table 2.3-2.
concentrations are presented in Appendix B.
bNo emissions.
Toxic
-------
47 ORNL/TM-9120
(2) and respirable particulates (30). Although these concentrations
may constitute a locally significant increment to the background
concentration of these major pollutants, the significance of ambient
air pollution to wildlife is largely unknown. The assumption that
protection of human health will automatically protect wildlife is not
scientifically defensible.
-------
ORNL/TM-9120 48
5. EVALUATION OF RISKS
5.1 EVALUATION OF RISKS TO FISH
Table 5.1-1 lists, for each technology, the RACs determined to be
potentially ecologically significant by one or more of the three methods
employed in this report. The significance criterion for the quotient
method is an acute-effects quotient greater than 0.01, i.e., the lowest
observed LC5Q or TLM9g less than a hundred times the estimated
environmental concentration. For analysis of extrapolation error,
RACs are considered to be significant if the risk that the environmental
concentration may exceed the PGMATC of one or more of the reference
fish species is greater than 0.1. For ecosystem uncertainty analysis,
RACs are considered to be significant if the risk of a 25% reduction in
game fish biomass is greater than 0.1.
A total of nine RACs were determined to be significant for one or
more technologies. RAC 5 (ammonia) and RAC 34 (cadmium) were the only
RACs found to be significant for both technologies and all risk analysis
methods. RAC 4 (acid gases) was significant for both technologies
according to the quotient method and analysis of extrapolation error;
however, this RAC could not be addressed using ecosystem uncertainty
analysis. In general, analysis of extrapolation error rated the
organic RACs substantially more hazardous, relative to the inorganic
RACs, than did the other two methods. The reasons for these differences
in sensitivity among methods are not clear at this time.
The exposure analyses, the significance criteria, and the methods
themselves are conservative; therefore, it would be premature to
conclude that adverse consequences would result from the contaminant
releases assessed in this report. These nine RACs should, however,
be used in future refinements of the risk analyses and in future
toxicological and ecological research. In addition to the RACs listed
in Table 5.1-1, there are three RACs for which nonzero exposures were
estimated but no applicable toxicity data were available: RACs 10
(volatile 0 & S heterocyclics), 19 (neutral N, 0, and S heterocyclics,
and 24 (alcohols).
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49 ORNL/TM-9120
0,0]
Table 5.1-1. RACs determined to pose potentially significant risks to fish
populations by one or more of three risk analysis methods: quotient
method (QM), analysis of extrapolation error (AEE), and ecosystem
uncertainty analysis (EUA)
Lurgi/Fischer-Tropsch process Koppers-Totzek/Fischer-Tropsch process
4 (acid gases) - QM, AEE 4 (acid gases) - QM, AEE
5 (alkaline gases) - QM, AEE, EUA 5 (alkaline gases) - QM, AEE, EUA
9 (volatile carboxylic acids) - AEE 9 (volatile carboxylic acids) - QM, AEE
20 (carboxylic acids, excluding 34 (cadmium) - QM, AEE, EUA
volatiles) - AEE
31 (arsenic) - AEE
32 (mercury) - AEE, EUA
33 (nickel) - EUA
34 (cadmium) - QM, AEE, EUA
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ORNL/TM-9120 50
There are two ways to compare the two technologies for ecological
risk. It was shown, using the toxic units approach (Sect. 3.2-3), that
the Lurgi/Fischer-Tropsch effluent has a somewhat greater potential
for acute toxicity to fish. A similar conclusion can be reached by
inspecting Table 5.1-1. The differences between the two processes
appear to be less important than their similarities. For both,
conventional pollutants, especially acid gases (RAC 4) and ammonia
(RAC 5), appear to be substantially more hazardous than the complex
organic contaminants usually associated with synfuels.
5.2 EVALUATION OF RISKS OF ALGAL BLOOMS
Algal toxicity data were available for only ten RACs. Moreover,
because of the diversity of experimental designs and test endpoints
used in algal bioassays, it is not meaningful to rank the RACs using
the quotient method. Finally, as noted in Sect. 3.1, there is no clear
distinction between acute effects and chronic effects in algal
bioassays.
It does appear, however, that most of the quotients that can be
calculated are lower for algae than for fish; only RACs 33 and 34 would
be judged significant for any technology using the quotient method.
Ecosystem uncertainty analysis suggests greater risks of effects
on algae than does the quotient method. Risks of 10% or more of a
fourfold increase in algal biomass for one or more technologies were
estimated for six of the nine RACs examined: 5, 31, 32, 33, 34, and
35. The effects pathway postulated in ecosystem uncertainty analysis
is indirect rather than direct. All of the RACs are toxic to algae.
The increases in algal biomass are caused by reductions in grazing
intensity related to the effects of contaminants on zooplankton and
fish.
5.3 EVALUATION OF RISKS TO VEGETATION AND WILDLIFE
The greatest threat to terrestrial biota from indirect coal
liquefaction appears to be the gases SO- (RAC 2 - sulfur oxides) and
N02 (RAC 3 - nitrogen oxides). The concentrations of S02 for both
technologies are near phytotoxic levels. Interactions between these
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51 ORNL/TM-9120
gases and their combined effects with background ambient pollution
deserve additional attention. The effects of acute exposures from
"plume strikes" are also likely to be important and deserve attention.
Air pollutants do not appear to be a threat to mammalian wildlife, but
the sensitivity of nonmammalian species is largely unknown.
Of the materials deposited on the soil, the trace elements arsenic,
cadmium, and nickel cause the greatest concern. However, they are
unlikely to be a problem except when deposited on soils having
preexisting high concentrations of trace elements and chemical
properties that favor the solution phase.
5.4 VALIDATION NEEDS
There are no uniquely correct methods of quantifying ecological
risks. There are several plausible ways to combine uncertainties
concerning differential sensitivities of fish taxa and acute-chronic
relationships. Similarly, there are many aquatic ecosystem models, and
different models produce different estimates of uncertainty and risk.
Validation studies of the methods used in these risk analyses would
greatly increase the credibility of the results.
There are two ways in which these synfuels risk analyses can be
validated. A specific validation would involve building a synfuels
industry and monitoring the resulting environmental effects. A generic
validation would involve checking the assumptions and models used in
the risk analyses against the results of field and laboratory studies.
Given the current state of the synfuels industry, a generic validation
seems more practical.
Generic validation of the environmental risk analysis methods
would begin with an examination of the capability of existing published
evidence to support or refute the models or their component
assumptions. To a certain extent, this has been done by us as a part
of our methods development (e.g., Suter et al. 1983, Suter and Vaughan
1984), and by others for generally used models such as the Gaussian
plume atmospheric dispersion model. However, there has been no
systematic consideration of such major assumptions as the validity of
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ORNL/TM-9120 52
hydroponic phytotoxicity studies nor of the risk analysis methodology
as a whole. The results of validation studies would indicate not only
the level of confidence that can be placed in environmental risk
analyses, but also the research needed for further development and
validation of risk analysis methods.
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53 ORNL/TM-9120
6. ACKNOWLEDGMENTS
The authors thank R. E. Millemann and J. W. Webb for their
thorough reviews of this report. We also thank S. G. Hildebrand and
A. A. Moghissi for their support and encouragement in this project.
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ORNL/TM-9120 54
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Siegel, B. Z., and S. M. Siegel. 1979. Biological indicators of
atmospheric mercury, pp. 131-159. IN J. 0. Nriagu (ed.), The
Biogeochemistry of Mercury in the Environment.
Elsevier/North-Holland Biomedical Press, New York.
Smith, L. L., D. M. Oseid, 6. L. Kimball, and S. M. Elkandelgy. 1976.
Toxicity of hydrogen sulfide to various life history stages of
bluegill (Lepomis macrochirus). Trans. Am. Fish. Soc. 105:442-449.
Southworth, G. R. 1979. Transport and transformation of anthracene in
natural waters, pp. 359-380. IN L. L. Marking and R. A. Kimmerle
(eds.), Aquatic Toxicology. ASTM STP 667. American Society for
Testing and Materials, Philadelphia.
Sprague, J. B. and B. A. Ramsay. 1965. Lethal levels of mixed
copper-zinc solutions for juvenile salmon. J. Fish. Res. Board
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Stahl, Q. R. 1969. Air Pollution Aspects of Mercury and Its Compounds.
Litton Systems, Inc., Bethesda, Maryland.
Suter, G. W., II, and D. S. Vaughan. 1984. IN K. E. Cowser and
C. R. Richmond (eds.), Synthetic Fossil Fuel Technologies:
Results of Health and Environmental Studies. Ann Arbor Science
Publishers, Ann Arbor, Michigan.
Suter, G. W., II, D. S. Vaughan, and R. H. Gardner. 1983. Risk
assessment by analysis of extrapolation error: A demonstration
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2:369-378.
Taylor, G. E., Jr. (in press). The significance of the developing
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J. Am. Soc. Hort. Sci.
-------
65 ORNL/TM-9120
Travis, C. C., C. F. Baes III, L. W. Barnthouse, E. L. Etnier,
G. A. Holton, B. D. Murphy, S. J. Niemczyk, 6. P. Thompson,
G. W. Suter II, and A. P. Watson. 1983. Exposure assessment
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synthetic fuels technologies: IN direct coal liquefaction
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California.
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dichloroethyl ether as soil fumigants for the woolly aphid,
Eroisoma lanigerum Hausm. Virginia Fruit 28:20-26.
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Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980b. Ambient Water Quality
Criteria for Chloroform. EPA 440/5-80-033. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980c. Ambient Water Quality
Criteria for Benzene. EPA 440/5-80-018. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980d. Ambient Water Quality
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Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980e. Ambient Water Quality
Criteria for Napthalene. EPA 440/5-80-059. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
-------
ORNL/TM-9120 66
U.S. Environmental Protection Agency. 1980f. Ambient Water Quality
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Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
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Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
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Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980i. Ambient Water Quality
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Washington, D.C.
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Washington, D.C.
-------
67 ORNL/TM-9120
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-------
ORNL/TM-9120 68
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-------
69 ORNL/TM-9120
APPENDIX A
Aquatic Toxicity Data
-------
Table A-l. Acute toxicity of synfuels chemicals to aquatic animals.
RAC
Representative
chemical(s)
Test
organism3
Duration
Test type (h)
Concentration
(mg/L)
Notes
Reference
1 Carbon monoxide
2 Sulfur oxides
3 Nitrogen oxides
4 H2S
Ammon i a
6 Heptane
7 Formaldehyde
8 Carbon tetrachloride
Chloroform
9 Acetic acid
Scud (Gammarus
pseudolimnaeus)
Bluegill
(Adults)
(Juveniles)
(Fry, 35-day-old)
(Eggs)
Northern pike
(Eggs)
(Fry)
Rainbow trout
(Fry, 85-day-old)
(Adults)
Rainbow trout
Rainbow trout
Rainbow trout (Fry)
(Fingerlings)
Mosquitofish
Several fish sp.
