and
OAK RIDGE
NATIONAL
LABORATORY
MARTIN Martl
ORNL/TM-9070
Unit Release Risk Analysis for
Environmental Contaminants of
Potential Concern in
Synthetic Fuels Technologies
L. W. Barnthouse
G. W. Suter II
C. F. Baes III
S. M. Bartell
R. H. Gardner
R. E. Millemann
R. V. O'Neill
C. D. Powers
A. E. Rosen
L. L. Sigal
D. S. Vaughan
fflUTEMY
ifflflHMRIETTA ENERGY SYSTEMS, INC.
OR THE UNITED STATES
IMHTMHIT OF ENERGY
ENVIRONMENTAL SCIENCES DIVISION
Publication No. 2291
•
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5285 Port Royal Road, Springfield, Virginia 22161
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This report was prepared as an account of work sponsored by an agency of the
United States Government. Neither the United States Government nor any agency
thereof, nor any of their employees, makes any warranty, express or implied, or
assumes any legal liability or responsibility for the accuracy, completeness, or
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represents that its use would not infringe privately owned rights Reference herein
to any specific commercial product, process, or service by trade name, trademark,
manufacturer, or otherwise, does not necessarily constitute or imply its
endorsement, recommendation, or favoring by the United States Government or
any agency thereof The views and opinions of authors expressed herein do not
necessarily state or reflect those of the United States Government or any agency
thereof
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ORNL/TM-9070
ENVIRONMENTAL SCIENCES DIVISION
UNIT RELEASE RISK ANALYSIS FOR ENVIRONMENTAL CONTAMINANTS OF
POTENTIAL CONCERN IN SYNTHETIC FUELS TECHNOLOGIES
Authors
L. W.
G. W.
C. F.
S. M.
R. H.
R. E.
R. V.
C. D.
A. E.
L. L.
D. S.
Barnthousel
Suter II1
Baes III
Bartell
Gardner
Millemann
O'Neill
Powers
Rosen
Si gal
Vaughan
ORNL Project Manager
S. G. Hildebrand
Environmental Sciences Division
Publication No. 2291
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. DW89930292-01-0
(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
-------
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 No. DW89930292-01-0 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.
-------
TABLE OF CONTENTS
LIST OF TABLES v
LIST OF FIGURES vii
SUMMARY ix
ABSTRACT xiii
1. INTRODUCTION 1
2. EXPOSURE ASSESSMENT 4
2.1 Surface Water 4
2.1.1 Stream Characteristics 4
2.1.2 Contaminant Characteristics 5
2.1.3 Results 9
2.2 Atmospheric Dispersion and Deposition 12
3. AQUATIC ENDPOINTS 16
3.1 Quotient Method 16
3.2 Analysis of Extrapolation Error 22
3.2.1 Methods 22
3.2.2 Results 25
3.3 Ecosystem Uncertainty Analysis 25
3.3.1 Explanation of Method 25
3.3.2 Results of Ecosystem Uncertainty Analysis .... 26
3.3.3 Patterns of Sensitivity Across Populations .... 29
3.3.4 Population Sensitivity Patterns and Risk 34
3.3.5 Importance of Patterns of Sensitivity 37
4. TERRESTRIAL ENDPOINTS 38
4.1 Vegetation 38
4.2 Wildlife 41
5. EVALUATION OF RISKS 44
5.1 Evaluation of Risks to Fish 44
5.1.1 Differences Among RACs 44
5.1.2 Differences in Sensitivity Among Fish Species . . 47
5.1.3 Differences in Risk Between Sites 47
5.2 Evaluation of Risks of Algal Blooms 48
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6.
7,
5.2.1 Comparison of Uncertainties Concerning
Exposure Concentrations and Effects
Concentrations
5.3 Evaluation of Risks to Vegetation and Wildlife
5.4 Validation Needs
ACKNOWLEDGMENTS
REFERENCES
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
DETAILED METHODS AND ASSUMPTIONS FOR ECOSYSTEM
UNCERTAINTY ANALYSIS
Page
49
51
52
53
54
69
85
101
105
. 117
IV
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LIST OF TABLES
Table Page
1.1 Risk Analysis Units (RACs) 2
2.1-1 Stream characteristics for the eastern reference site ... 6
2.1-2 Stream characteristics for the western reference site ... 7
2.1-3 Contaminant characteristics 8
2.1-4 Near-field contaminant concentrations (g/L) in the
eastern and western reference streams 10
2.1-5 Median half-lives and dominant removal processes of
contaminants in eastern and western reference stream
reaches 11
2.2-1 Maximum ambient atmospheric and soil concentrations
of RACs at the eastern and western reference sites .... 14
3.1-1 Toxicity quotients for toxicity to fish and algae
(ambient contaminant concentration/toxic benchmark
concentration) for unit release 18
3.2-1 Ranges of ratios of ambient concentrations to PGMATC
and probabilities of exceeding the PGMATC for unit
release, eastern and western sites 24
3.3-1 Values of LC^Q/EC^Q (mg «L~^) used to calculate
E matrix for SWACOM (Appendix E) 27
3.3-2 Risks associated with nine risk assessment units,
as estimated by ecosystem uncertainty analysis 28
3.3-3 Trophic patterns in sensitivity 31
3.3-4 Comparison of responses to different patterns of
sensitivity 32
3.3-5 Risk associated with a fourfold increase in noxious
blue-green algae blooms and 25% reduction in average
annual biomass of game fish 35
3.3-6 Ranking of nine chemicals according to their
calculated risk effect (Table 3.3-2) and their
LC5o's (ECso's) normalized across population and
trophic levels 36
4.1-1 Toxicity quotients for terrestrial plants 39
4.2-1 Toxicity quotients for terrestrial animals 42
5.1-1 Rankings of Risk Analysis Units (RACs) according
to the quotient method (QM) and analysis of
extrapolation error (AEE), in order of decreasing
risk to fish 45
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Page
Rankings of nine Risk Analysis Units (RACs) according
to the quotient method (QM), analysis of
extrapolation error (AEE), and ecosystem uncertainty
analysis (EUA), in order of decreasing risk to fish .... 4b
A-l Acute toxicity of synfuels chemicals to aquatic
animals 71
A-2 Chronic toxicity of synfuels chemicals to aquatic
animals 80
A-3 Toxicity of synfuels chemicals to algae 82
B-l Toxicity of chemicals in air to vascular plants 87
B-2 Toxicity of chemicals in soil or solution to
vascular plants 90
B-3 Toxicity of chemicals in air to animals 95
D-l Predicted geometric mean maximum allowable
toxicant concentrations (PGMATCs) for each RAC
and each species of fish 107
D-2 Probabilities of chronic toxic effects on fish
populations due to RAC 4 at annual median ambient
concentrations for unit release 108
D-3 Probabilities of chronic toxic effects on fish
populations due to RAC 5 at annual median ambient
concentrations for unit release 109
D-4 Probabilities of chronic toxic effects on fish
populations due to RAC 15 at annual median ambient
concentrations for unit release 110
D-5 Probabilities of chronic toxic effects on fish
populations due to RAC 22 at annual median ambient
concentrations for unit release Ill
D-6 Probabilities of chronic toxic effects on fish
populations due to RAC 26 at annual median ambient
concentrations for unit release 112
D-7 Probabilities of chronic toxic effects on fish
populations due to RAC 32 at annual median ambient
concentrations for unit release 113
D-8 Probabilities of chronic toxic effects on fish
populations due to RAC 32A at annual median ambient
concentrations for unit release 114
D-9 Probabilities of chronic toxic effects on fish
populations due to RAC 33 at annual median ambient
concentrations for unit release 115
D-10 Probabilities of chronic toxic effects on fish
populations due to RAC 34 at annual median ambient
concentrations for unit release
VI
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LIST OF FIGURES
Figure Page
3.1-1 RACs (Table 1.1) arranged according to their acute
toxicities to fish, as determined by the quotient
method using the unit release concentrations from
the eastern site 19
3.1-2 RACs (Table 1.1) arranged according to their
toxicities to freshwater algae, as determined by
the quotient method using the annual median unit
release concentrations from the eastern site 20
vn
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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 Units (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 presents results of a unit release risk analysis,
i.e., an analysis that assumes identical release rates for all RACs.
The primary purpose of this analysis is to compare the relative hazards
of the 38 RACs, based purely on their environmental toxicology and
chemistry, and to quantify and compare the major sources of uncertainty
concerning their fate and effects.
Two reference environments were employed: an eastern environment
resembling eastern Kentucky or West Virginia and a western environment
resembling the western slope of the Rocky Mountains in northern
Colorado or southern Wyoming. Estimates of concentrations of released
contaminants in the air, soil, and surface water of the two reference
environments were obtained, using a simple Gaussian-plume atmospheric
dispersion and deposition model and a steady-state surface water fate
model.
ix
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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 analysis. 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 two reference environments. In ecosystem uncertainty analysis, an
aquatic ecosystem model was used to compute risk estimates that
explicitly incorporate biological phenomena such as competition and
predation that can magnify or offset the direct effects of contaminants
on organisms.
With only environmental transport and toxicity of the 38 classes
of contaminants accounted for, acid gases (primarily hydrogen sulfide),
esters, mercury, and cadmium were found to have the greatest potential
effects on fish populations. Based on the ecosystem uncertainty
analysis, it appears that contaminants that are highly toxic to fish
are the most likely to produce increases in algal biomass. Existing
data were insufficient for performing separate risk analyses for forests
and crops. For terrestrial plants in general, hydrogen sulfide was
found to be the most toxic gaseous pollutant. Of contaminants likely
to be deposited on soil, arsenic, cadmium, and nickel appear most
likely to accumulate to toxic levels. The most serious threats to
wildlife, considering only inhalation exposures, are aldehydes and
ketones, cadmium, arsenic, and respirable particles.