Daphnia magna
Fathead minnow
Bluegill
Bluegill
D. magna
BluegTTT
Bluegill
Rainbow trout
Fathead minnow
Mosquitofish
TLm
TLm
TLm
TLm
TLm
TLm
TLm
TLm
LC5°
ir50
[Cr50
LC50
TLm
5°
LC;
'50
LCi
LC;
LC;
LC;
TLm
96
96
96
96
72
96
96
24
24
24
24
24
24
96
24
48
96
96
96
96
96
96
96
96
0.022
0.0448
0.0478
0.0131
0.0190
0.034-
0.037
0.009-
0.026
0.068
0.097
0.50
0.47
0.2
0.2
4,924
50-120
35.2
43.1
27.3
125.0
28.9
100.0
115.0
43.8
88.0
251.0
No toxicity data
Aquatic problems
associated with pH,
not direct toxicity
Aquatic problems
associated with pH,
not direct toxicity
Flow-through test
Flow-through test
Flow-through test
Flow-through test
DO=2-6 ppm
DO=2-6 ppm
Flow-through test
Oseia and Smith, 1974
Smith et al., 1976
Smith et al., 1976
Smitn et al., 1976
Smith et al., 1976
Adelman and Smith, 1970
Adelman and Smith, 1970
Rice and Stokes, 1975
Kice and Stokes, 1975
Herbert and Shurben, 1963
Lloyd and Orr, 1969
LIFAC, 1970
EIFAC, 1970
Wallen et al., 1957
National Research
Council, 1981
U.S. EPA, 1980a
U.S. EPA, 1980a
U.S. EPA, 1980a
U.S. EPA, 1980a
U.S. EPA, 1980b
U.S. EPA, 1980b
U.S. EPA, 1980b
U.S. EPA, 1980b
Mattson et al, 1976
Wallen et al., 1957
I
UD
ro
o
-------
Table A-l. (continued).
Representative
KAC chemical (s)
10 Volatile O&S
heterocyclics
11 Pyridine
12 Benzene
13 Cyclohexane
Indan
14 Toluene
Naphthalene
Xylene
1 5 Anthracene
Test
organism3
Ciliate (Tetrahymena
pyriforma')
D. magna
IT. magna
D. magna
D. magna
Fathead m i n n ow
Fathead minnow
Mosquitofish
Rainbow trout
Fathead minnow
Fathead minnow
Fathead minnow
Bluegill
Fathead minnow
D. magna
Fathead minnow
Fathead minnow
Bluegill
Bluegill
D. magna
U. magna
Fathead minnow
Fathead minnow
Rainbow trout
Fathead minnow
Goldfish
Test type
LC,50
LC50
LC50
LC50
LC5°
LC30
LC50
LC50
TLm
TLm
TLm
LC50
LC50
TLm
TLm
TLm
LC50
LC50
LC50
'-'-50
LC50
TLm
TLm
Duration
(h)
72
48
48
48
48
96
96
96
96
96
96
96
96
96
48
96
96
96
96
48
48
48
96
96
96
96
Concentration
(mg/L)
1211.8
1165
1755
203.0-
620.0
426.0
32.0
15.1
1300.0
5.3
93.0
42.3
32.7
34.7
14.0
39.22
42.3
34.3
24.0
12.7
2.16
8.57
3.14
4.90-
8.90
2.30
42.0
17.0
Notes
No toxicity data
50% growth
inhibition
Flow-through test
Flow-through test
Hard water
Soft water
Soft water
Hard water
Soft water
Soft water
2 tests
Not toxic to fish,
Reference
Schultz et al., 1980
Canton and Adema, 1978
Canton and Adema, 1978
U.S. EPA, 1980c
Canton and Adema, 1978
U.S. EPA, 1980c
DeGraeve et al., 1982
Wallen et al., 1957
U.S. EPA, 1980c
Mattson et al . , 1976
Pickering and
Henderson, 1966a
Pickering and
Henderson, 1966a
Pickering and
Henderson, 1966a
Mattson et al., 1976
Millemann et al . 1984
Pickering and
Henderson, 1966a
Pickering and
Henderson, 1966a
Pickering and
Henderson, 1966a
U.S. EPA, 1980d
Millemann et al., 1984
U.S EPA, 1980e
Millemann et al., 1984
U.S EPA, 1980e
U.S EPA, 1980e
Mattson et al., 1976
Brenniman et al., 1976
McKee and Wolf, 1963
O
72
\ —
— t
1
ro
o
— i
even in super-
saturated solutions
-------
Table A-l. (continued).
RAC
16
17
18
19
20
21
Representative
chemical (s)
Phenanthrenc
Fluorantnene
Aliphatic amines
Aniline
3, 5-Dimethyl aniline
Quinoline
2-Methylquinoline
2,6-Dimethylquinol ine
Neutral N,0,S
heterocyclics
Benzoic acid
Phenol
2-Methyphenol
Test
organism8
D. magna
ET. magna
Rainbow trout
(Embryo- larvae)
D. magna
Bluegill
D. magna
Daphnia cucullata
D. magna
J). magna
Ciliate (T. pyriforma)
D. magna
Fathead minnow
Fathead minnow
Ciliate (T. pyriforma)
Ciliate (]_. pyriforma)
Mosquitof ish
D. magna
TT. magna
U. magna (Young)
Copepod (Mespcyclops
leukarti)
Fathead minnow
Fathead minnow
Bluegill
Rainbow trout
D. magna
IT. magna
Fathead minnow
Fathead minnow
Test type
LCso
LCso
LC50
LCso
LC50
LC50
50
50
LC50
LCso
EC50
"SO
TLm
LC50
TLm
LC50
50
50
LC50
LC50
LCso
TLm
TLm
Duration
(h)
48
48
96
48
96
48
48
48
48
72
48
48
96
72
72
96
48
50
48
96
48
48
96
96
Concentration
(mg/L)
0.75
1.10
0.04
325.0
3.9
0.65
0.68
0.58
1.29
125.7
30.28
1.50
46.0
48.7
33.0
180
19.79
9.6
7.0
108.0
25.6
24.0-
67.5
11.5-
23.9
8.9-
11.6
9.2
23.5
12.55
13.42
Notes
No toxicity data
50% growth
inhibition
50% growth
inhibition
50% growth
inhibition
No toxicity data
4 tests
6 tests
2 Flow-through
tests
Soft water
Hard water
Reference
Millemann et al., 1984
Parkhurst, 1981
Birge and Black, 1981
U.S. EPA, 1980f
U.S. EPA, 1980f
Canton and Adema, 1978
Canton and Adema, 1978
Millemann et al., 1984
Millemann et al., 1984
Schultz et al., 1980
Millemann et al., 1984
Millemann et al., 1984
Mattson et al., 1976
Schultz et al., 1980
Schultz et al., 1980
Wallen et al., 1957
Millemann et al., 1984
U.S. EPA, 1980g
Oowden and Bennett, 1965
U.S. EPA, 1980g
Millemann et al., 1984.
U.S. EPA, 1980g
U.S. EPA, 1980g
U.S. EPA, 1980g
U.S. EPA, 1980g
U.S. EPA, 1980g
Pickering and
Henderson, 1966a
Pickering and
Henderson, 1966a
•vj
co
o
73
•z.
-H
2
1
ro
0
-------
Tabl
RAC
22
23
24
25
26
e A-l . (continued) .
Representative
chemical (s)
4-Methylphenol
Mixed cresol isomers
2,4-Dimethylphenol
3, 4-Di methyl phenol
2, 5-Ui methyl phenol
Acrolein
Acetaldehyde
Acetone
Nonheterocylic
organo sulfur
Alcohols
Nitroaromatics
Di-2-ehtylhexyl
phthalate
Diethyl phthalate
Butylbenzl phthalate
Test
organism3
Bluegill
Fathead minnow
Aquatic life
D. magna
Fathead minnow
(Juvenile)
Bluegill
Fathead minnow
D. magna
D. magna
D. magna
Mosquitof ish
Bluegill
Bluegill
Brown trout
Rainbow trout
Largemouth bass
Bluegill
D. magna
D. magna
D. magna
Bluegill
D. magna
D. magna
Fathead minnow
Fathead minnow
Bluegill
Test type
TLm
TLm
TLm
LC50
LC50
LC50
50
50
50
LC50
LC50
LC5Q
LC50
LC50
LC5Q
LC50
LC50
LC50
LC50
LC50
LC5Q
LC50
LC50
LC50
Duration
(h)
96
96
96
48
96
96
96
48
48
48
48
96
96
24
24
96
96
48
48
96
96
Concentration
(mg/L) Notes
20.78 Soft water
19.0
1.0-10.0
2.12
16.75 Flow-through test
7.75
14.0
0.96
0.057
0.080
0.061
0.100
0.090
0.046
0.065
0.160
53.0
12,600
No toxicity data
No toxicity data
No toxicity data
11.1
52.1
98.2
92.3
3.7
5.3 160 mg/L hardness
as CaC03
2.1 40 mg/L hardness
43.3
Reference
Pickering and
Henderson, 1966a
Mattson et al., 1976
Kingsbury et al., 1979
U.S. EPA, 1980h
U.S. EPA, 1980h
U.S. EPA, 1980h
Mattson et al., 1976
Millemann et al . , 1984
U.S. EPA, 19801
U.S. EPA, 1980i
National Research
Council, 1981
U.S. EPA, 19801
U.S. EPA, 19801
National Research
Council, 1981
National Research
Council, 1981
U.S. EPA, 19801
National Research
Council, 1981
Canton and Adema, 1978
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
Gledhill et al., 1980
Gledhill et al . , 1980
Gledhill et al., 1980
U.S. EPA, 1980J
ORNL/TM-1
-------
Table A-l. (continued).
Representative
RAC chemical (s)
Di-n-butyl phthalate
27 Amides
28 Acrylonitrile
29 Tars
30 Respirable particles
31 Arsenic
32 Mercury (inorganic)
Methylmercury
Test
organism3
Bluegill
Rainbow trout
Scud (G. pseudo-
limnaeus)
Fathead minnow
Bluegill
Rainbow trout
D. magna
Fathead minnow
Fathead minnow
Fathead minnow
Bluegill
Bluegill
D. magna
TJ. magna
TTaphnia pulex
Stonefly (Pteronarcys
californica)
Fathead minnow
(Juvenile)
Bluegill (juvenile)
Bluegill
Rainbow trout
Brook trout
D. magna
Stonefly (Acroneuria
lycorius)
Fathead minnow
Rainbow trout
Coho salmon
Rainbow trout
(Juvenile)
Rainbow trout
Rainbow trout (Sac fry)
(Fingerling)
(Juvenile)
Brook trout
(Juvenile)
(Yearling)
Test type
LCso
LC50
LC50
LC50
LC50
LCso
LC50
LC50
LC50
LC50
LCso
LC50
TLm
Ef50
EC50
LC50
iLr50
LC50
LC50
LC50
LC50
LC50
TLm
LC50
LC50
ic50
LC50
LC50
LC50
LC5°
LC50
LC5°
LC50
Duration
(h)
96
96
96
96
96
96
96
96
96
96
48
48
48
96
96
96
93
48
96
96
96
96
96
Concentration
(mg/L)
1.7
3.3
2.1
1.3
0.73
6.47
7.55
14.3
18.1
10.1
11.8
10.1
7.4
5.28
1.04
22.04
15.66
41.76
15.37
13.34
14.96
0.005
2.0
0.19
0.31
0.24
0.155-
0.4
0.03
0.024
0.042
0.025
0.084
0.065
Notes
No toxicity data
Flow-through test
No aquatic emissions
No aquatic emissions
Immobilization
Immobilization
Flow-through test
Flow-through test
Flow-through test
4 tests
Flow-through test
Flow-through test
Reference
Gledhill et al., 1980
Gledhill et al . , 1980
Mayer and Sanders, 1973
Mayer and Sanders, 1973
Mayer and Sanders, 1973
Mayer and Sanders, 1973
U.S. EPA, 1980k
U.S. EPA, 1980k
U.S. EPA, 1980k
U.S. EPA, 1980k
U.S. EPA, 1980k
U.S. EPA, 1980k
Hohreiter, 1980
Anderson, 1946
Sanders and Cope, 1966
Sanders and Cope, 1968
Cardwell et al . , 1976
Cardwell et al., 1976
U.S. EPA, 19801
U.S. EPA, 19801
Cardwell et al., 1976
Biesinger and
Christensen, 1972
Warnick and Bell, 1969
U.S. EPA, 1980m
Hohreiter, 1980
U.S. EPA, 1980m
U.S. EPA, 1980m
Hohreiter, 1980
Hohreiter, 1980
Hohreiter, 1980
U.S. EPA, 1980m
McKim et al., 1976
McKim et al . , 1976
en
O
70
to
ro
O
-------
iaoieA-1. icontinuedj.
Representative Test
RAC chemical (s) organism3
33 Nickel D. magna
D. magna
Mayfly (Ephemerella
subvaria]
Stonefly (A. lycorius)
Damselfly
(Unidentified)
Midge
(Chironomus sp.)
Caddisf ly
(Unidentified)
Fathead minnow
Fathead minnow
Bluegill
Bluegill
Rainbow trout
Fish sp., general
Fish sp., general
34 Cadmium D. magna
D". magna
D. magna
Mayfly (Ephemerella
grandis grandis)
Mayfly (E. subvar i a )
Stonefly (Pteronarcella
badia)
Damselfly
(Unidentified)
Midge
(Chironomus) Caddisfly
(Unidentified)
Fathead minnow
Fathead minnow
Bluegill
Bluegill
Rainbow trout
(swim-up and parr)
Test type
LC50
LC50
TLm
TLm
TLm
TLm
TLm
LCsf)
TLm
TLm
TLm
LC50
LC50
LC50
LC50
TLm
TLm
TLm
TLm
TLm
TLm
TLm
TLm
TLm
LC50
Duration
(h)
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
Concentration
(mg/L)
1.81
2.34
4.0
33.5
21.2
8.6
30.2
4.58-
5.18
25.0
5.18-
5.36
39.6
35.5
4.6-9.8
39.2-
42.4
0.0099
0.033
0.049
28.0
2.0
18.0
8.1
1.2
3.4
0.630
72.6
1.94
21.1
0.001-
0.0013
Notes
Hardness:51
(mg/L as CaC03)
Hardness: 100
Hardness:42
Hardness :40
Hardness: 50
Hardness: 50
Hardness: 50
Hardness:20
2 Flow-through
tests
Hardness:210
Flow-tnrough test
Hardness:20
2 tests
Hardness: 360
Flow-through test
Soft water
Hard water
Hardness: 51
Hardness: 104
Hardness:209
Hardness: 54
Hardness: 50
Hardness: 50
Hardness: 50
Hardness: 20
Hardness: 360
Hardness: 20
Hardness: 207
Hardness: 23
2 Flow-through
tests
Reference
U.S. EPA, 1980n
U.S. EPA, 1980n
Warnick and Bell, 1969
Warnick and Bell, 1969
Rehwoldt et al., 1973
Rehwoldt et al . , 1973
Rehwoldt et al., 1973
U.S. EPA, 1980n
Pickering, 1974
Pickering and
Henderson, 1966b
Pickering and
Henderson, 1966b
Hale, 1977
Hohreiter, 1980
Hohreiter, 1980
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 1980o
Clubb et al., 1975
Warnick and Bell, 1969
Clubb et al., 1975
Rehwoldt et al., 1973
Rehwoldt et al., 1973
Rehwoldt et al., 1973
Pickering and
Henderson, 1966b
Pickering and
Henderson, 1966b
Pickering and
Henderson, 1966b
U.S. EPA, 1980o
U.S. EPA, 1980o
ORNL/TM-
10
ro
o
^.
CT
-------
Table A-l. (continued).
Representative Test
RAC chemical (s) organism3
Rainbow trout
Carp
Chinook salmon
(Parr)
Brook trout
Green sunfish
Pumpkin seed
35 Lead 0. magna
0. magna
Fathead minnow
Fathead minnow
Bluegill
Bluegill
Rainbow trout (Fry)
Rainbow trout
Rainbow trout
Rainbow trout
Brook trout
36 Fluorine D. magna
Goldfish
Goldfish
Goldfish
Rainbow trout
Test type
LC5o
LC50
LC50
LC50
LCcn
LC50
LC50
LC^O
TLm
TLm
TLm
LCcg
LC50
LC50
LC50
TLm
Duration
(h)
96
96
96
96
96
96
96
96
96
96
48
96
12-29
60-102
240
Concentration
(mg/L)
0.00175
0.24
0.0035
0.0024
2.84
1.5
0.612
0.952
2.4
482.0
23.8
442.0
0.6
1.17
1.0
8.0
4.1
270.0
120.0
1000.0
1000.0
2.3-7.5
Notes
Hardness:31
Flow-through test
Hardness: 55
Hardness:23
Hardness :44
(sodium sulfate)
Hardness :20
Hardness:55
Hardness:54
Hardness: 110
Hardness:20
Hardness: 360
Hardness :20
Hardness: 360
Hardness:32
Flow-through test
Hardness: 44
"Toxic threshold"
100% kill
100% kill in soft
water
100% kill in hard
water
TLm varies with
temperature
Reference
U.S. EPA, 1980o
U.S. EPA, 19800
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 1980p
U.S. EPA, 1980p
U.S. EPA, 1980p
Pickering and
Henderson, 1966b
Pickering and
Henderson, 1966b
Pickering and
Henderson, 1966b
Hohreiter, 1980
Davies et al., 1976
Hohreiter, 1980
U.S. EPA, 1980p
U.S. EPA, 1980p
Hohreiter, 1980
Hohreiter, 1980
Hohreiter, 1980
Hohreiter, 1980
Angelovic et al . , 1961
aLatin binomials are listed in Appendix C.
PO
O
-------
Table A-2. Chronic toxicity of synfuels chemicals to aquatic animals.
RAC
8
12
14
21
22
26
28
31
32
Representative
chemical (s)
Carbon tetrachloride
Chloroform
Benzene
Naphthalene
Phenol
2,4-Oimethylphenol
Acrolein
Di-2-ethylhexyl
pnthalate
Butyl benzyl
phthalate
Acrylonitrile
Arsenic
Mercuric chloride
Methylmercuric
chloride
Test
organism8
Fathead minnow
Rainbow trout
Rainbow trout
Rainbow trout
Daphnia magna
Fathead minnow
Fathead minnow
Fathead minnow
Fathead minnow
D. magna
D. magna
Fathead minnow
D. magna
TTainbow trout
D. magna
Fathead minnow
D. magna
Fathead minnow
D. magna
D. magna
Bass sp., general
Pink salmon
D. magna
D. magna
Fathead minnow
Brook trout
Duration
Test type (d)
Embryo-larval
Embryo-larval 27
Embryo-larval 27
Embryo 23
Life cycle
Embryo-larval
Embryo-larval
Embryo-larval
Embryo-larval
Life cycle
Life cycle
Life cycle
Life cycle
Embryo-larval
Life cycle
Embryo-larval
Life cycle
LC50 30
Life cycle
TLm 21
10
10
Life cycle
Life cycle
Life cycle
Concentration
(mg/L) Notes
>3.4
1 .2 200 mg/L water
hardness
2.0 50 mg/L water
hardness
10.6 40% teratogenesis
>98.0
0.62
2.56
2.191
2.475
0.024
0.034 Survival reduced
after 64 days
0.021
<0.003
0.008
0.44
0.22
>3.6
2.6
0.912
2.85
7.60 Toxic
5.00 Lethal
0.001 - 4 tests
0.0025
0.001
0.00023 92% dead, 3 months
0.00052
Reference
U.S. EPA, 1980a
U.S. EPA, 1980b
U.S. EPA, 1980b
U.S. EPA, 1980b
U.S. EPA, 1980c
U.S. EPA, 1980e
U.S. EPA, 1980g
U.'S. EPA, 1980h
U.S. EPA, 1980h
U.S. EPA, 1980i
National Research
Council, 1981
U.S. EPA, 1980i
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980k
U.S. EPA, 1980k
U.S. EPA, 19801
Hohreiter, 1980
Hohreiter, 1980
Hohreiter, 1980
U.S. EPA, 1980m
U.S. EPA, 1980m
Hohreiter, 1980
U.S. EPA, 1980m
o
73
•z.
r~
— 1
i
ro
o
00
-------
Table A-2. (continued).
Representative Test
RAC chemical (s) organism3
33 Nickel
34 Cadmium
3b Lead
36 Fluorine
D_. magna
D. magna
Caddisfly
(Clistoronia
magnifica)
Fathead minnow
Fathead minnow
Rainbow trout
D. magna
D. magna
IT. magna
Midge (fanytarsus
dissimilis)
Fathead minnow
Bluegill
Brook trout
Brook trout
0. magna
D. magna
Stonefly (Acroneuria
lycoriasl
Mayfly (Ephemerella
subvarTa)
Caddisfly (Hydropsyche
betteri)
Bluegi 1 1
Rainbow trout
Rainbow trout
Rainbow trout
Rainbow trout
Duration
Test type (d)
Life cycle
Life cycle
Life cycle
Embryo-larval
Life cycle
Embryo- larval
Life cycle
Life cycle
Life cycle
Life cycle
Life cycle
Embryo-larval
Embryo-larval
Life cycle
Life cycle
LC50 14
LC50 7
LC50 7
Embryo- larval
Embryo-larval
Embryo- larval
21
21
Concentration
(mg/L) Notes
0.015
0.123
0.465
0.109
0.527
0.350
0.00015
0.00021
0.00044
0.0031
0.046
0.050
0.0017
0.0092
0.012
0.128
64.0
16.0
32.0
0.092
0.019
0.102
113.0
250.0
Hardness: 51
(mg/L as CaC03)
Hardness: 105
Hardness: 50
Hardness: 44
Hardness: 210
Hardness: 50
Hardness: 53
Hardness: 103
Hardness: 209
Hardness: 201
Hardness: 207
Hardness: 36
Hardness: 187
Hardness: 52
Hardness: 151
Hardness: 41
Hardness: 28
Hardness: 35
100% kill,
45 mg/L CaC03
100% kill,
320 mg/L CaCO?,
yearling trout
Reference
U.S. EPA, 1980n
U.S. EPA, 1980n
U.S. EPA, 1980n
U.S. EPA, 1980n
U.S. EPA, 1980n
U.S. EPA, 1980n
U.S. EPA, 19800
U.S. EPA, 19800
U.S. EPA, 1980o
U.S. EPA, 1980o
U.S. EPA, 19800
U.S. EPA, 19800
U.S. EPA, 19800
U.S. EPA, 1980o
U.S. EPA, 1980p
U.S. EPA, 1980p
Hohreiter, 1980
Hohreiter, 1980
Hohreiter, 1980
U.S. EPA, 1980p
U.S. EPA, 1980p
U.S. EPA, 1980p
Hohreiter, 1980
Hohreiter, 1980
aLatin binomials are listed in Appendix C.
ro
o
-------
Table A-3. Toxicity of synfuels chemicals to algae.