Between-site comparisons were performed for aqueous releases.
Because of differences in important hydrological parameters, especially
sediment loading, estimated half-lives of many contaminants differ
significantly between sites. Sedimentation rates of hydrophobic
contaminants are higher in the western river. Photolysis rates of
= concentration lethal to 50% of population exposed.
-------
photodegradable compounds are higher in the eastern river. The
salmonid fishes found in western rivers are more sensitive to many
contaminants (most notably cadmium) than are the fish typically found
in eastern rivers.
A number of significant uncertainties were identified.
Toxicological data suitable for use in risk analysis are sparse for
most organisms other than fish. The data that do exist are frequently
of limited utility because of the diversity and lack of comparability
of the test systems employed. Magnitudes of uncertainty concerning
(1) the expected environmental concentrations of contaminants in the
vicinity of synfuels plants and (2) predicted effects thresholds for
fish were compared. This comparison shows that, at least for the
contaminants occurring in synfuels products and effluents, uncertainty
concerning the toxicological effects of contaminants is much greater
than is uncertainty concerning environmental transport.
XI
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ABSTRACT
BARNTHOUSE, L. W., G. W. SUTER II, C. F- BAES III,
S. M. BARTELL, R. H. GARDNER, R. E. MILLEMANN,
R. V. O'NEILL, C. D. POWERS, A. E. ROSEN, L. L. SIGAL,
and D. S. VAUGHAN. 1984. Unit release risk analysis
for environmental contaminants of potential concern in
synthetic fuels technologies. ORNL/TM-9070. Oak Ridge
National Laboratory, Oak Ridge, Tennessee. 136 pp.
This report contains results of a risk analysis study of
38 categories of chemical contaminants [Risk Analysis Units (RACs)]
that may be released to the environment by synthetic fuels production
facilities. The analysis includes modeling of the environmental
transport and fate of contaminants in the atmosphere and in surface
water, and quantification of risks for five ecological endpoints. Two
generic "reference environments" with meteorological, hydrological, and
biological characteristics representative of (1) the central
appalachian coal basin and (2) the western slope of the Rocky Mountains
were used. A uniform release rate was assumed for all RACs.
Consequently, the primary objectives of the risk analysis were to
(1) estimate the relative risks of the RACs as functions of their
environmental chemistry and toxicology, and (2) to quantify and compare
the major sources of uncertainty concerning the fate and effects of the
contaminants.
xi n
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1. INTRODUCTION
Environmental risk analysis is defined as the process of
identifying and quantifying probabilities of adverse changes in the
environment resulting from human activities. This includes explicit
incorporation and, to the extent possible, quantification of scientific
uncertainties regarding 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 employed 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
is focusing on risks associated with toxic environmental contaminants
derived from synthetic liquid fuels technologies. The overall objective
of the project is to guide reseach 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 HSK analysis, the thousands of potentially
significant contaminants present in waste streams and products of
synthetic liquid fuels technologies have been grouped into the
38 categories, termed Risk Analysis Units (RACs), listed in Table 1.1.
Five ecological endpoints are addressed: (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
employed in research for the USEPA: an eastern environment resembling
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ORNL/TM-9070
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
NO;
H2S, HCN
NH3
Methane through butanes,
acetylene, ethene
7 Formaldehyde
8 Volatile organochlorines
9 Volatile carboxylic acids
10 Volatile 0 & S heterocyclics
11 Volatile N heterocyclics
12 Benzene
13 Aliphatic/alicyclic
hydrocarbons
14 Mono- or diaromatic hydro-
carbons (excluding
benzene)
15 Polycyclic aromatic
hydrocarbons
16 Aliphatic amines (excluding
N heterocyclics)
17 Aromatic amines (excluding
N heterocyclics)
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
through butenes; C-|-C4 alkanes, alkynes
and cyclocompounds; bp < %20°C
HCHO
To bp -\,120°C; CH2C12, CHC13, OC14
To bp ^120^; formic and acetic acids only
To bp ^120°C; furan, THF, thiophene
To bp VI20°C; pyridine, piperidine,
pyrrolidine, alkyl pyridines
Benzene
Cs (bp %40°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, ami no 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-9070
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).
This report presents results of a unit release risk analysis, i.e.,
an analysis that assumes identical release rates for all of the RACs
listed in Table 1.1. The unit release risk analysis is intended to
compare the relative hazards of the various RACs, based purely on their
environmental chemistry and toxicology, and to quantify and compare the
major sources of uncertainty concerning their fate and effects. In
addition, the unit release risk analysis provides initial information
on the relative risks of the RACs to eastern and western ecosystems.
Finally, this analysis identifies significant gaps in the chemical and
toxicological data bases that are used for synfuels risk analysis.
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ORNL/TM-9070
2. EXPOSURE ASSESSMENT
The exposure assessments presented in this section used the
atmospheric transport and deposition and surface water transport and
transformation models described in Travis et al. (1983). Exposure
assessments were performed for both the eastern and the western sites
described by Travis et al. (1983).
2.1 SURFACE WATER
Estimates of the concentrations of 30 RACs in the surface waters
of the eastern and western reference sites were calculated. The only
RACs for which analyses were not performed were gases (e.g., COp,
S09, NOJ that could not reasonably be expected to occur in aqueous
CX. p
effluents. A unit release rate of 4.12 x 10 g/s was assumed for
all RACs in both reference environments. This number was the median of
17 release rates employed in a preliminary risk analysis for indirect
coal liquefaction and therefore was felt to be a reasonable value.
2.1.1 Stream Characteristics
The environmental parameters used in determining stream
characteristics were stream flow (m/s), stream width (m), reach
3
length (m), 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 eastern site were set within ranges observed by the U.S.
Geological Survey (USGS) for the Big Sandy River at Louisa, Kentucky,
and the Monongahela Kiver at Braddock, Pennsylvania (USGS 1977, 1979).
For the western site, these estimates were obtained from USGS data for
the Colorado River at De Beque, Colorado (USGS 1980). Values for the
other stream parameters were taken from Southworth (1979). Irradiance
2
values [photons/(cm *s)] for estimating photolysis rates were
obtained from Zepp and dine'(1977).
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5 ORNL/TM-9070
The effects of environmental variability on contaminant transport
and fate were quantified, using the probabilistic version of the
surface water transport model. 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 assessments
are presented in Tables 2.1-1 and 2.1-2.
2.1.2 Contaminant Characteristics
For determining the characteristics of organic contaminants,
(Table 2.1-3), 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 was used for the unit release 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 - to 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 Oil ling (1977).
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 and quinoline were calculated using the method
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ORNL/TM-9070 6
Table 2.1-1. Stream characteristics for the eastern reference site
Environmental
parameter
Stream flow
Reach length
Stream width
Suspended solids
Sediment depth
Solids density
Fraction organic
carbon
Particle radius
Temperature
Current velocity
Wind velocity
Units
m3/s
m
m
mg/L
cm
g/cm
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
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7 ORNL/TM-9070
Table 2.1-2. Stream characteristics for the western reference site
Environmental
parameter
Stream flow
Reach length
Stream width
Suspended solids
Sediment depth
Solids density
Fraction 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
175
1000
20
260
1
1.02
0.1
0.005
292
0.5
1.5
Standard
deviation
100
0
0
200
0
0
0.1
0.0025
3
0.2
0.1
Minimum
value
40
1000
20
50
1
1.02
0.05
0.001
280
0.2
0.25
Maximum
value
600
1000
20
1000
1
1.02
0.25
0.01
305
2.0
4.0
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ORNL/TM-9070
Table 2.1-3. 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 Octanol-water Quantum
or atomic Aqueous partition yield of
Representative weight3 solubility0 coefficient photolysis
contaminant (g/mol) (g/L) (log P) (unitless)
Hydrogen sulfide
Ammonia
Butane
Formaldehyde
Methylene chloride
Acetic acid
Thiophene
Pyridine
Benzene
Cyclohexane
Toluene
Anthracene
Aniline
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
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
-0.17C
1.81C
0.650C
2.13C
4.0C
2.69C
4.45C 0.003d
0.90C
4.12C
0.79C
1.46°
0.90e
-0.660C
-0.74C
2.31e
-0.92C
JWeast (1980).
5Verschueren (1977).
:Leo et al. (1971).
jZepp and Schlotzhauer (1979).
2Briggs (1981).
-------
9 ORNL/TM-9070
of Zepp and Cline (1977). Adsorption/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 due to
sedimentation were estimated using an adsorption-desorption coefficient
of 200. The results of Schell and Sibley's (1982) study of
distribution coefficient for radionuclides suggest that this is a
conservative estimate for most trace elements under most environmental
conditions.
2.1.3 Results
Comparisons were performed for both reference streams, using a
_2
source rate of 4.12 x 10 g/s for all contaminants. 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 Table 2.1-4.
For all practical purposes, the concentrations computed using
contaminant-specific removal rates are identical to concentrations
computed from pure dilution. Thus, at least in the immediate vicinity
of contaminant sources located on rivers such as the eastern and
western reference streams, 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.
Estimates of the half-lives of 23 reference contaminants for which
removal rates were calculated are presented in Table 2.1-5. These
values can be interpreted as estimates of the time required to reduce
the total mass of contaminant in the water column by one-half after
cessation of contaminant release. The half-lives range from 100 to
5000 h accounting for the negligible influence of removal processes on
the steady-state contaminant concentrations. For many contaminants,
the half-lives differ markedly between sites, principally because of
the tenfold difference in sediment loads between the eastern and
western rivers. For contaminants for which sedimentation is the
-------
ORNL/TM-9070
10
Table 2.1-4. Near-field contaminant concentrations (g/L) in the
eastern and western reference streams3
Reference
environment
Eastern
Eastern
Eastern
Western
Western
Contaminant
Anthracene
All others
Dilution only
All
Dilution only
Mean
3.4 E-07
3.4 E-07
3.4 E-07
2.8 E-07
2.8 E-07
Median
3.0 E-07
3.0 E-07
3.0 E-07
2.2 E-07
2.2 E-07
95%b
6.4 E-07
6.7 E-07
6.7 E-07
6.4 E-07
6.4 E-07
Release rate = 4.12 E-02 g/s for all contaminants.
bConcentration expected to be equaled or exceeded on 5% of days.