Representative
RAC chemical (s)
12 Benzene
14 Toluene
Naphthalene
15 Fluoranthene
17 Aniline
p-Toluidene
21 Phenol
2,4-Dimethylphenol
Test
organism Test type
Chlorella vulgaris ECi;n
C_. vulgaris EC5Q
Selenastrum
capricornutum ECt;n
C. vulgaris EC50.
Chalamydomonas
angulosa ECgi
S. capricornutum EC^n
S. capricornutum EC^n
Agmenel lum
quadruplicatum
A. quadruplicatum
Coccochloris elabens
Eucapsis sp.
Oscillator! a williamsii
S. capricornutum
S. capricornutum ECt;n
Nitzschia linearis ECi;fi
Chlorella pyrenoidosa ECmn
C. vulgaris EC£Q
T. pyrenoidosa ECmn
Duration Concentration
(h) (mg/L) Notes
48 525.
0
24 245.0
96 433.
48 33.
24 34.
96 54.
96 54.
0.
0.
0.
0.
0.
20.
24 40.
120 258.
48 1500.
80 470.
48 500.
0
0
4
4
6
010
010
010
010
010
0
0
0
0
0
.0
Reduction in cell
numbers
Reduction in cell
numbers
Reduction in cell
numbers and
chlorophyll a
production
Reduction in
extrapolated
cell numbers
61% mortality of
cells
Reduction in eel 1
numbers
Reduction in
chlorophyll a
production
Diffusion from disk
onto algal lawn
inhibited growth
for 3-7 days
Same as above
for all 4 species
Growth inhibition of
12-66% depending on
time (2-3 d) and
temperature (20,
24, 28°C)
Reduction in cell
numbers
Reduction in cell
numbers
Complete destruction
of chlorophyl 1
Growth inhibition
Complete destruction
of chlorophyll
Reference
U.S.
.U.S.
U.S.
U.S.
U
.S.
U.S.
U.
.S.
EPA,
EPA,
EPA,
EPA,
EPA,
EPA,
EPA,
Batterton
1980c
1980d
1980d
1980e
1980e
1980f
1980f
et al.,
— )
1
ro
0
00
o
1978
Batterton
et al . ,
1978
U
U
U
U
U
U
.S.
.S.
.S.
.S.
.S.
.s.
EPA,
EPA,
EPA,
EPA,
EPA,
EPA,
1980g
1980g
1980g
1980g
1980g
1980g
-------
TaDle A-3. (continued).
RAC
26
31
32
33
34
Representative
chemical(s)
Butyl benzyl phthalate
Uimethyl phthalate
uiethyl phthalate
Arsenic
Mercuric chloride
Methylmercuric
chloride
Nickel
Cadmium
Test
organism Test type
S. capricornutum ECi;r)
S. capricornutum ECi;n
Microcystis aeruginosa ECsg
Navicula pelliculosa ECqn
S. capricornutum ECso
S. capricornutum ECc;n
S. capricornutum ECc;n
S. capricornutum EC^n
Cladophora, Spirogyra,
Zygnema sp. EC-|go
Scenedesmus sp.
C. vulgaris ECc,o
Spring diatom EC5Q
assemblages
Coelastrum
microporum ECi;n
Chlamydomonas,
Chlorella,
Haematococcus,
Scenedesmus sp.
Phormidium ambiguum EC-j^
Scenedesmus
Scenedesmus sp.
Scenedesmus sp.
C. pyrenoidosa
C. vulgaris ECso
?. capricornutum
Mixed species
Duration
(h)
96
96
96
96
96
96
96
96
336
96
768
2
336
Concentration
(mg/L)
0.11
0.13
1000.0
0.60
42.7
39.8
90.3
85.6
2.32
20.0
1.03
0.08
2.4-4.8
0.1-0.7
0.5-10.0
1.5
0.0061
0.05-0.5
0.25
0.06
0.05
0.005
Notes
Reduction in
chlorophyll a
Reduction in eel 1
numbers
Reduction in eel 1
numbers
Reduction in eel 1
numbers
Reduction in
chlorophyll a
Reduction in eel 1
numbers
Reduction in
chlorophyll ji
Reduction in cell
numbers
100% kill
Threshold effects
Cell division
inhibition
Reduction in photo-
synthetic activity
Growth inhibition
Growth reduced in
all cultures in
water with 50 mg/L
CaC03
Growth inhibition
Threshold effects
Reduction in eel 1
numbers
Growth inhibition
Growth inhibition
Growth reduction
Growth reduction
Population reduction
Reference
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 1980J
U.S. EPA, 19801
Cushman et al . ,
1977
U.S. EPA, 1980m
U.S. EPA, 1980m
U.S. EPA, 1980m
U.S. EPA, 1980n
Cushman et al . , 1977
Cushman et al., 1977
U.S. EPA, 19800
Cushman et al., 1977
U.S. EPA, 19800
U.S. EPA, 19800
U.S. EPA, 19800
U.S. EPA, 19800
CO
-------
i
<£>
ro
o
Table A-3. (continued).
Representative Test
RAC chemical (s) organism
35 Lead Ankistrodesmus sp.
(.morel la sp.
Scenedesmus sp.
Selenastrum sp.
Anabaena sp.
Chlamydomonas sp.
Cosmarium sp.
Navicula sp.
Scenedesmus sp.
Test type
Ff24
Ef53
Ff35
Ff52
EC50
EC50
EC50
EC50
Duration Concentration
(h) (mg/L)
1
0
0
0
24 15
24 17
24 5
24 17
2
.00
.50
.50
.50
.0-26.0
.0
.0
.0-28.0
.5
Notes
Growth inhibition
Growth inhibition
Growth inhibition
Growth inhibition
Reduction In COj
fixation
Reduction in C02
fixation
Reduction in C02
fixation
Reduction in COj
fixation
Threshold effects
Reference
U.S. EPA,
U.S. EPA,
U.S. EPA,
U.S. EPA,
'U.S. EPA,
U.S. EPA,
U.S. EPA,
U.S. EPA,
Cushman et
1980p
1980p
1980p
1980p
1980p
1980p
1980p
1980p
al., 1977
00
ro
-------
83 ORNL/TM-9120
APPENDIX B
Terrestrial Toxicity Data
-------
Table B-l. Toxicity of chemicals in air to vascular plants.
Representative Test
RAC chemical organism3
1 Carbon monoxide Grapefruit
Red clover
Several species
Popinac
2 Sulfur dioxide0 Barley
Durum wheat
Alfalfa
Tobacco, Bel W3
Cocksfoot
Broadbean
White pine
Norway spruce
3 Nitrogen dioxide Wheat
Bush bean
Spruce
Endive
Carrot
Tobacco, bean,
tomato, radish,
oat, soybean
Cocksfoot and
meadow grass
4 Hydrogen sulfide Green bean
Green bean
Alfalfa
Lettuce
Douglas-fir
Sugar beets
Exposure
Duration
Response (hours)
-C02 uptake
-20% N fixation
-Growth
Defoliation
-44% yield
-42% yield
-26% foliage
-22% foliage
-40% total wt.
Reduced net
photosynthesis
Needle damage
threshold
-25% volume growth
-12% straw yield
-27% yield
-7% linear growth
-37% yield
-30% yield
Visible foliar
injury
-Yield
-20% photosynthesis
-25% whole plants
yield
552
24
72/wk
72/wk
100
100
2070
8
6
1680
334
639
1900
620
357
4
2070
3
64
-39% yield 672-840
-66% yield
-weight and
-38% sugar
+43% sugar
2112
5904
3216
3216
Concentration
(ug/m3) Notes'5
1.8 EOS
1.1 E05
1.1 E07
2.3 E07
3.9 E02
3.9 E02
1.3 E02
1.3 E02
1.78 E02
9.2 E01
6.5 E01
1.3 E02
2 EOS
2 EOS
2-3 EOS
2 £03
4 EOS
3.8 £03
2.1 E02
7.0 E02
2.8 E02
4.2 E02
4.2 E02
4.2 £02
4.2 E02
4.2 E01
detached leaves
field, growing season
field, growing season
5 hr/d, 5 d/wk, 4 wk
5 hr/d, 5 d/wk, 4 wk
103.5 hr/wk, 20 wk
sensitive clone
-17% linear growth in
following year
Reference
National Research
Council, 1977a
National Research
Council, 1977a
National Research
Council, 1977a
National Research
Council, 1977a
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
Zahn, 1975
Zahn, 1975
Zahn, 1975
Zahn, 1975
Zahn, 1975
Heck and Tingey, 1979
103.5 h/wk, 20 wk
4 h/d, 4 d/wk for 4 wk
continuous fumigation
continuous fumigation
continuous fumigation
continuous fumigation
continuous fumigation
Ashenden and
Mansfield, 1978
Taylor, in press
Taylor, in press
Thompson and Kats,
Thompson and Kats,
Thompson and Kats,
Thompson and Kats,
Thompson and Kats,
1978
1978
1978
1978
1978
oo
en
5 Ammonia
Mustard
Injury
2.1 EOS
National Research
Council, 1979b
o
•70
IN3
O
-------
Table B-l . (continued).
Representative
RAC chemical
6 Ethyl ene
7 Formaldehyde
8 Vinyl chloride
12 Benzene
13 Cyclohexene
14 Toluene
17 Aniline
22 Acrolein
23 Carbonyl sulfide
O
70
f—
Exposure 55
Test
organism3
African marigold
Carnation
Cotton
Lily family
Various plants
Alfalfa
Petunia
Cowpea, cotton,
squash
Pinto bean
Runner bean
Pinto bean
Loblolly pine
Alfalfa
Runner bean
Green bean
Duration
Response (hours)
Epinasty
Flowers do not open
Growth inhibition
Growth inhibition
Growth inhibition
Injury
Necrosis and leaf
symptoms
Injury
Red-bordered spots
LDKQ, toxicity
to leaves
Bronze color
Damage
Oxi dent-type damage
LD5Q, toxicity
to leaves
-13% growth
20
72
720
168
240
5
48
168
0.6
1
0.6
3
9
1
64
Concentration
(yg/m3)
1.15 EDO
1.15 E02
6.85 E02
8.60 E02
2.39 EOS
4.9 E02
2.47 E02
2.6 E05
3.0 E04
1.12 E12
1.88 E05
2.7 E02
2.5 E02
2.7 E03
4.9 E02
, ro
Notes0 Reference O
National Air
Pollution Control
Administration, 1970
National Air
Pollution Control
Administration, 1970
National Air
Pollution Control
Administration, 1970
National Air
Pollution Control
Administration, 1970
National Air
Pollution Control
Administration, 1970
00
National Research °^
Council, 1981
Kingsbury et al., 1979
Heck and Pires, 1962
Kingsbury et al . , 1979
Ivens, 1952
Kingsbury et al., 1979
Cheeseman and Perry, 1977
Kingsbury et al., 1979
Ivens, 1952
4 h/d, 4 d/w for 4 wk Taylor, in press
-------
Table B-l. (continued).
Exposure
Representative
RAC chemical
32 Mercury (metal ic)
Mercuric chloride
Oimethylmercury
Test
organism3
Rose
Sugar beet
English ivy
Coleus, Thevetia
and Ricinus
Thevetia and
Ricinus
Coleus, Thevetia
and Ricinus
Response
Severe damage
Damage
Damage
Abscision
Necrosis
Abscision
Duration
(hours)
5
12
168
168
36
Concentration
(ug/m3) Notesb
1.0 E01
2.8 E02
1.5 E04
1.0 EOT
1.0 E01
1.0 E01
Reference
Stahl, 1969
Waldron and Terry,
Waldron and Terry,
Siegel and Siegel ,
Siegel and Siegel,
Siegel and Siegel,
1975
1975
1979
1979
1979
\atin binomials are listed in Appendix C.
DUnless "field" is noted, results are for laboratory studies.
cSee also Table 4.
00
10
ro
o
-------
Table B-2. Toxicity of chemicals in soil or solution to vascular plants.
RAC
9
11
13
14
15
16
19
20
21
22
Representative
chemical
Acetic acid
Methyl pyridine
Hexene
Xylene
Benzo( a)pyrene
3,4-benzopyrene
1 ,2-benzanthracene
1,2,5-6-di-
benzanthrancene
Dimethylalkylamine
Benzothiophene
Indole,
3-ethyl-lH
Indole-3
-acetic acid, 1H
Benzoic acid
2-hydroxy
-benzoic acid
Phenol
4-hydroxy
-benzaldehyde
Test organism*
and
life stage
Barley (seedling)
Alfalfa (sprout)
Oat (seedling)
Sugar beet (seedling)
Corn (sprout)
Tobacco (seedling)
Tobacco (seedling)
Tobacco (seedling)
Gram, rice
Cucumber (sprout)
Oat, cress,
mustard (sprout)
Oat, cress,
mustard (sprout)
Cucumber
Pea (sprout)
Lettuce (seedling)
Rice (seedling)
Lettuce (seedling)
Durum wheat (seed)
Lettuce (seedling)
Test medium
Solution in sand
Solution
Solution
Solution
Solution
Soil
Soil
Soil
Solution
Solution
Solution
Solution
Solution
Solution
Solution on
filter paper
Soil
Solution on
filter paper
Solution
Solution on
filter paper
Response Duration
Root growth inhibition 5d
Root growth inhibition 4d
Mortality
Root growth inhibition 2d
Root growth stimulation 6h
78% growth stimulation 60d
80% growth stimulation 60d
130% growth stimulation 60d
Mortality
9% root growth inhibition 4d
Growth inhibition
Growth inhibition
Mortality lid
Germination reduced by >50% 8h
23% growth inhibition
Seedling growth inhibition 5d
61% growth inhibition
Germination inhibition 4d
26% growth inhibition
Concentration
(ug/g)
600
93.1
25.2
100
0.0005
0.01
0.02
0.02
7.0
10
100
100
35
10
25
1.6
25
2000
100
Reference
Lynch, 1977
Naik et al.,1972
Chen and Elofson, 1978
Allen et al., 1961
Deubert et al., 1979
Graf and Nowak, 1966
Graf and Nowak, 1966
Graf and Nowak, 1966
Dutta et al., 1972
Schlesinger and Mowry, 1951
Oavies et al., 1937
Davies et al., 1937
Hilton and Nomura, 1964
Shukla, 1972
Chou and Patrick, 1976
Gaur and Pareek, 1976
Chou and Patrick, 1976
Badilescu et al., 1967
Chou and Patrick, 1976
^—
r—
—1
ro
O
00
oo
-------
Table B-2. (continued).
Representative
RAC chemical
22 Acrolein
23 Carbon disulfide
24 Ethanol
27 N,N-dimethyl-
formamide
2-methyl
-benzamide
31 Arsenic*
32 Mercury
33 Nickel
Test organism*
and
life stage
Alfalfa
Apple
Lettuce (seed)
Lettuce (seed)
Poppy, chickweed,
carrot, ryegrass
corn, lucerne
(mature)
Corn
(seedling)
Cotton
(mature)
Cotton
(mature)
Soybean
(mature)
Soybean (mature)
Cowpea
Barley
(seed-sprout)
Barley
(seed-sprout)
Lettuce
(seed-sprout)
Corn
(mature)
Sunflower
(mature)
Test medium
Soil
Solution
Solution
Soil
Soil
Soil (fine sandy
loam)
Soil (clay)
Soil (fine sandy
loam)
Soil (clay)
-
Solution
Solution
Solution
Solution
Solution
Concentration
Response Duration (ug/g) Reference
Oxidant-type damage
Root injury
Germination inhibition
Nearly total suppression of
germination
13-87% reduction in yield
10% growth reduction
(wet tissue weight)
Approx. 55% reduction in yield
Approx. 40% reduction in yield
Approx. 45% reduction in yield
Approx. 40% reduction in yield
Retarded growth
12% growth reduction
(fresh weight)
12% growth reduction
(fresh weight)
68% reduction in elongation
of lettuce hypocotyl
10% decrease
in net photosynthesis
10% decrease
in net photosynthesis
9h 0.1
420
44h 1,000,000
24h 1,000,000
3-5w 220,000
4w 64
6w 8b
6w 28b
6w 3b
6w 12b
lb
7d post 5 (as Hg++)
germination
7d post 1 (as PMA)C
germination
5d post 109 (as
germination HgCl2)
7d 5
7d 0.8
Kingsbury et al., 1979
Underfill 1 and Cox, 1940
Meyer and Mayer, 1971
Meyer and Mayer, 1971
Pizey and Wain, 1959
Woolson, et al., 1971
Deuel and Swoboda, 1972
Deuel and Swoboda, 1972
Deuel and Swoboda, 1972
Oeuel and Swoboda, 1972
Albert and Arndt, 1932
Mukhiya et al., 1983
Mukhiya et al., 1983
Nag et al., 1980
Carlson et al., 1975
Carlson et al., 1975
O3
U3
0
70
•z.
J—
5
IO
ro
o
-------
Table B-2. (continued).
Test organism*
Representative and
RAC chemical life stage
33
Oats
(seeds-seedlings)
Oats (mature)
Barley
(seedling)
34 Cadmium Corn
(mature)
Sunflower
(mature)
Soybeans
(mature)
Bean (5 weeks old)
Beet (5 weeks old)
Turnip (5 weeks old)
Corn (5 weeks old)
Lettuce (5 weeks old)
Tomato (5 weeks old)
Barley (5 weeks old)
Pepper (5 weeks old)
Cabbage (5 weeks old)
Soybean
(seedling)
Wheat
(seedling)
Lettuce
(mature)
Test medium
Solution in
coarse sand
Soil
Solution in sand
Solution
Solution
Solution in sand
and vermiculite
Solution
Solution
Solution
Solution
Solution
Solution
Solution
Solution
Solution
Soil (silty clay
loam)
Soil (silty clay
loam)
Soil (silty clay
loam)
Response
Stunted growth
Decreased grain yield
Over 50% reduction in whole
plant fresh weight
10% decrease
in net photosynthesis
10% decrease
in net photosynthesis
35% decrease in fresh weight
of pods
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
50% growth reduction
15% reduction in yield
(dry weight)
20% reduction in yield
(dry weight)
40% reduction in yield
(fresh weight)
Duration
Up to
22d post
germination
Whole life
3w
7d
7d
90d
3w
3w
3w
3w
3w
3w
3w
3w
3w
5w
5w
Whole
life
Concentration
(u9/9)
10
50
281
(NiS04'7H20)
0.9
0.45
2
0.2
0.2
0.2
1.2
0.9
4.8
5.6
2.0
9.0
2.5
2.5
2.5
Reference
Vergnano and Hunter, 1953
Hal stead et al . , 1969
Agarwala et al ., 1977
Carlson et al., 1975
Carlson et al . , 1975
Huang et al . , 1974
Page et al . , 1972
Page et al., 1972
Page et al., 1972
Page et al., 1972
Page et al., 1972
Page et al., 1972
Page et al., 1972
Page et al., 1972
"Page et al., 1972
Haghiri, 1973
Haghiri, 1973
Haghiri, 1973
O
70
1—
10
no
O
O
-------
Table B-2. (continued).
Test organism*
Representative and
RAC chemical life stage
34 Sycamore
(sapling)
3b Lead Soybeans
(mature)
Lettuce
(44d old)
Corn
(25d seedling)
Soybean
(25d seedling)
Sycamore
(sapling)
Test medium Response Duration
Soil (6:1 silty 25% reduction in new stem 90d
clay loam & perlite) growth
Solution in sand 35% decrease in fresh weight 90d
and vermiculite of pods
Soil (silty clay 25% reduction in yield 30d
loam)
Vermiculite and 20% decrease in ll-21d
solution photosynthesis
Vermiculite and 20% decrease in ll-21d
solution photosynthesis
Soil (6:1 silty 25% reduction in new stem 90d
clay loam & perlite) growth
Concentration
(ug/g) Reference
39
62
1000
(Pb(N03)2
1000
2000
500
Carlson and Bazzaz,
Huange et al ., 1974
1977
John and VanLaerhoven
1972
Bazzaz et al., 1974
Bazzaz et al., 1974
Carlson and Bazzaz,
1977
*Latin oinomials are listed in Appendix C.
aArsenic shows a stimulatory effect on plants when
et al., 1971).
BConcentration of water extractable contaminant.
c(PMA-Phenyl mercuric acetate).
present at low concentrations (40-50 pg/g total As or 5 ug/g extractable As in soil) (Woolson
O
73
-------
Table B-3. Toxicity of
Representative
RAC chemical
1 Carbon monoxide
2 Sulfur dioxide
Sulfuric acid
3 Nitrogen dioxide
4 Hydrogen sulfide
chemicals in air
Test
organism8
Rabbit
Dog
Chicken
Rabbit
Human
Guinea pig
Guinea pig
Dog
Chicken
Guinea pig
Guinea pig
Dog
Guinea pig
Rat
Rat
Mouse
Rat and mouse
Canaries, rats
and dogs
to animals.
Response
Aortic lesions
Heart damage
75% egg hatch
90% neonate survival
Lethality
Increased airway
resistance
LT50
Increased airway
resistance
Modified nasal
clearance
Respiratory function
Lethality
Respiratory function
LC50
11% lethality
Bronchial damage
Defects in pulmonary
microbial defense
Pulmonary pathologies
Pulmonary
irritation
Duration
(hours)
4
1008
432
720
1
1.1
5,400
1
8
4,725
1
5,120
24
24
Chronic
Exposure
Concentration
(ug/m3) Nates
1.51 £05
4.3 E04
4.9 E05 egg exposed
1.0 E05 mother exposed
9.2 EOS
4.2 E02
5.8 E06
1.3 E04
3.7 E03 Intermittent
exposure, 7 d
1.0 E02
1.8 E04
8.9 E02
1.5 £05
2.3 £04
2.8 E04
3.8 E03
9.4 E02 Also decreased
resistance to
infection
Subacute 7.0 E04 No established chronic
effects
Reference
National Research
Council, 1977a
National Research
Council, 1977a
National Research
Council, 1977a
National Research
Council, 1977a
Cleland and Kingsbury,
1977
U.S. EPA, 1982
U.S. EPA, 1982
U.S. EPA, 1982
Wakabayashi et a!.,
1977
Wakabayashi et al .,
1977
Wakabayashi et al. ,
1977
Wakabayashi et al. ,
1977
National Research
Council, 1977b
National Research
Council, 1977b
National Research
Council, 1977b
National Research
Council, 1977b
National Research
Council, 1977b
National Research
Council, 1979a
o
•yo
r—
—I
«3
PO
o
10
ro
-------
Table B-3. (continued).