-------
11
ORNL/TM-9070
Table 2.1-5.
Median half-lives and dominant removal processes of
contaminants in eastern and western reference stream reaches
RAC
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
28
31
32
33
34
35
av =
S =
P =
Reference
contaminant
Acetic Acid
Thiophene
Pyridine
Benzene
Cyclohexane
Toluene
Anthracene
Methyl ami ne
Aniline
Quinoline
Dibenzofuran
Butanoic acid
Phenol
Acrolein
Methanethiol
Methanol
Nitrobenzene
Acrylonitrile
Arsenic
Mercury
Nickel
Cadmium
Lead
volatilization.
sedimentation.
photolysis.
Eastern
Median
half-life
(h)
1.1 E+03
1.3 E+03
1.3 E+03
1.2 E+03
6.9 E+02
1.3 E+03
8.6 E+01
8.0 E+02
1.4 E+03
2.8 E+03
5.6 E+02
1.4 E+03
1.4 E+03
1.1 E+03
1.0 E+03
8.1 E+02
1.6 E+03
1.0 E+03
4.8 E+03
4.8 E+03
4.8 E+03
4.8 E+03
4.8 E+03
site
Dominant
removal
process3
V
V
V
V
V
V
P
V
V
P
S
V
V
V
V
V
V
V
S
S
S
S
S
Western
Median
half-life
(h)
9.0 E+02
1.1 E+03
1.0 E+03
8.9 E+02
2.2 E+02
7.2 E+02
7.6 E+01
6.5 E+02
1.2 E+03
5.0 E+03
1.3 E+02
1.1 E+03
1.2 E+03
1.2 E+03
8.1 E+02
6.6 E+02
1.0 E+03
8.5 E+02
5.7 E+02
5.7 E+02
5.7 E+02
5.7 E+02
5.7 E+02
site
Dominant
removal
process3
V
V
V
V
S
V
S
V
V
V
S
V
V
V
V
V
V
V
S
S
S
S
S
-------
ORNL/TM-9070 12
dominant removal process, half-lives are 5 to 10 times longer in the
eastern river than in the western river. Conversely, photolysis, which
is the dominant removal process for anthracene and quinoline in the
eastern river, is greatly reduced in the western river. For
anthracene, this decrease is more than offset by an increase in
sedimentation rate; for the highly soluble quinoline, the decrease in
photolysis results in an approximate doubling of the half-life.
2.2 ATMOSPHERIC DISPERSION AND DEPOSITION
The terrestrial assessment was based on an atmospheric release
g
rate for all RACs of 10 g/year (a reasonable release rate for major
gaseous pollutants from a synfuels plant). The emissions were
partitioned among five sources based on their distribution among
sources at an indirect coal liquefaction plant. The sources were a
150-m stack, a 6.5-m locK-hopper vent, a 25-m cooling tower, and area
emissions from a tank farm and fugitive sources.
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 is
summarized in Travis et al. (1983), who also describe the method for
calculating accumulation in soil. Soil concentrations are calculated
for a 35-year accumulation period, using site-specific parameters for
soil bulk density, precipitation, evapotranspiration, and irrigation,
and taking into account removal by leaching, biological degradation,
and chemical degradation.
Because most phytotoxicity studies are conducted 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 as above: that is, the depositing material is
summed over the lifetime of the facility and corrected 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,
-------
13 ORNL/TM-9070
where
C. = the concentration of compound i in root zone soil solution
(yg/L),
C. = the concentration of compound i in root zone soil
(ug/kg),
Kd = the distribution coefficient (L/kg).
Because Kj is in the denominator of Eq. (1), the soil solution
concentration C. could take on extremely high values with small values
of K^. In order to bound the maximum value of C^ s, it is assumed that
the 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
r max
**
iss 10 p 9 d Xsi
where
D. = the ground-level deposition rate of compound i
[ug/(m2-s)],
\.ci = the sum of all soil removal rate constants (L/s),
the period of long-term buildup in soil, equal to
of time that the source term is in operation (s),
a conversion fa(
(1 kg/1000 g)],
t, = the period of long-term buildup in soil, equal to the length
of time that the source term
22 22
10 = a conversion factor from g/cm to kg/m [(10,000 cm /I m )
3
p = soil bulk density (g/cm ),
3 3
6 = volumetric water content (cm /cm ),
d = the depth of the root zone (cm),
2
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
-------
Table 2.2-1.
Maximum ambient atmospheric and soil concentrations of RACs at the eastern and western reference
sites
Annual average Concentration
concentration in air in soil3
(ug/m3) (ug/kg)
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
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
Formaldehyde
Volatile organochlorines
Volatile carboxylic acids
Volatile 0 & S heterocyclics
Volatile N-heterocyclics ,
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
Eastern
65.7
0.134
0.112
65.2
9.82
63.4
43.7
65.4
4.46
66.7
4.45
67.5
26.2
59.0
70.5
56.6
50.8
4.45
4.76
27.9
43.3
55.6
50.4
62.3
56.7
56.7
64.0
64.1
65.9
65.9
4.35
0.336
47.9
4.19
4.30
Western
93.3
0.331
0.263
92.4
15.4
88.2
61.9
92.7
7.51
94.9
7.49
96.4
29.5
82.8
99.8
80.3
63.8
7.50
8.04
40.7
59.5
78.6
65.9
88.6
80.4
80.4
91.0
91.1
93.7
93.7
7.33
0.584
68.1
7.06
7.12
Eastern
a
a
a
a
a
25.5
2240
4.93
829
2.74
243
28.1
623
34.3
6330
639
257
445
0.181
804
13100
89.4
60.3
614
1350
2050
73.4
103
a
a
1.57 E+06
53.6
1.58 E+06
2.36 E+05
5.51 E+05
Western
a
a
a
a
a
35.2
2810
6.93
1080
3.87
335
38.7
699
47.8
8760
863
304
627
0.305
1120
11200
126
78.3
815
1850
2530
101
137
a
a
1.81 E+06
40.5
1.71 E+06
1.45 E+05
7.48 E+05
Concentration in
soil solution3
(yg/L)
Eastern
a
a
a
a
a
26.4
4610
4.63
1710
2.28
501
21.6
44.5
6.85
97.4
1320
531
171
0.0475
670
19300
184
27.4
1270
792
4230
151
213
a
a
7860
5.36
10500
36200
612
Western
a
a
a
a
a
36.4
5780
6.51
2220
3.23
691
29.8
49.9
9.55
135
1780
626
241
0.0802
931
16500
260
35.6
1680
1090
5220
207
283
a
a
9050
4.05
11400
22300
831
I
vo
O
^J
o
3No accumulation in soil.
-------
15 ORNL/TM-9070
measured values of K, are usually under saturated conditions, 9 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 are presented in Table 2.2-1. These
Q
results are based on a release rate for all RACs of 10 g/year.
-------
ORNL/TM-9070
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 (EC20). the
mean toxic concentration (MTC), the threshold bioaccumulation
concentration (TBC), the lowest observed toxic concentration (LOTC),
the median tolerance limit (TLm), and the concentration required to
kill 50% of the test organisms (LC5Q). The benchmarks used in this
risk analysis are presented in Appenidix A.
Since this report compares potential toxic differences between
groups of chemicals (RACs), benchmarks common to as many of the RACs as
possible were preferred. LC5Q and TL , the two benchmarks most
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,
and therefore, different 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
extensively tested heavy metals, several representative values are
included for the sake of brevity.
16
-------
17 ORNL/TM-9070
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 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.
Table 3.1-1 presents the highest quotients for each RAC and
category of effect for both the eastern and western sites. The acute
toxicity quotients were calculated using the upper 95th percentile
concentration (Sect. 2), an estimate of the worst acute exposure,
assuming stable plant operation. The chronic quotients were calculated
using the annual median concentration, and 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
inhabiting 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; 0.1 to 10
(1.0 E+01) suggests possible or potential adverse effects; and greater
than 10 describes a chemical of probable environmental concern. While
these interpretations are consistent with current practice in hazard
assessment, their utility in screening chemicals for risk analysis must
be confirmed by experimental research and environmental monitoring.
To facilitate evaluation of the data in Table 3.1-1, the range of
quotients for each RAC (for which data were available) is plotted for
fish acute toxicity and for algal toxicity (Figs. 3.1-1 and 3.1-2).
Thus, the relative toxicities of most of the chemicals is readily
apparent. Although 18 of the 24 RACs in Fig. 3.1-1 overlap between the
limits of 0.01 (E-02) and 0.00001 (E-05), only five of them (RACs 4,
15, 22, 32, and 34) extend beyond the limit of 0.01 (E-02) and one
-------
Table 3.1-1.
Toxicity quotients for toxicity to fish and algae (ambient contaminant concentration/toxic benchmark concentration) for
unit release
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
aThe
by
RAC Name
Carbon monoxide
Sulfur oxides
Nitrogen oxides
Acid gases
Alkaline gases
Hydrocarbon gases
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
Phenols
Aldehydes and ketones
Nonheterocyclic organosulfur
Alcohols
Nitroaromatics
Esters
Amides
Nitriles
Tars
Respirable particles
Arsenic
Mercury
Nickel
Cadmium
Lead
-Other trace elements
quotients are calculated using the
a fish (Table A-2), and the lowest
Highest
Fish, acute
95%
b
c
c
7.47 E-02
9.88 E-03
1.36 E-07
1.34 E-05
2.46 E-05
7.64 E-06
b
b
1.27 E-04
4.80 E-05
2.92 E-04
1.59 E-02
b
4.48 E-04
b
3.73 E-06
8.67 E-05
1.46 E-02
b
b
b
9.21 E-04
b
6.65 E-05
d
quotient -
eastern site3
Fish, chronic Algae
Median
b
c
c
2.52 E-04
b
b
4.87 E-04
b
b
1.38 E-04
1.44 E-02
b
b
b
3.78 E-02
b
1.16 E-04
d
Median
b
c
c
b
b
5.75 E-07
9.15 E-06
5.50 E-06
b
3.02 E-02
b
1.51 E-05
b
b
b
2.75 E-03
b
d
95%
b
c
c
b
b
1.28 E-06
2.04 E-05
1.17 E-05
b
6.72 E-02
b
3.36 E-05
b
b
b
6.11 E-03
b
d
Highest quotient - western site3
Fish, acute
95%
b
c
c
7.11 E-02
9.41 E-03
1.30 E-07
1.28 E-05
2.34 E-05
7.27 E-06
b
b
1.21 E-04
4.57 E-05
2.78 E-04
1.60 E-02
b
4.27 E-04
b
3.56 E-06
8.26 E-05
1.39 E-02
b
b
b
8.77 E-04
b
6.34 E-05
d
Fish, chronic Algee
Median
b
c
c
1.84 E-04
b
b
3.56 E-04
b
b
1.09 E-04
1.05 E-02
b
b
b
2.76 E-02
b
8.50 E-05
d
Median
b
c
c
b
b
4.21 E-07 1
6.70 E-06 1
4.06 E-06 1
b
2.21 E-02 6
b
1.11 E-05 3
b
b
b
2.01 E-03 5
b
d
95%
b
c
c
b
b
.22 E-06
.94 E-05
.18 E-05
b
.40 E-02
b
.20 E-05
b
b
b
.82 E-03
b
d
No aquatic emissions
5.04 E-05
2.80 E-02
1.46 E-04
6.72 E-01
1.12 E-03
2.92 E-04
lowest acute
algal response
6.04 E-05
1.31 E-00
2.77 E-03
1.78 E-01
1.59 E-02
2.67 E-06
LCso or TLm
(Table A-3)
1.30 E-04
3.78 E-03
3.02 E-03
6.04 E-02
6.04 E-04
for fish in
2.90 E-04
8.40 E-03
6.72 E-03
1.34 E-01
1.34 E-03
each RAC
4.80 E-05
2.67 E-02
1.39 E-04
6.40 E-01
1.07 E-03
2.78 E-04
(Table A-l),
4.42 E-05
9.61 E-01
2.03 E-03
1.30 E-01
1.16 E-02
1.96 E-06
9.53 E.05 2
2.76 E-03 8
2.21 E-03 6
4.42 E-02 1
4.42 E-04 1
.76 E-04
.00 E-03
.40 E-03
.28 E-01
.28 E-03
the lowest chronic response
with either the median or upper 95th percentile
^f. I T-.U 1 — O 1 A \
of the predicted
ambient contaminant concentration at the eastern and western sites (Table 2.1-4).
''No toxicity data.
cAquatic problems associated with pH, not direct toxicity.
dNo aquatic emissions.
I
VD
O
CD
-------
19 ORNL/TM-9070
ORNL-DWG 82-18466
I—7—I
-11—1 ' 9 '
1 ~ 12
22
11 ml i i i i mil i i i 11 ml i i i 11 nil i i i 11 ml i i i 11 ml i i i 11 in
E-07 E-06 E-05 E-04 E-03 E-02 E-01 EOO
Fig. 3.1-1. RACs (Table 1.1) arranged according to their acute
toxicities to fish, as determined by the quotient
method using the unit release concentrations from the
eastern site. The scale ranges from 1.0 x 10~7
(E-07) to 1.0 (E+00). The farther to the right an RAC
appears in the figure, the greater its potential for
adverse environmental effects.
-------
ORNL/TM-9070 20
ORNL-DWG 82-18465
I I II 1111 I I I I I III I I I I I I ll| I I I I I I ll| I I I I I I ll| I l l l l Ml| i i i i i i
III
E-06 E-05 E-04 E-03 E-02 E-01 EDO E01
Fig. 3.1-2. RACs (Table 1.1) arranged according to their toxicities
to freshwater algae, as determined by the quotient
method using the annual median unit release
concentrations from the eastern site. The scale ranges
from 1.0 x lO'7 (E-07) to 1.0 (E+00). The farther to
the right an RAC appears in the figure, the greater its
potential for adverse environmental effects.
-------
21 ORNL/TM-9070
(RAC 34) exceeds 0.1 (E-01). These six (acid gases, polycyclic
aromatic hydrocarbons, aldehydes and ketones, mercury, and cadmium),
then, can be considered as most likely to harm fish and merit further
risk analyses and research on their ecological effects. Conversely,
RACs 7, 8, 9, 12-14, 18, 20, 21, 26, 31, 35, and 36 (Table 1.1) appear
to represent the least threat to the freshwater fish. Only two RACs,
aromatic amines (17) and cadmium (34), appear to pose a significant
threat of algal toxicity.
The high ranking of RAC 15 may be due to the inclusion of data
obtained using the trout embryo-larval acute assay, which appears to be
considerably more sensitive than more commonly used tests for acute
toxicity. If the other contaminants had been tested using this assay,
their estimated toxicities would likely have been substantially higher.
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 (nickel, cadmium, and lead), 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
water. 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.
-------
ORNL/TM-9070 22
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 the field 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 and western reference sites (Travis et al. 1983). The
acute toxicity criterion is the 96-h LC(-Q. The chronic toxicity
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
A detailed description of the computational methods used for the
analysis of extrapolation error (AEE) is contained in Suter and Vaughan
(1954). 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, and used in obtaining estimates
of risk when given estimates of the distribution of the ambient
contaminant concentrations.
-------
23 ORNL/TM-9070
Twenty-one RACs have been analyzed by the extrapolation error
method (Table 3.2-1). These are all of the RACs for which 96-h
LC^Q'S could be found. The ratio of the ambient concentration of an
RAC to its predicted GMATC (PGMATC) is presented as an estimate of the
hazard with respect to 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 annual average ambient concentrations (Table 2.1-4).
In general, the extrapolation between species was performed 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
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 minnow (Pimephales promelas). Difficulties arose with
RACs 4 and 15 in estimating the acute toxicity of white bass (Morone
chrysops) and with RACs 13 and 18 in estimating the acute toxicity of
bigmouth and smallmouth buffalo (Ictiobus cyprinellus and ^. bulbalus).
The problem arose because no fish in the family Percichthyidae or in
the genus Ictiobus were tested at the Columbia National Fisheries
Research Laboratory. The genus Ictiobus falls within 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 to all
<•>
Perciformes other than bluegills (R = 0.91) and fathead minnow to
o
all Cypriniformes other than fathead minnow (R = 0.92).
-------
ORNL/TM-9070
24
Table 3.2-1.
Ranges of ratios of ambient concentrations to PGMATC and
probabilities of exceeding the PGMATC for unit release, eastern
and western sites
Ratio of ambient
concentration
to PGMATC"
Probability of exceeding the PGMATC0
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
32A
33
34
35
Eastern
b
b
b
0.0261-0.1940
0.0069-0.0168
0.0000-0.0000
b
0.0002-0.0027
0.0003-0.0014
b
b
0.0007-0.0026
0.0011-0.0046
0.0021-0.0046
0.0016-0.0136
b
b
0.0005-0.0021
b
0.0000-0.0002
0.0007-0.0058
0.0238-0.1263
b
b
b
0.0011-0.0374
b
0.0008-0.0075
b
b
0.0006-0.0045
0.0088-0.0216
0.0259-0.0675
0.0003-0.0115
0.0039-0.5739
0.0007-0.0056
Western
b
b
b
0.0839-0.0839
0.0144-0.0149
0.0000-0.0000
b
0.0004-0.0004
0.0009-0.0009
b
b
0.0018-0.0026
0.0032-0.0032
0.0034-0.0044
0.0030-0.0030
b
b
0.0014-0.0014
b
0.0002-0.0002
0.0011-0.0017
0.0507-0.0550
b
b
b
0.0015-0.0374
b
0.0014-0.0014
b
b
0.0008-0.0009
0.0184-0.0186
0.0498-0.0948
0.0004-0.0008
0.7237-1.1682
0.0022-0.0036
Eastern
b
b
b
0.0468-0.2261
0.0097-0.0196
0.0000-0.0214
b
0.0000-0.0053
0.0000-0.0015
b
b
0.0001-0.0035
0.0002-0.0071
0.0008-0.0062
0.0004-0.0262
b
b
0.0000-0.0027
b
0.0000-0.0267
0.0000-0.0115
0.0266-0.1711
b
b
b
0.0007-0.0667
b
0.0001-0.0146
b
b
0.0000-0.0088
0.0130-0.0252
0.0428-0.0853
0.0001-0.0225
0.0008-0.3908
0.0000-0.0091
Western
b
b
b
0.1117-0.1117
0.0090-0.0149
0.0002-0.0002
b
0.0001-0.0001
0.0008-0.0008
b
b
0.0002-0.0011
0.0046-0.0046
0.0008-0.0026
0.0021-0.0021
b
b
0.0015-0.0015
b
0.0016-0.0016
0.0001-0.0005
0.0538-0.0628
b
b
b
0.0002-0.0009
b
0.0006-0.0006
b
b
0.0000-0.0001
0.0132-0.0197
0.0478-0.0964
0.0000-0.0001
0.4308-0.5332
0.0003-0.0009
aSpecies-specific values are presented in Appendix D.
3No toxicity data.
-------
25 ORNL/TM-9070
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. The species-specific
hazard and risk values are presented only for those RACs with a hazard
greater than or equal to 0.01. They are summarized in Table 3.2-1.