Representative Test
RAC chemical organism3
5 Ammonia Chicken
Pig
Rabbit
Mouse
Human
6 Acetylene Human
7 Formaldehyde Rat
Guinea pigs
Rat
8 Chloroform Mouse
Human
9 Acetic acid Mouse
Human
Human
10 Furan Rat
Thiophene Mouse
11 Pyridine Rat
2-Ethylpyridine Rat
Exposure
Duration Concentration
Response (hours) (ug/m^) Notes Reference
Increased disease
susceptibility
Respiratory irritation
LJj-Q
Lethal threshold
Throat irritation
Unconsciousness
LC50
Increased airway
resistance
Respiratory and eye
irritation and
liver weight loss
LC50
Enlarged liver
LC50
Irritation
Respiratory, stomach
and skin irritation
Lethal threshold
Lethal threshold
LC50
LC100
72
840
33
16
Immediate
0.08
4
1
1400
Chronic
1
0.05
Chronic
8-48
8-48
4
3
1.3 E04
4.3 E04
7.0 E06
7.0 £05
2.8 E05
3.7 £08
5.7 £05
3.6 £02
1.0 £03
1.4 £05
4.9 £04
1.4 E07
2.0 £06
1.5 £05
2.4 £08
3.0 E07
1.3 £07
2.4 £07
Newcastle virus National Research
Council, 1979b
National Research
Council, 1979b
National Researcn
Council, 1979b
National Research
Council, 1979b
National Research
Council, 1979b
National Research
Council, 1976
National Researcn
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
Kingsbury et al., 1979
In workplace air Kingsbury et al., 1979
Kingsbury et al . , 1979
Kingsbury et al., 1979
7-12 years, workplace National Research
exposure Council, 1976
Kingsbury et al., 1979
Kingsbury et al., 1979
Kingsbury et al . , 1979
Kingsbury et al., 1979
to
CO
0
^
r~
•*^
12 Benzene
Human
Lethal threshold
Chronic
1.9 £05 Workplace exposure
National Research
Council, 1976
IN3
o
-------
Table B-3. (continued).
RAC
13
14
15
16
17
18
19
20
Representative
chemical
Pentane
Cyclopentane
Hexane
Cyclohexane
Heptane
Butadiene
Cyclopentadine
Toluene
Ethyl benzene
p-Xylene
Tetrahydro-
napnthalene
Naphthalene
Test
organism3
Mouse
Mouse
Mouse
Human
Rabbit
Rabbit
Human
Human
Rat
Rat
Human
Rat
Human
Mouse
Guinea pig
Human
(No data on respiratory toxicity but
Ethyl ami ne
1 -Aminopropane
Aniline
Dimethyl anal ine
Rat
"Animals"
Rat
Rat
Mouse
Exposure
Duration Concentration
Response (hours) (ug/m3) Notes
Lethality
Lethality
Lethality
Dizziness
Lethality
Narcosis and convulsions
Dizziness
Respirtory and eye
irritation
Liver and kidney
damage
Lethal threshold
Psychological effects
Lethal threshold
Eye irritation
Lethal threshold
Lethal threshold
Eye irritation and
damage
several members of this RAC
Lethal threshold
Lung, liver and
kidney damage
50
50
LC50
..
—
0.
1
1
0.
8
245
4
--
4
<0.
4
136
--
are
4
1008
4
4
7
3
1
1
17 1
9
4
10 4
1
1
1
3
1
08 8
1
1
7
.8
.1
.2
.8
.2
.5
.1
.8
.4
.5
.8
.7
.8
.5
.5
.9
EOS
EOS
EOS
E07
E07
E07
E06
E07
E06 expsoure = 7 hr/day
for 35 days
E07
E05
E07
E05
E07
E06 8 hours for 17 days
E04
carcinogens)
5
1
5
9
7
.5
.8
.6
.5
.4
E06
E05
E06
E05
E05 Mixed isomers
Reference
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
Kingsbury
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
et
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
al.,
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
(No data on respiratory toxicity)
(No data on respiratory toxicity)
(No data on respiratory toxicity)
1
to
ro
O
21 (No data on respiratory toxicity)
-------
Table B-3. (continued).
Representative
RAC chemical
22 Acrolein
Acetal dehyde
Proprional dehyde
Butyral dehyde
Butanone
23 Methyl mercaptan
Ethyl mercaptan
n -Butyl mercaptan
Thiophenol
Carbon disulfide
24 Methanol
Exposure
Test
organism3
Rat
Monkey
Mice, rabbits and
guinea pigs
Rat
Rat
Rat
Mouse
Rat
Rat
Human
Rat
Human
Rat
Human
Monkey
Human
Response
LC50
Respiratory system
damage
LC50
LCcn
Reduced weight gain
50
50
Lethal threshold
l-Cso
Central nervous
system effects
'-''50
"Toxic effect"
Central nervous
system effects
LC5Q
Central nervous
Duration
(hours)
4
2,160
4
0.5
36
0.5
0.75
—
—
—
4
3
4
Concentration
(ug/m ) Notes
1.8 E04
5.1 E02
2.0 E06
6.2 E07
3.1 E06 6 h/d x 6 d
1.7 EOS
6.1 EOS
2.0 E07
1.1 E07
1.0 E04
1.5 E07
1.0 E04
1.5 E05
5.0 E04 7 years exposure
1.3 E06
7.5 E04
Reference
National Research
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
National Research
Council, 1981
Kingsbury et al . 1979
Kingsbury et al. 1979
Kingsbury et al . 1979
Kingsbury et al . 1979
Kingsbury et al . 1979
Kingsbury et al . 1979
Cleland and Kingsbury,
1977
Kingsbury et al . , 1979
Kingsbury et al., 1979
25
26
27
28
Ethanol
Human
(No data on respiratory toxicity)
Methyl acetate Human
Methyl methacrylate Rat
Butyl acetate Human
Human
n-Amyl acetate Human
(No data on respiratory toxicity)
Acetonitrile
Acrylonitrile
Rat
Human
Rat
system effects
Eye and respiratory 1.9 E06
irritation and
mental effects
Severe toxic effects 1 1.5 E06
LC50 1 1.5 E07
Throat irritation 9.6 E05
Toxic effects 1 9.6 E06
Toxic threshold 0.5 1.0 E06
Lethal threshold 4 1.3 E07
Bronchial effects 2.7 E05
Lethal threshold 4 1.1 E06
Kingsbury et al., 1979
Kingsbury et al., 1979
Kingsbury et al.
Kingsbury et al.
Kingsbury et al.
Kingsbury et al.
Kingsbury et al.
1979
1979
1979
1979
1979
01
Kingsbury et al., 1979
Kingsbury et al., 1979
Kingsbury et al., 1979
Kingsbury et al., 1979
O
73
I
UD
ro
O
-------
Table B-3. (continued).
RAC
29
30
31
32
Representative
chemical
Test
organism3
Exposure
Duration Concentration
Response (hours) (yg/m^) Notes
(No data on respiratory toxicity)
Fly ash
Arsenic trioxide
Mercury (metal)
Monkey
Rat
Human
Rabbit
Human
Slight lung fibrosis 13,390 4.6 E02
Weight lag and 24 2.5 E01
physiological effects
Toxic threshold 1.0 E03
Toxic threshold 2.9 E04
Central nervous 1.7 E02 40 yr. exposure
system effects
ro
O
Reference
Kingsbury et al., 1979
National Research
Council, 1979c
National Research
Council, 1977c
Cassidy and Furr, 1978
Cassidy and Furr, 1978 10
Kingsbury et al . , 1979 ^
33 Nickel carbonyl
Rat
34 Cadmium oxide fumes Human
Cadmium oxide dust Human
Cadmium Human
35 Lead
Human
0.5 2.4 E05
Lethality
Impaired lung function
Pulmonary and renal
effects
Threshold of overt
poisoning
5.0 E03
3.15 £03 20 yr. exposure
1.0-27 E01 Occupational exposure
5.0 E02 Occupational exposure
National Research
Council, 1975
Hammons et al., 1978
Hammons et al., 1978
Kingsbury et al., 1979
National Research
Council, 1972
Latin binomials are lised in Appendix C.
-------
97 ORNL/TM-9120
APPENDIX C
Common and Scientific Names of Animals and Plants
-------
99
ORNL/TM-9120
Animals
Common name
Bigmouth buffalo
Black crappie
Bluegill
Brook trout
Brown trout
Canary
Carp
Channel catfish
Chicken
Chinook salmon
Coho salmon
Dog
Fathead minnow
Goldfish
Green sunfish
Guinea pig
Human
Largemouth bass
Monkey
Mosquitofish
Mouse
Northern pike
Pig
Pink salmon
Pumpkinseed
Rabbit
Rainbow trout
Rat
Smallmouth buffalo
White bass
Scientific name
Ictiobus cyprinellus
Pomoxis nigromaculatus
Lepomis macrochirus
Salvelinus fontinalis
Salmo tfutta
Serinus canarius
Cyprinus carpio
Ictalurus punctatus
Gallus gall us
Oncorhynchus tshawytacha
Oncorhynchus kisutch
Canis familiaris
Pimephales promelas
Carassius auratus
Lepomis cyanellus
Cavia cobaya
Homo sapiens
Micropterus salmoides
Macaca sp.
Gambusia affinis
Mus musculus
Esox lucius
Sus scrofa
Oncorhynchus gorbuscha
Lepomis gibbosus
Oryctolagus cuniculus
Salmo gairdneri
Rattus rattus
Ictiobus bulbalus
Morone chrysops
-------
ORNL/TM-9120
TOO
Plants
Common name
African marigold
Alfalfa
Apple
Barley
Bean
Broadbean
Bush bean
Cabbage
Carnation
Carrot
Chickweed, common
Cocksfoot
Coleus
Corn
Cotton
Cowpea
Cress
Cucumber
Durum wheat
Endive
English ivy
Gram
Grapefruit
Green bean
Lettuce
Loblolly pine
Lucerne
Meadowgrass
Mustard
Norway spruce
Oat
Oat, wild
Pea
Pepper
Petunia
Pinto bean
Popinac
Poppy
Radish
Red clover
Rice
Ricinus
Rose
Runner bean
Ryegrass, Italina
Soybean
Spruce
Squash
Scientific name
Tagetes sp.
Medicago sativa
Malus sylvestris
Hordeum vulgare
Phaseolus vulgaris
Vicia faba
Phaseolus vulgaris
Brassica oleracea
Dianthus caryophyllos
Daucus carota
Stellaria media
Dactyl is glomerata
Coleus blumei
Zea mays
Gossypium hirsutum
Vigna sinensis
Lepidium sativum
Cucumis sativus
Triticum durum
Cicorium endivia
Hedera helix
Cicer arietinum
Citrus paradisi
Phaseolus vulgaris
Lactuca sativa
Pinus taeda
Medicago sativa
Poa pratensis
Brassica alba
Picea abies
Avena sativa
Psoralea corylifolia
Capsicum frutescens
Petunia sp.