The RACs for which any of the nine eastern species had a nonzero hazard
or risk are (in decreasing rank order): acid gases, mercury (methyl),
aldehydes and ketones, cadmium, mercury (inorganic), alkaline gases,
esters, polycyclic aromatic hydrocarbons, and nickel. The RACs for
which either of the western species had a nonzero risk are (in
decreasing rank order): cadmium, acid gases, mercury (methyl),
aldehydes and ketones, mercury (inorganic), alkaline gases, polycyclic
aromatic hydrocarbons, esters, and nickel. These rankings are based on
the geometric mean across the nine eastern species or two western
species of either the hazard quotients or the risk probabilities (the
results were the same for hazard and risk). Cadmium, acid gases, and
mercury (methyl) were each the most toxic RAC for at least one of the
fish species.
The differences in the relative rankings between species is
attributable to variation in three factors: (1) the magnitudes of the
LCr0's of different species that have been tested for a 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.
3.3 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.
-------
ORNL/TM-9070 26
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 primarily concern mortality. Therefore,
assumptions about the mode of action of the toxicant are required to
determine appropriate changes in model parameters. We have assumed
that organisms respond to all chemicals according to a general stress
syndrome. For example, they increase respiration rates, decrease
photosynthetic and grazing rates, and become more susceptible to
predation. This assumption permits us to define percent changes in
model parameters which result in 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 in O'Neill et al. (1982).
The data used to implement the EUA are shown in Table 3.3-1.
Estimates of risk can be made for nine RACs. These RACs were the only
chemical groups for which adequate data seem to exist.
3.3.2 Results of Ecosystem Uncertainty Analysis
Results of the EUA are given in Table 3.3-2. 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
-------
Table 3.3-1. Values of
(mg'L"1) used to calculate E matrix for SWACOM (Appendix E)
Trophic
level
Algae
Zooplankton
Forage fish
Game fish
Model
Species
1-3
4-7
8-10
11
12
13
14
15
16
17
18
19
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
Qu incline0
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 d
258.0
20.0
95.0
300.0
36.4
58.1
157.0
14.0
36.0
16.4
34.9
9.0
Arsenic6
2.32
2.32
2.32
4.47
5.28
1.35
2.49
0.51
15.6
41.8
26.0
13.3
Nickelf
0.50
0.50
0.50
9.67
0.85
1.93
4.91
0.15
4.87
5.27
4.45
0.05
Cadmi urn9
0.16
0.06
0.06
0.5
0.0099
0.14
0.25
0.0035
0.63
1.94
1.63
0.002
Leadh
0.50
0.50
0.50
40.8
0.45
27.4
14.0
0.67
4.61
23.8
31.5
1.17
Mercury1
0.01
0.01
0.01
0.78
0.005
0.53
0.27
0.01
0.15
0.24
0.50
0.25
Values taken from the following water quality criteria documents:
EPA 440/5-80-018 (USEPA 1980c).
bEPA 440/5-80-059 (USEPA 1980e).
cO'Neill et al. (1982).
dEPA 440/5-80-066 (USEPA 1980g).
eEPA 440/5-80-021 (USEPA 19801).
TEPA 440/5-80-060 (USEPA 1980n).
9EPA 440/5-80-025 (USEPA 1980o).
hEPA 440/5-80-057 (USEPA 1980p).
^PA 440/5-80-058 (USEPA 1980m).
o
•yo
i
10
o
-------
ORNL/TM-9070
28
Table 3.3-2.Risks associated with nine risk assessment
units, as estimated by ecosystem uncertainty
analysis [Values based on the 95th percentile
concentration for the eastern site
(6.72 x 10-4 mg-L-')]
RAC
number
12
14
17
21
31
32
33
34
35
Chemical
Benzene
Naphthalene
Qu incline
Phenol
Arsenic
Mercury
Nickel
Cadmium
Lead
Fourfold increase
in Blue-green
algae bloom
0.088
0.092
0.086
0.086
0.088
0.424
0.178
0.544
0.110
25% reduction
in Game fish
biorp-^s
0.038
0.040
0.040
0.038
0.040
0.350
0.054
0.972
0.042
-------
29 ORNL/TM-9070
chosen as indicative of minimal effects that could be detected in the
field. Results are shown for the upper 95th percentile concentration
for the eastern site, that is, the highest concentration of interest in
the study. Because the estimated contaminant concentrations in the
western river were similar to those in the eastern river, a separate
analysis using the western scenario was not necessary.
None of the risk values is exactly zero, because there is a
minimal risk of an increase in algae (0.086) or a decrease in fish
(0.038) even though the environmental concentration of the toxicants is
zero. This reflects residual uncertainty in simulating ecosystem
behavior. For example, there is always some probability of a small
decrease in fish due to environmental variability.
Considering this residual uncertainty, the risks calculated by EUA
are very small for most of the chemicals. A unit release of phenol
represents no risk over and above the uncertainty from environmental
variability. The additional risks involved in a unit release of
benzene, naphthalene, quinoline, and arsenic are also minimal.
The EUA does forecast significant risks for both endpoints
associated with two of the RACs: cadmium and mercury. It also
projects small risks associated with lead and nickel. The risk values
associated with cadmium and mercury are high even at the minimal
concentrations involved in the unit release calculations.
3.3.3 Patterns of Sensitivity Across Populations
No two species show identical sensitivities, and the way the
sensitivities (i.e., LC50's) are distributed can influence the
response of the ecosystem. For illustrative purposes, we concentrated
on six of the chemicals in table 3.3-1, excluding nickel, benzene, and
quinoline. The distribution of sensitivities in the table will be
referred to as the "population" pattern. To remove differences among
populations in the same trophic level, the standard approach would be
to take the geometric mean of the LC50's. However, the data were not
measured for the same period of time, and some of the values were
ECr0's and EC2Q's. We assumed a simple mortality process described
by x(t) = x(0) exp(-d t), where x(0) is the initial population size,
-------
ORNL/TM-9070 30
x(t) is the size at time t, and d is the mortality rate. We assume
that mortality is a function of concentration, d = aC. We know the
fraction, F, = x(t)/x(0), that survive at one concentration, C-j,
measured over one time period, t-j. Since (In F-|)(/C-| t-|) = -a
= (In F2)/(C2 to), we can then estimate the concentration, Co,
that would result in a different fraction, F2, measured over a
different time period, to. By simple rearrangement we find
Co = (C1 t] In F2)/(t2 In F^ . (3)
Using Eq. (3), and taking geometric means, we arrive at an LC5Q
for each trophic level (Table 3.3-3). This distribution will be
referred to as the "trophic" pattern. We apply this approach once again
to arrive at a single IC™ value that removes even the trophic
pattern. This value is shown in the last line of Table 3.3-3 and will
be referred to as "no pattern."
The upper half of Table 3.3-4 shows the percent difference in
annual biomass for each trophic level, comparing the trophic pattern to
the no-pattern case. For phenol, the game fish is more sensitive than
the no-pattern LC5Q. The other trophic levels are relatively
insensitive. Therefore, the toxicant reduces game fish and has little
direct effect on the other organisms. However, because game fish are
reduced, the forage fish experience less predation and show a slight
increase. Because there are more forage fish, there are fewer
zooplankton. Because there is less grazing, phytoplankton increases.
As a result of trophic interactions, the zooplankton, which have the
lowest sensitivity, have as great a decrease as the game fish. The
same type of pattern is seen with cadmium; however, the game fish is
now ten times more sensitive and the effect is magnified.
With naphthalene and mercury, the LC5Q of the zooplankton is
close to the no-pattern concentration. As a result, there are direct
effects on both game fish and zooplankton. The forage fish, relatively
insensitive to the toxicant, are also decreased because of reductions
in their food supply.
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31 ORNL/TM-9070
Table 3.3-3. Trophic patterns in sensitivity. Values are geometric means of the
values in Table 3.3-1, after those values were modified by means of
Eq. (3) in the text. The last line in the table gives "the geometric
mean across trophic levels, once again modified by Eq. (3).
Phenol Naphthalene Cadmium Mercury Arsenic Lead
Phytoplankton
Zooplankton
Forage fish
Game fish
No-pattern
26
67
27
9
18
33
5.6
43
2
4.7
0.050
0.057
1.2
0.002
0.025
0.0084
0.089
0.36
0.25
0.054
2.3
2,1
26.0
13.0
2.6
0.5
5.4
15.0
1.2
1.0
-------
ORNL/TM-9070 32
Table 3.3-4, Comparison of responses to different patterns of sensitivity. The
upper portion gives percent differences in average annual biomass,
comparing the trophic pattern to the no-pattern case (Table 3.3-3).
The lower portion compares the trophic pattern (Table 3.3-3) to the
full population pattern (Table 3.3-1).
Phenol Naphthalene Cadmium Mercury Arsenic Lead
Phytoplankton
Zooplankton
Forage fish
Game fish
Phytoplankton
Zooplankton
Forage fish
Game fish
0.14
-1.0
1.0
-1.0
6.0
-6.0
-8.0
-6.0
Trophic
9.0
-7.0
-2.0
-6.0
Population
6.0
-5.0
-6.0
-5.0
vs no pattern
19.0
-19.0
25.0
-33.0
vs trophic
1.0
-6.0
-4.0
-4.0
2.0
-4.0
-4.0
-0.47
pattern
11.0
-6.0
-5.0
-3.0
-0.02
-1.0
2.0
5.0
1.0
-1.0
-4.0
-3.0
0.36
-0.49
3.0
6.0
10.0
-10.0
-10.0
-10.0
-------
33 ORNL/TM-9070
The phytoplankton and zooplankton both show LC5Q's that are
close to the no-pattern concentration for arsenic. Therefore, they are
directly affected and their populations decrease. However, the
reductions occur during the spring blooms. Because nutrients are not
exhausted during this period, as they usually are, plankton survive
during the remainder of the year. The result is a lower average size
for the plankton, but higher plankton concentrations during the period
of maximum growth of the fish populations. Therefore, fish show a
slight increase in response to arsenic.
A similar phenomenon occurs with lead. Here the phytoplankton
populations are the most sensitive. Therefore, their spring peak is
decreased, cutting off the food supply to the zooplankton. The
resulting decrease in the zooplankton permits the phytoplankton to
increase slightly during the remainder of the year. The
counter-intuitive result is that the most sensitive trophic level,
phytoplankton, actually shows a slight increase in its annual average
population size.
It is clear from Table 3.3-4 that the pattern of sensitivities
across the trophic levels alters the response of the ecosystem. Our
use of geometric means and Eq. (3) guarantees that all chemicals have
exactly the same effect in the absence of pattern. Therefore, the
percent differences truly reflect the effect of trophic pattern. In
some cases (e.g., phenol), the effect of pattern is small, causing
deviations from the no-pattern case of 1% or less. In other cases,
(e.g., cadmium) the effects are large, causing differences as large as
33%. What is very clear is that ignoring the effect of trophic
patterns can lead to significant errors.
The next step is to compare the trophic and population patterns
(Table 3.3-4). The percent differences are shown in the lower portion
of Table 3.3-4. The results show that a consistent bias is introduced
by ignoring population patterns. For all chemicals, the average
phytoplankton biomass is larger and the consumer trophic levels are
always smaller.
-------
ORNL/TM-9070 34
One of the purposes of the unit release calculations was to rank
the relative risk associated with the RACs. The rankings for the nine
chemical groups according to EUA are given in Table 3.3-6. The table
compares this ranking with the ranking resulting from normalized
LCc.'s. These normalized values are calculated by adjusting all
oU
LCcn values used in SWACOM to the same endpoint (50% mortality in
bU
7 d) and taking a geometric mean across populations and trophic levels.
The method is explained in more detail in Appendix E. The normalized
LCr-n seems to be a reasonable estimator. This indicates that such a
oU
normalized value might be of use in determining the relative risks of
different chemicals, especially when the toxicological data are
insufficient to permit application of EUA.
3.3.4 Population Sensitivity Patterns and Risk
In a final set of studies, we examined the effect of population
patterns on risk. We performed the analysis for phenol at a reasonable
environmental concentration of 0.178 mg/L (Barnthouse et al. 1982).
The first three rows of Table 3.3-5 compare the three patterns. There
are only small differences between the no-pattern and trophic cases.
However, the bias in ignoring population patterns has a large effect:
the risk of a blue-green algal bloom has doubled, and the risk of 25%
reduction in game fish has almost tripled. The indications are that it
is important to include the variability in sensitivity to a chemical
within a trophic level. Ignoring this pattern would underestimate risk
by a factor of %2.
Rows 4 and 5 in Table 3.3-5 compare the risk when all populations
in a trophic level are set to the sensitivity of the most sensitive or
least sensitive species. Setting all populations to the least sensitive
species produces risks that are only slightly below the no-pattern
case. Setting all populations to the most sensitive produces results
only slightly higher than the population pattern. Synergistic effects
can influence production as though all populations were as sensitive as
the most sensitive species.
-------
35 ORNL/TM-9070
Table 3.3-5. Risk associated with a fourfold increase in noxious blue-green
algae blooms and 25% reduction in average annual biomass of game
fish. The table compares the risks, expressed as percentages,
resulting from different simulation experiments described in the
text.
Fourfold increase 25% reduction
in blue-green algae in game fish
No pattern 14.4 7.2
Trophic pattern 15.0 7.6
Standard pattern 34.8 20.6
All populations set at:
Most sensitive 36.8 30.6
Least sensitive 10.2 6.4
Population sensitivities rearranged to:
Least sensitive in spring 35.8 22.6
Most sensitive in spring 10.0 6.2
-------
ORNL/TM-9070
36
Table 3.3-6. Ranking of nine chemicals according to their
calculated risk effect (Table 3.3-2) and
their LC^Q'S (£650*s) normalized across
population and trophic levels
Cadmium
Mercury
Lead
Nickel
Arsenic
Naphthalene
Quinoline
Phenol
Benzene
Normalized
LC50
0.025
0.054
1.041
1.250
2.616
4.683
7.019
17.800
31.080
Ranking of
risk
1
2
4
3
6
5
7
9
8
-------
37 ORNL/TM-9070
The final two cases use the population pattern, but within each
trophic level, the sensitivities are temporally reassigned. In the
first case, the most sensitive species occurs in the spring and the
least sensitive in the summer. In the second case, the order is
reversed. Rearranging the sensitivites causes approximately the same
range of results as assigning all species to the lowest or highest
sensitivities. The seasonal arrangement of sensitivities is about
equal in importance to the actual magnitude of the sensitivities. This
indicates once again the importance of population patterns of
sensitivity.
3.3.5 Importance of Patterns of Sensitivity
The results indicate that synergistic effects are important. Toxic
stress will interact with other constraints in the ecosystem, causing
the greatest effect when natural environmental stress is greatest.
Different responses during the year are likely to be related to those
components of the system that are undergoing their greatest growth. It
is particularly important to recognize that ignoring differences in
sensitivities among populations can cause a significant bias.
Because the real benefit in applying EUA lies in its ability to
detect higher-order effects, it is clear that EUA is most usefully
applied when sufficient data exist to quantify the population type of
pattern; i.e., multiple toxicity values should be available for each
(or most) of the trophic levels. Without this information, many of the
synergistic mechanisms in the ecosystem will not be represented, and
higher-order effects predicted by the model may be strongly biased.
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ORNL/TM-9070
4. TERRESTRIAL ENDPOINTS
The quotient method, as discussed in Barnthouse et al. (1982),
consists of deriving the quotients of ambient concentrations of
toxicants divided by toxicological benchmark concentrations. It is
used in this section to provide an indication of the inherent
toxicities of the RACs. The other risk analysis methods are not
readily applicable to terrestrial organisms because of the much smaller
toxicological data base for effects of most RACs on forests, crops, and
wildlife, the lack of standard tests and toxicological benchmarks in
the existing data base, and even the lack of agreed-upon standard
responses for terrestrial biota. Because meteorological differences
between the sites do not change the ranking of the RACs, only results
for the eastern site will be presented.
4.1 VEGETATION
The phytotoxicity data for the gaseous and volatile RACs are
presented in Table B-l, the concentrations in ambient ground-level air
are in Table 2.2-1, and the quotients of the ratios of these values are
in Table 4.1-1. The ambient concentrations are the increment of the
entire RAC to the background concentration at the point of maximum
ground-level concentration (Sect. 2.2). It is assumed that the RAC is
composed entirely of the representative chemical, and that the
background concentration is zero. Quotients are 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 yields.
Of the 15 RACs for which data on toxicity in air were found, the
worst atmospheric toxicant in the unit release is RAC 6 (hydrocarbon
gases). 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,
38
-------
Table 4.1-1.
Toxlclty quotients for terrestrial plants. Ambient concentrations of RACs 1n air (annual, median, ground-level) and 1n soil (soil solution or
whole dry soil) are divided by concentrations causing reductions 1n growth, yield, or other toxic responses3
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
Phytotoxicity In air
Phytotoxicity In soil or soil solution
Ambient concentration/ Range of ambient concentration/ Soil concentration/
lowest toxic concentration growth-effects concentration lowest toxic concentration