Phaseolus vulgaris
Acacia farnesiana
Papaver sp.
Raphanus sativus
Trifolium pratense
Oryza sativa
Ricinus communis
Rosa sp.
Phaseolus vulqaris
Lolium mmtiflorurn
Glycine max
Picea abTes
Cucurbita sp.
-------
101 ORNL/TM-9120
Plants (continued)
Common name Scientific name
Sugar beet Beta vulgaris
Sunflower Helianthus annuus
Sycamore Platanus occidental is
Thevetia Thevetia neriifolca
Tobacco Nicotiana tabacum
Tomato Lycopersicon esculentum
Turnip Brassica napus
Wheat Triticum durum
White pine Pinus strobus
-------
103 ORNL/TM-9120
APPENDIX D
Species-Specific Results of the Analysis of Extrapolation Error
-------
Table 0-1. Predicted geometric mean maximum allowable toxicant concentrations (PGMATCs) for each RAU and each species of fish.
PGMATC3 (mg/L)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
32A
33
34
35
RAU
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases 1,565
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile N heterocyclics
Benzene
Aliphatic/alicyclic hydrocarbons
Mono- or diaromatic hydrocarbons
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines
Alkaline N heterocyclics
Neutral N, 0, S heterocyclics
Carboxylic acids 48
Phenols
Aldehydes and ketones
Nonheterocylic organo S
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury (inorganic)
Mercury (methyl)
Nickel
Cadmium
Lead
Carp
8.8
43.5
,162
b
533
941
b
b
421
218
120
190
b
b
562
b
,548
462
12.7
b
b
b
33.0
B
215
t
b
238
34.2
11.7
94
11.1
54
Buffalo
8.8
43.5
1,565,162
b
1245
933
b
b
252
255
146
190
b
b
590
b
48,548
387
12.7
b
b
b
287.4
b
389
b
b
479
34.2
11.7
876
1.5
171
Channel
catfish
11.6
32.9
11,313
b
600
518
b
b
144
166
91
134
b
t
590
b
1435
207
11.7
b
b
b
160.9
b
237
b
b
247
26.9
10.9
410
2.0
104
White
bass
3.3
18.0
29,185
b
135
213
b
b
116
66
65
79
b
b
347
t
2001
182
4.9
b
t
ti
133.0
b
65
b
b
229
14.0
4.5
433
0.5
77
Green
sunf ish
6.7
18.0
29,185
b
705
213
b
b
116
66
65
121
b
b
141
b
2001
308
10.7
b
b
b
40.5
b
236
b
b
409
14.0
4.5
147
76.7
393
Bluegill
sunf ish
3.1
18.0
29,185
b
814
213
b
b
116
66
65
98
b
b
141
b
2001
302
5.4
b
b
b
26.6
b
220
b
b
424
14.0
4.5
124
57.0
404
Largemouth
bass
2.5
18.0
29,185
b
744
213
b
b
116
66
65
86
b
b
141
b
2001
271
8.1
b
b
b
22.8
b
196
b
b
383
14.0
4.5
110
51.3
364
Black
crappie
1.6
18.0
29,185
b
110
213
b
b
116
66
65
22
b
b
141
b
2001
52
2.4
b
b
b
8.1
b
41
b
b
67
14.0
4.5
26
14.8
65
Rainbow
trout
2.6
15.3
19,705
0
566
252
b
b
125
68
65
74
b
b
159
b
1317
208
4.0
b
b
b
145.9
D
160
b
b
257
11.9
2.3
552
0.2
61
Brook
trout
2.6
14.9
19,705
b
566
252
b
b
86
68
50
74
b
b
159
b
1317
131
4.4
b
b
b
97.6
b
160
b
D
281
12.0
4.4
296
0.3
102
o
en
o
•yo
ro
o
-------
ORNL/TM-9120
106
Table D-2. Probabilities of chronic toxic effects on fish populations
due to RAC 4 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
aBluegill - Perciformes
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
1.0017
1.0017
1.0017
0.7580
2.6564
1.3084
2.8465
3.4440
5.6337
0.5003
0.5003
0.5003
0.4529
0.6942
0.5578
0.7244
0.7399
0.7859
Class
Class
Class
Class
a
Genus
Species
Family
Family
-------
107
ORNL/TM-9120
Table D-3. Probabilities of chronic toxic effects on fish populations
due to RAC 5 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
2.7565
2.7565
2.7565
3.6404
6.6493
6.6493
6.6493
6.6493
6.6493
0.6832
0.6832
0.6832
0.7149
0.8330
0.8330
0.8330
0.8330
0.8330
Class
Class
Class
Class
Class
Class
Class
Class
Class
-------
ORNL/TM-9120
108
Table D-4. Probabilities of chronic toxic effects on fish populations
due to RAC 9 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
aFathead minnow - Cypriniformes
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0756
0.0763
0.0763
0.1372
0.3336
0.3336
0.3336
0.3336
0.3336
0.0950
0.0943
0.0943
0.1730
0.3098
0.3098
0.3098
0.3098
0.3098
Family
a
a
Class
Class
Class
Class
Class
Class
-------
109
ORNL/TM-9120
Table D-5. Probabilities of chronic toxic effects on fish populations
due to RAC 31 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0326
0.0162
0.0162
0.0315
0.0340
0.0190
0.0184
0.0203
0.1161
0.0369
0.0282
0.0282
0.0662
0.0441
0.0181
0.0123
0.0227
0.1721
Family
Class
Class
Class
Class
Genus
Species
Family
Family
-------
ORNL/TM-9120
110
Table D-6. Probabilities of chronic toxic effects on fish populations
due to RAC 32A at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0072
0.0072
0.0072
0.0077
0.0187
0.0187
0.0187
0.0187
0.0187
0.0101
0.0101
0.0101
0.0162
0.0216
0.0216
0.0216
0.0216
0.0216
Class
Class
Class
Class
Class
Class
Class
Class
Class
-------
Ill
ORNL/TM-9120
Table D-7. Probabilities of chronic toxic effects on fish populations
due to RAC 33 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0099
0.0011
0.0011
0.0023
0.0022
0.0063
0.0075
0.0085
0.0355
0.0073
0.0008
0.0008
0.0042
0.0010
0.0033
0.0027
0.0066
0.0670
Family
Class
Class
Class
Class
Genus
Species
Family
Family
-------
ORNL/TM-9120
112
Table D-8. Probabilities of chronic toxic effects on fish populations
due to RAC 34 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0076
0.0551
0.0551
0.0427
0.1617
0.0011
0.0015
0.0017
0.0057
0.0026
0.0884
0.0884
0.0845
0.1816
0.0001
0.0001
0.0004
0.0097
Species
Class
Class
Class
Class
Genus
Species
Family
Family
-------
113
ORNL/TM-9120
Table D-9. Probabilities of chronic toxic effects on fish populations
due to RAC 35 at annual median ambient concentrations for
the Lurgi/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0183
0.0057
0.0057
0.0094
0.0123
0.0025
0.0024
0.0027
0.0152
0.0168
0.0080
0.0080
0.0207
0.0134
0.0008
0.0004
0.0012
0.0329
Family
Class
Class
Class
Class
Genus
Species
Family
Family
-------
ORNL/TM-9120
114
Table D-10. Probabilities of chronic toxic effects on fish populations
due to RAC 4 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
aBluegi11-Perciformes
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.8924
0.8924
0.8924
0.6753
2.3665
1.1657
2.5357
3.0682
5.0189
0.4796
0.4796
0.4796
0.4334
0.6728
0.5330
0.7020
0.7201
0.7701
Class
Class
Class
Class
a
Genus
Species
Family
Family
-------
115
ORNL/TM-9120
Table D-ll.
Probabilities of chronic toxic effects on fish populations
due to RAC 5 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.8527
0.8527
0.8527
1.1262
2.0569
2.0569
2.0569
2.0569
2.0569
0.4701
0.4701
0.4701
0.5208
0.6435
0.6435
0.6435
0.6435
0.6435
Class
Class
Class
Class
Class
Class
Class
Class
Class
-------
ORNL/TM-9120
116
Table D-12. Probabilities of chronic toxic effects on fish populations
due to RAC 9 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
aFathead minnow - Cypriniformes
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.6911
0.6972
0.6972
1.2547
3.0498
3.0498
3.0498
3.0498
3.0498
0.4256
0.4269
0.4269
0.5429
0.6930
0.6930
0.6930
0.6930
0.6930
Family
a
a
Class
Class
Class
Class
Class
Class
-------
117
ORNL/TM-9120
Table D-13.
Probabilities of chronic toxic effects on fish populations
due to RAC 31 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to P6MATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0113
0.0056
0.0056
0.0109
0.0118
0.0066
0.0064
0.0071
0.0403
0.0096
0.0082
0.0082
0.0247
0.0126
0.0040
0.0022
0.0055
0.0792
Family
Class
Class
Class
Class
Genus
Species
Family
Family
-------
ORNL/TM-9120
118
Table D-14. Probabilities of chronic toxic effects on fish populations
due to RAC 33 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0048
0.0005
0.0005
0.0011
0.0010
0.0031
0.0036
0.0041
0.0173
0.0024
0.0003
0.0003
0.0016
0.0003
0.0010
0.0007
0.0022
0.0343
Family
Class
Class
Class
Class
Genus
Species
Family
Family
-------
119
ORNL/TM-9120
Table D-15. Probabilities of chronic toxic effects on fish populations
due to RAC 34 at annual median ambient concentrations for
the Koppers-Totzek/Fischer-Tropsch process
Species
Ratio of ambient
concentration to
to PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
0.0047
0.0339
0.0339
0.0262
0.0993
0.0007
0.0009
0.0010
0.0035
0.0011
0.0573
0.0573
0.0562
0.1246
0.0000
0.0000
0.0002
0.0052
Species
Class
Class
Class
Class
Genus
Species
Family
Family
-------
121 ORNL/TM-9120
APPENDIX E
Detailed Methods and Assumptions for
Ecosystem Uncertainty Analysis
-------
123 ORNL/TM-9120
APPENDIX E
DETAILED METHODS AND ASSUMPTIONS FOR
ECOSYSTEM UNCERTAINTY ANALYSIS
E.I ORGANIZING TOXICITY DATA
The first step in Ecosystem Uncertainty Analysis (EUA) is selection
of appropriate toxicity data and association of the data with components
of SWACOM.
Toxicity data on phytoplankton are sparse. It is possible to find
values for green algae, such as Selenastrum capricornutum. and these
data are used for all 10 algal populations if no other information is
available. If data are available on diatoms and blue-greens, then a
further division is possible based on physiological parameters in the
model and past experience with SWACOM. Like diatoms, species 1-3 appear
early in the spring and are associated with low temperatures and high
nutrient concentrations. Species 4 to 7 dominate the spring bloom and
are associated with intermediate temperatures and light. Species 8 to
10 appear in the summer and are tolerant of high temperatures and low
nutrient concentrations.
The identification of the zooplankton is more tenuous. Based on
model behavior and physiological parameters, species 12 and 13 are
identified with Cladocerans. The ubiquitous data for Daphnia magna are
used for species 12. When data are available for Daphnia pulex, they
are used for species 13. The remaining zooplankters (species 11, 14
and 15, and species 13 when no data was available for £. pulex) are
simply identified as crustaceans. Of the available data, the smallest
concentration is assigned to 15 and the largest to 11. Species 14 (and
13 when necessary) is assigned an intermediate value between these
extremes. Assuming species 15 to be the most sensitive is conservative.