RAC Name Eastern Western Eastern
Carbon monoxide 3.65 E-02 5.18 E-02 5.97 E-06
Sulfur oxides 2.06 E-03 5.09 E-03 3.44 E-04-
1.03 E-03
Nitrogen oxides 5.33 E-04 1.25 E-03 2.80 E-05-
5.33 E-04
Add gases 2.33 E-01 3.3 E-01 2.33 E-01
Alkaline gases 4.68 E-03 7.33 E-03
Hydrocarbon gases 5.51 E-01 7.67 E-01 2.65 E-02-
9.26 E-02
Formaldehyde 1.77 E-01 2.51 E-01
Volatile organochlorines 2.52 E-04 3.57 E-04
Volatile carboxylic acids
Volatile 0 & S heterocyclics c c c
Volatile N-neterocyclics
Benzene 2.25 E-03 3.21 E-03
Aliphatic/alicyclic hydrocarbons 2.34 E-ll 2.63 E-ll
Mono/diaromatic hydrocarbons 3.14 E-04 4.40 E-04
Polycyclic aromatic hydrocarbons
Aliphatic amines
Aromatic amines 1.88 E-01 2.36 E-01
Alkaline nitrogen heterocyclics c c c
Neutral N, 0, S heterocyclics
Carboxylic acids
Phenols
Aldehydes and ketones 2.22 E-01 3.14 E-01
Nonheterocyclic organosulfur 1.87 E-02 2.44 E-02 1.03 E-01
Alcohols
Nitroaromatics c c
Esters c c
Amides
Nitriles c c
Tars c c
Respirable particles c c
Arsenic
Mercury 3.36 E-02 5.84 E-02
Nickel
Cadmium
Lead
Western
8.48 E-06
8.49 E-04-
2.55 E-03
6.58 E-05-
1.25 E-03
3.3 E-01
3.69 E-02-
1.29 E-01
2
c
5
1
6
6
1
c
4
5
9
1
1.34 E-01 1
1
c
c
3
c
c
c
5
5
3
1
Eastern
b
b
t
b
b
.85
E-03
3
c
.38
.77
.85
.33
.89
E-03
E-03
E-05
E+02d
E-01
7
1
9
8
2
c
.75
.03
.65
.84
.44
.27
c
c
.34
c
c
c
.23
.36
.16
.81
E-06
E-01d
E-03
E-03
E-04d
E-06
A
E-07d
,1
E+02d
E-03
E+01d
E+02
1.1 E+OOd
8
7
8
2
1
1
4
6
4
3
1
1
Western
b
b
b
b
b
.7 E-03
c
.42 E-03
.98 E-03
.55 E-05
.76 E+02d
.54 E-01
c
.02 E-06
.0 E-01d
.25 E-03
.6 E-03
.86 E-04d
.68 E-06
A
.59 E-07
fl
.03 E+02
.05 E-03
.42 E+01d
.12 E+02
.5 E+02d
Range of soil concentration/
growth-effects concentration
Eastern
2
5
6
1
6
4
2
5
1
3
2
4
3
3
4
9
b
b
b
b
b
.85 E-03
c
.38 E-03
85 E-05
95 E+02-
33 E+02d
c
75 E-07-
4.75 E-06
68 E-02-
03 E-01d
84 E-03
34 E-07d
45 E+01- rf
5.23 E+02°
92 E-05-
5.36 E-03
74 E-02-
16 E+01 d
02 E+00-
1.81 E+02
87 E-03-
1.1 E+OOd
Western
b
b
t
b
b
3.7 E-03
c
7.42 E-03
9.55 E-05
2.7 E+02-
8.76 E+02d
c
8.02 E-07-
8.02 E-06
3.72 E-02-
7.0 E-01d
2.6 E-03
A
4.59 E-07
2.83 E+01-.
6.03 E+02°
3.72 E-05-
4.05 E-03
4.06 E-02-
3.42 E+01d
2.48 E+00-
1.12 E+02
1.34 E-02-
1.5 E+OOd
aAmbient air concentrations, soil and soil solution concentrations are presented in Table 2.2-1. Toxic concentrations are presented in Appendix B.
^No accumulation in soil.
cNo phytotoxicity data.
^Quotients 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.
co
O
73
I
VD
O
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ORNL/TM-9070 40
since atmospheric ethylene has caused significant damage to crops near
urban areas and in the vicinity of petrochemical plants (National
Research Council 1976), the emission rate of this gas should be
specifically considered in the future. The five most phytotoxic RACs
in air (ignoring ethylene) are mercury (32), acid gases (4), aldehydes
and ketones (22), aromatic amines (17), and formaldehyde (7). Although
some phytotoxicity data were found for 15 RACs, data on growth-related
effects are available for only 6 RACs. Of these six, acid gases and
nonheterocyclic organosulfur were the highest ranking. These ranks
result from differential dispersion as well as differential toxicity.
In particular, the relatively low ranking of sulfur oxides and nitrogen
oxides (RACs 2 and 3) is primarily due to their emission from the tall
boiler stack rather than from the short stack, from cooling towers, or
from area sources.
The phytotoxicity of materials deposited on the landscape is a
more complex phenomenon than that of gases and vapors. Deposited
nongaseous RACs were assumed to accumulate in the soil over the 35-year
life of the liquefaction plant. Loss due to decomposition and leaching
from this soil horizon was calculated by the terrestrial food chain
model (Sect. 2.2).
The toxicity data (Table B-3) were primarily derived from exposure
of plants or plant parts to solutions of the chemicals rather than
contaminated soil because few data are available on toxicity in soil.
While the results of tests conducted in soil can be directly compared
to concentrations in whole soil, results of tests conducted in solution
must be compared to 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, these numbers are less reliable. In addition, as with the
gases and vapors, the toxicity data come 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 whether the chemicals used are representative of the entire
RAC.
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41 ORNL/TM-9070
No data were found for the phytotoxicity in root exposures of RACs
6, 7, 8, 10, 12, 17, 18, 25, 26, and 28. 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 S04
deposition in forests with acid soils. The effects of S04 deposition
in forests result from regional-scale emissions and atmospheric
processes and are therefore well beyond the scope of this report. The
most phytotoxic RACs deposited in soil are polycyclic aromatic
hydrocarbons (RAC 15), cadmium (34), arsenic (31), nickel (33), and
lead (35). The high rank of RAC 15 is suspect because benzo(a)pyrene
and other polycyclic aromatic hydrocarbons (PAHs) appear to act as
plant hormones, stimulating growth at low concentrations. Although
PAHs can modify plant growth at concentrations as low as 0.5 ng/g soil,
it does not appear likely that their presence in synfuel emissions
would reduce plant yields. Thus, heavy metals appear to be the most
serious phytotoxicants in soil, and methods for predicting their
effects require attention.
4.2 WILDLIFE
Table 4.2-1 presents the lowest toxicity quotients for the two
sites for terrestrial animals. The quotients were 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 median ground-level concentration in air derived from
unit releases of all RACs (Sect. 2.2). Carcinogenesis and other
genotoxic effects are not included. Data from all species are lumped
because there were not enough data on the nonmammalian taxa for
separate treatment. Data on the avian toxicity of industrial chemicals
are virtually nonexistent. Yet the responses of birds are likely to be
considerably different from those of mammals for the following
reasons: (1) the complex respiratory systems of birds with both lungs
and air sacs must modify the rate and pattern of deposition, (2) birds
possess lower levels of mixed-function oxidases, epoxide hydrolases,
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ORNL/TM-9070
42
Table 4.2-1. Toxicity quotients for terrestrial animals. Annual median ground-level
concentrations in air are divided by lethal concentrations and the lowest toxic
concentrations.3
Lowest lethal concentration
Lowest toxic concentration
RAC
RAC name
Eastern
Western
Eastern
western
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 N-heterocyclics
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
7.14 E-08
7.44 E-06
4.87 E-06
3.10 E-04
1.40 E-05
7.67 E-05
4.67 E-04
3.19 E-07
2.22 E-06
3.42 E-07
3.55 E-04
2.85 E-07
3.93 E-05
b
1.03 E-05
6.86 E-05
b
b
b
b
3.09 E-03
3.36 E-04
4,79 E-05
b
3.78 E-06
b
5.83 E-05
b
2.0 E-04
8.38 E-04
1.01 E-07
1.84 E-05
1.14 E-05
4.40 E-04
2.2 E-05
1.09 E-04
6.62 E-04
5.36 E-07
3.16 E-06
5.76 E-07
5.07 E-04
3.21 E-07
5.52 E-05
b
1.46 E-05
8.62 E-05
b
b
b
b
4.37 E-03
4.39 E-04
6.82 E-05
b
5.36 E-06
b
8.28 E-05
b
2.84 E-04
1.41 E-03
1.53 E-03
1.34 E-03
1.19 E-04
9.31 E-04
7.55 E-04
1.71 E-07
1.21 E-02
1.33 E-03
2.97 E-05
2.22 E-06
3.42 E-07
3.55 E-04
1.87 E-05
7.47 E-04
b
3.14 E-04
6.86 E-05
b
b
b
b
1.09 E-01
5.04 E-03
8.31 E-04
b
5.91 E-05
b
2.37 E-04
b
1.43 E-01
1.74 E-01
1.98 E-03
2.0 E-04
4.19 E-01
8.16 E-03
2.17 E-03
3.31 E-03
2.80 E-04
1.32 E-03
1.18 E-03
2.38 E-07
1.72 E-01
1.89 E-03
5.01 E-05
3.16 E-06
5.76 E-07
5.07 E-04
2.11 E-05
1.05 E-03
b
4.46 E-04
8.62 E-05
b
b
b
b
1.54 E-01
6.59 E-03
1.18 E-03
b
8.38 E-05
b
3.37 E-04
b
2.04 E-01
2.93 E-01
3.44 E-03
2.84 E-04
7.06 E-01
1.42 E-02
aAmbient air concentrations are presented in Table 2.2-1. Toxic concentrations are
presented in Appendix B.
^No data on respiratory toxicity.
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43 ORNL/TM-9070
and glucuronyl transferases (detoxification enzymes) than mammals
(Walker 1980); (3) birds are generally less protected by deposition of
chemicals in air on vegetation and other surfaces; (4) both primary and
secondary predation are more common among birds; and (5) oviparous
reproduction by birds makes data on mammalian reproductive effects
largely irrelevant. The data base is even smaller for reptiles,
amphibians, and terrestrial invertebrates.
Lethality was considered because it is a consistent and frequently
determined response that has clear population implications. The most
lethal RACs in a unit release are (in decreasing rank order) aldehydes
and ketones (RAC 22), cadmium (34), volatile organochlorines (8), lead
(35), nonheterocyclic organosulfur (23), acid gases (4),nickel (33),
and formaldehyde (7). Most of the lethality data is derived from
laboratory rodents. 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 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 functions in human occupational exposures.
The most toxic RACs by this sublethal criterion are cadmium (RAC 34),
arsenic (31), respirable particles (30), formaldehyde (7), and
aldehydes and ketones (22).
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ORNL/TM-9070
5. EVALUATION OF RISKS
5.1 EVALUATION OF RISKS TO FISH
Because the toxicological data base is larger for fish than for
any other aquatic biota, a variety of comparisons were possible for
this endpoint. The relative risks of the RACs were compared, using the
quotient method, analysis of extrapolation error, and ecosystem
uncertainty analysis. In addition, differences in sensitivity among
fish species and differences in vulnerability of the fish communities
in the eastern and western reference rivers were considered.
5.1.1 Differences Among RACs
Table 5.1-1 shows the ranking of 21 RACs for which risks could be
estimated using both the quotient method (QM) and analysis of
extrapolation error (AEE) for the eastern reference site. For QM, the
RACs were ranked from highest to lowest based on either acute or
chronic toxicity, whichever was highest (Table 3.1-1). To obtain
rankings for AEE, geometric means of the risk estimates in Table 3.2-1
were calculated across species for each RAC. Although not identical,
the two rankings are highly correlated. Three of the top five RACs are
the same on both lists: acid gases (RAC 4), mercury (32), and cadmium
(34). Esters (RAC 26) also ranked relatively high: fourth according to
QM and sixth according to AEE.