Since blue-green algae increase is one of our endpoints, we assign the
greatest sensitivity to the consumer (i.e., 15) which is most abundant
during the summer of the simulated year.
-------
ORNL/TM-9120 124
LC50 data for fathead minnow (Pimephales sp.), bluegill (Lepomis
macrochirus), and guppy (Poecilia reticulata) are assigned to forage
fish (species 16, 17 and 18). When data on these species are not
available, others are substituted, such as goldfish or mosquitofish.
The game fish (species 19) was identified as rainbow trout.
E.2 TRANSFORMING TOXICITY DATA
A critical step in applying EUA involves changing parameter values
in SWACOM. This requires three important assumptions which are
outlined below.
E.2.1 The General Stress Syndrome (GSS)
Toxicity tests provide information on mortality (or similar
endpoint) but provide little insight on the mode of action of the
chemicals. Thus, some assumption must be made about how the toxicant
affects physiological processes in SWACOM. In an application that
focuses on a single chemical it may be possible to obtain detailed
information on modes of action. However, the present effort must cover
a number of Risk Assessment Units, and it was necessary to make a
single overall assumption.
We assumed that organisms respond to all toxicants according to a
General Stress Syndrome (GSS). For phytoplankton, this involved
decreased maximum photosynthetic rate, increased Michaelis-Menten
constant, increased susceptibility to grazing, decreased light
saturation, and decreased nutrient assimilation. For zooplankton and
fish, the syndrome involves increased respiration, decreased grazing
rates, increased susceptibility to predation, and decreased nutrient
assimilation. For all organisms, the optimum temperature was assumed
to be unchanged. The GSS represents how organisms respond to most
toxicants. Where observations were recorded for the chemicals used in
this assessment, the researchers noted hyperactivity, increased
operculation and other symptoms consistent with the assumption of the
GSS. However, some organics might have a "narcotic" effect which would
be opposite to the reaction assumed here.
-------
125 ORNL/TM-9120
The General Stress Syndrome defines the direction of change of
each parameter in SWACOM. It is also necessary to make an assumption
about the relative change in each parameter. We have assumed that all
parameters of SWACOM change by the same percentage. This assumption
can be removed only if considerable information is available on modes
of action of each chemical.
E.2.2 The MICROCOSM Simulations
The key to arriving at new parameters is simulation of the
experiments which generated the toxicity data. This involves simulating
each species in isolation with light, temperature, food supply, and
nutrients set at constant levels that would maintain the population
indefinitely. Then the parameters are altered together in the direction
indicated by the 6SS until we duplicate the original experiment. Thus,
for an LC^0 (96 hours), we find the percentage change which halves
the population in 4 d.
At the conclusion of the MICROCOSM simulations, we have the
percentage change in the parameters which matches the experiment.
We must now make an additional assumption to arrive at the expected
response for concentrations below the LC™ or EC We assume a
linear dose response. Thus, an environmental concentration 1/5 of the
LC5Q would cause a 10% reduction in the population. The MICROCOSM
simulations are then repeated with this new endpoint to arrive at a new
percentage change in the parameters. Since most response curves are
concave, our assumption should be conservative.
E.2.3 Choosing Uncertainties
To implement the analysis, it is necessary to associate
uncertainties with the parameter changes. We assume that all parameter
changes have an associated uncertainty of plus or minus 100%. This
assumption seems sufficiently conservative. One might wish to adopt a
more complex strategy which would combine information on modes of
action with a Delphi survey of experienced researchers to arrive at
more specific estimates of uncertainty.
-------
127
ORNL/TM-9120
INTERNAL DISTRIBUTION
1. S. I. Auerbach 34. C. W. Miller
2-6. C. F. Baes III 35-39. R. V. O'Neill
7-13. L. W. Barnthouse 40. D. E. Reichle
14-18. S. M. Bartell 41-45. A. E. Rosen
19. C. C. Coutant 46. L. L. Sigal
20. K. E. Cowser 47-51. W. Suter II
21. W. F. Furth 52. C. C. Travis
22-26. R. H. Gardner 53. P. J. Walsh
27. A. K. Genung 54. H. E. Zittel
28. J. M. Biddings 55-56. Central Research Library
29. M. R. Guerin 57-71. ESD Library
30. S. G. Hildebrand 72-73. Laboratory Records Dept.
31. S. V. Kaye 74. Laboratory Records, ORNL-RC
32. L. E. McNeese 75. ORNL Y-12 Technical Library
33. R. E. Millemann 76. ORNL Patent Office
EXTERNAL DISTRIBUTION
77. J. Frances Allen, Science Advisory Board, Environmental
Protection Agency, Washington, DC 20460
78. Richard Balcomb, TS-769, Office of Pesticide Programs,
Environmental Protection Agency, 401 M Street, SW, Washington,
DC 20460
79. Nathaniel F. Barr, Office of Health and Environmental Research,
Department of Energy, Washington, DC 20545
80. Colonel Johan Bayer, USAF OHEL, Brook AFB, TX 78235
81. Frank Benenati, Office of Toxic Substances, Environmental
Protection Agency, 401 M Street, SW, Washington, DC 20460
82. K. Biesinger, Environmental Protection Agency, National Water
Quality Laboratory, 6201 Congdon Boulevard, Duluth, MN 55804
83. J. D. Buffington, Director, Office of Biological Services,
U.S. Fish and Wildlife Services, 1730 K Street, NW, Washington,
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84. J. Cairns, Center for Environmental Studies, Virginia
Polytechnic Institute and State University, Blacksburg,
VA 24061
85. Melvin W. Carter, Georgia Institute of Technology, School of
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91. Paul Cho, Health and Environmental Risk Analysis Program,
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Laboratories, Richland, WA 99352
93. R. C. Dahlman, Carbon Cycle Program Manager, Carbon Dioxide
Research Division, Office of Energy Research, Room J-311,
ER-12, Department of Energy, Washington, DC 20545
-------
ORNL/TM-9120 128
94. Sidney Draggan, Ecologist-Policy Analyst, Division of Policy
Research and Analysis, National Science Foundation,
Washington, DC 20550
95. Charles W. Edington, Office of Health and Environmental
Research, Department of Energy, Washington, DC 20545
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Office of Solid Waste, Environmental Protection Agency,
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101. Norman R. Glass, National Ecological Research Laboratory,
Environmental Protection Agency, 200 SW 35th Street,
Con/all is, OR 97330
102. D. Heyward Hamilton, Office of Health and Environmental
Research, Department of Energy, Washington, DC 20545
103. Leonard Hamilton, Department of Energy and Environment,
Brookhaven National Laboratory, Upton, NY 11973
104. Norbert Jaworski, Environmental Research Laboratory-Duluth,
6201 Congdon Boulevard, Duluth, NM 55804
105. The Institute of Ecology, 1401 Wilson Blvd., Box 9197,
Arlington, VA 22209
106. Donald Johnson, Gas Research Institute, 8600 West Bryn Mawr
Avenue, Chicago, IL 60631
107. Library, Bureau of Sport Fisheries and Wildlife, Department
of the Interior, Washington, DC 20240
108. Library, Food and Agriculture, Organization of the United
Nations, Fishery Resources and Environment Division, via
delle Termi di Caracal la 001000, Rome, Italy
109. Library, Western Fish Toxicology Laboratory, Environmental
Protection Agency, Corvallis, OR 97330
110. Ronald R. Loose, Department of Energy, Washington, DC 20545
111. Helen McCammon, Director, Ecological Research Division,
Office of Health and Environmental Research, Office of Energy
Research, MS-E201, ER-75, Room E-233, Department of Energy,
Washington, DC 20545
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401 M Street, SW, Washington, DC 20460
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of Energy, Washington, DC 20545
163. Harold A. Mooney, Department of Biological Sciences, Stanford
University, Stanford, CA 94305
164. Sam Morris, Brookhaven National Laboratory, Associated
Universities, Inc., Upton, NY 11973
165. J. Vincent Nabholz, Health and Environmental Review Division,
Office of Toxic Substances, Environmental Protection Agency,
401 M Street, SW, Washington, DC 20460
-------
129 ORNL/TM-9120
166. Barry E. North, Engineering-Science, 10 Lakeside Lane,
Denver, CO 80212
167. Goetz Oertel, Waste Management Division, Department of
Energy, Washington, DC 20545
168. William S. Osburn, Jr., Ecological Research Division, Office
of Health and Environmental Research, Office of Energy
Research, MS-E201, EV-33, Room F-216, Department of Energy,
Washington, DC 20545
169. F. L. Parker, College of Engineering, Vanderbilt University,
Nashville, TN 37235
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Pennsylvania State Universtiy, University Park, PA 16802
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Oak Ridge, TN 37830
173. J. C. Randolph, School of Public and Environmental Affairs,
Indiana University, Bloomington, IN 47405
174. Irwin Remson, Department of Applied Earth Sciences, Stanford
University, Stanford, CA 94305
175. Abe Silvers, Electric Power Research Institute, P.O.
Box 10412, Palo Alto, CA 94303
176. David Slade, Office of Health and Environmental Research,
Department of Energy, Washington, DC 10545
177. R. J. Stern, Director, Division of NEPA Affairs, Department
of Energy, 4G064 Forrestal Building, Washington, DC 20545
178. Frank Swanberg, Jr., U.S. Nuclear Regulatory Commission,
Washington, DC 20555
179. Burt Vaughan, Battelle-Pacific Northwest Laboratory,
Richland, WA 99352
180. D. S. Vaughan, National Fisheries Service, Beaufort
Laboratories, Beaufort, NC 28516
181. John Walker, Assessment Division, TS 778, Office of Toxic
Substances, U.S. Environmental Protection Agency, 401 M
Street, SW, Washington, DC 20460
182. Robert L. Watters, Ecological Research Division, Office of
Health and Environmental Research, Office of Energy Research,
MS-E201, ER-75, Room F-226, Department of Energy, Washington,
DC 20545
183. D. E. Weber, Office of Energy, Minerals, and Industry,
Environmental Protection Agency, Washington, DC 20460
184. A. M. Weinberg, Institute of Energy Analysis, Oak Ridge
Associated Universities, Oak Ridge, TN 37830
185. Ted Williams, Division of Policy Analysis, Department of
Energy, Washington, DC 20545
186. Frank J. Wobber, Division of Ecological Research, Office of
Health and Environmental Research, Office of Energy Research,
MS-E201, Department of Energy, Washington, DC 20545
187. Bill Wood, TS-798, U.S. Environmental Protection Agency,
401 M Street, SW, Washington, DC 20460
188. Robert W. Wood, Director, Division of Pollutant
Characterization and Safety Research, Department of Energy,
Washington, DC 20545
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ORNL/TM-9120 130
189. R. Wyzga, Manager, Health and Environmental Risk Department,
Electric Power Research Institute, P.O. Box 10412, Palo Alto,
CA 94303
190. Office of Assistant Manager for Energy Research and
Development, Oak Ridge Operations, P. 0. Box E, Department of
Energy, Oak Ridge, TN 37831
191-217. Technical Information Center, Oak Ridge, TN 37831
4U.S. GOVERNMENT PRINTING OFFICE: 1984-544-045/4254
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