Table 5.1-2 presents rankings according to QM, AEE, and ecosystem
uncertainty analysis (EUA) for the nine RACs to which EUA could be
applied. Again, the rankings are similar, especially for the highest
RACs. Although the top four RACs according to EUA are heavy metals,
many of the most toxic RACs could not be considered because of
insufficient toxicological data. Given the good correlations among the
three methods for those RACs that could be examined using EUA, it is
conceivable that, had sufficient data been available for acid gases and
esters, significant risks of reductions in fish populations would have
been obtained from EUA.
44
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45
ORNL/TM-9070
Table 5.1-1. Rankings of Risk Analysis Categories (RACs), according to the
quotient method (QM) and analysis of extrapolation error (AEE), in
order of decreasing risk to fish
RAU (representative compound)
Rank
QM
AEEa
1 32 (mercury)
2 34 (cadmium)
3 4 (acid gases)
4 26 (esters)
5 35 (lead)
6 15 (polycyclic aromatic hydrocarbons)
7 22 (aldehydes and ketones)
8 5 (alkaline gases)
9 33 (nickel)
10 14 (mono- or diaromatics)
11 18 (alkaline N-heterocyclics)
12 8 (volatile organochlorines)
13 21 (phenols)
14 12 (benzene)
15 28 (nitriles)
16 21 (arsenic)
17 13 (aliphatic/alicyclic hydrocarbons)
18 7 (formaldehyde)
19 9 (volatile carboxylic acids
20 20 (carboxylic acids)
21 6 (hydrocarbon gases)
4 (acid gases)
34 (cadmium)
22 (aldehydes and ketones)
32 (mercury)
5 (alkaline gases)
26 (esters)
14 (mono- or diaromatics)
15 (polycyclic aromatic hydrocarbons)
13 (aliphatic/alicyclic hydrocarbons)
35 (lead)
12 (benzene)
28 (nitriles)
18 (alkaline N-heterocyclics)b
21 (phenols)6
33 (nickel)
31 (arsenic)
9 (volatile carboxylic acids)
8 (volatile organochlorines)
20 (carboxylic acids)
6 (hydrocarbon gases)
formaldehyde (RAC 7) could not be evaluated by AEE, since only 24-h
were available.
bTied RACs.
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ORNL/TM-9070
46
Table 5.1-2. Rankings of nine Risk Analysis Categories (RACs), according to the quotient method
(QM), analysis of extrapolation error (AEE), and ecosystem uncertainty analysis
(EDA), in order of decreasing risk to fish
RAD (representative compound)
Rank
QM
AEE
EUA
1 32 (mercury)
2 34 (cadmium)
3 35 (lead)
4 33 (nickel)
5 14 (mono- or diaromatics)
6 18 (alkaline N heterocyclics)
7 21 (phenols)
8 12 (benzene)
9 31 (arsenic)
34 (cadmium)
32 (mercury)
14 (mono- or diaromatics)
35 (lead)
12 (benzene)
18 (alkaline N heterocyclics);
21 (phenols)
33 (nickel)
31 (arsenic)
34 (cadmium)
32 (mercury)
33 (nickel)
35 (lead)
14 (mono- or diaromatics)3
31 (arsenic)3
18 (alkaline N heterocyclics)3
12 (benzene )b
21 (phenols)6
aTied observations.
''Tied observations.
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47 ORNL/TM-9070
5.1.2 Differences in Sensitivity Among Fish Species
Table D-l shows that there are substantial differences among fish
species with respect to sensitivity to the various RACs. Several
species, notably the black crappie, rainbow trout, and brook trout,
appear to be unusually sensitive to a wide range of toxic chemicals,
based on current information. The carp and buffalo appear unusually
insensitive. For most contaminants, PGMATCs for different fish species
range over two orders of magnitude. Table 3.2-1 demonstrates the
importance of considering the uncertainty associated with estimates of
PGMATCs or other toxicological benchmarks. Estimated PGMATCs for
nearly all species-RAC combinations are 10 or more times higher than
the estimated ambient contaminant concentrations. Nonetheless, there
are five RACs for which there is a 5% or greater risk that the ambient
concentration in the eastern river may exceed the PGMATC for one or
more species. For the western river, four RACs have a 5% or greater
risk of exceeding one or more PGMATCs.
The model experiments described in Sects. 3.3.3 and 3.3.4 show
that differences in sensitivity among ecologically similar populations
can markedly increase or decrease the ultimate effects of a given
contaminant concentration. It was found, for the particular
parameterization of the Standard Water Column Model used in this
analysis, that the responses of the model ecosystem assuming a range of
sensitivites to contaminants for the populations within each trophic
level were similar to the responses obtained when all populations were
assumed to be as sensitive as the most sensitive species (Table 3.3-5).
Although different model parameters might produce different results, it
is clear that uncertainty about the relative sensitivities of different
populations introduces substantial uncertainties into estimates of
risks of higher-order ecological effects.
5.1.3 Differences in Risk Between Sites
Under the scenarios used in the unit release analysis, there are
few differences in the ecological risks of the various RACs between the
eastern and western sites. There are significant between-site
differences in half-life for many of the RACs (Table 2.1-5) due to
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ORNL/TM-9070 48
differences in sediment load, depth, and current velocity. However, in
the near field, dilution is the primary determinant of contaminant
concentration, and stream flows in the two rivers are similar.
AEE shows, however, that there are differences in potential
ecological risks to fish, due to differences in the sensitivities of
the fish species in the two rivers. The two trout species in the
western river are relatively sensitive to cadmium compared to the
species in the eastern river. In addition, they are among the most
sensitive species to several other highly toxic contaminants, notably
methyl mercury and hydrogen sulfide (Table 3.2-1).
5.2 EVALUATION OF RISKS OF ALGAL BLOOMS
Fewer conclusions are possible for algae than for fish, in part
because of the relative scarcity of data on the effects of
synfuels-derived contaminants on algae. Equally important, however, is
the lack of standardization of test systems for algae. The test results
summarized in Table 3.1-3 reflect more than a dozen combinations of
toxicological responses and test durations. Consequently, QM could not
be used to develop a meaningful ranking of the RACs. For the same
reasons, interspecies differences in sensitivity and intersite
differences in vulnerability could not be considered.
Although the use of EUA analysis to determine effects on
phytoplankton was also limited because of insufficient data, the
problem of test incomparability was partly remedied through use of the
microcosm simulations (Appendix E) and normalization procedures
(Sect. 3.3.3). These procedures made possible approximate comparisons
between results of tests performed using different toxicological
endpoints.
Estimates of the risks of fourfold increases in algal biomass
resulting from unit releases of nine RACs are presented in Table 3.3-2.
The results for algae are similar to those obtained for fish in that
the same four RACs produced the greatest risks to both fish and algae.
This similarlity cannot be explained on the basis of the relative
toxicity of the nine RACs to algae. The RACs most toxic to algae
(viz., cadmium, mercury, lead, and nickel) produced the greatest
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49 ORNL/TM-9070
increases in algal biomass. The explanation for this observed
counterintuitive response is that these highly toxic chemicals produce
reductions in grazing intensity (due to decreased zooplankton
abundance) that more than offset the toxic effects of the contaminants
on algae.
5.2.1 Comparison of Uncertainties Concerning Exposure
Concentrations and Effects Concentrations
A revealing comparison is possible between the magnitudes of
uncertainty concerning (1) the expected environmental concentrations of
contaminants in the vicinity of a synfuels plant and (2) the
predicted-effects thresholds for fish. The distributions of estimated
contaminant concentration in Table 2.1-4 are approximately lognormal.
The variances of the corresponding log-transformed normal distributions
range from 0.21 (anthracene, eastern site) to 0.42 (all contaminants,
western site). These variances are 10 to 100 times lower than the
error variances associated with the log-transformed PGMATCs described
in Sect. 3.2.
Because the exposure analyses included only uncertainty about the
values of environmental parameters, the results undoubtedly
underestimate the true uncertainty for contaminant concentrations.
Although we cannot directly estimate the effects on contaminant
concentrations of uncertainties for volatilization, biological
degradation, complexation, hydrolysis, or other removal processes, we
can indirectly estimate the magnitude of uncertainty for the total
removal rate that would be necessary to produce an uncertainty
concerning steady-state contaminant concentrations equivalent to the
calculated uncertainty of PGMATCs.
The total removal rate cannot be smaller than zero. Therefore,
the 95% concentrations for the "dilution only" cases in Table 2.1-4 can
be reasonably assumed to be upper bounds on contaminant concentrations
no matter how high or low the contaminant removal rates are. Using the
eastern reference river as an example, the upper 95% limit on
contaminant concentrations for a release rate of 4.12 E-02 g/s is
6.7 E-07 g/L. A lognormal distribution of concentrations with a
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ORNL/TM-9070 50
log-transformed variance of 3.7 (the median value of variances of
log-transformed PGMATCs for the 120 taxon-RAC combinations used in this
report) and a upper bound fixed by dilution would have a median of 2.7
E-08 g/L and a lower 5% concentration of 1.2 E-09 g/L. The removal
rate needed to produce a steady-state concentration this low can be
calculated by rearranging Eq. (2-17) of Travis et al. (1983) to obtain
k - J " QC (4)
kt VC ' { '
where
k = combined first-order rate for all removal processes (L/s),
U
I = contaminant release rate (kg/s),
o
Q = stream flow rate (m /s),
V = reach volume (m ),
3
C = contaminant concentration (kg/m ).
The lowest contaminant concentrations are expected to occur when stream
flows, and consequently reach volume are high; therefore, for this
•3 3
example, we use the upper 95% values of Q (251 m /s) and V (9.2 E+05 m ).
Substituting these values, the contaminant release rate (4.12 E -05 kg/s),
and the above contaminant concentration into Eq. (4), we calculate that a
total removal rate constant of 3.7 E -02/s is required to produce a
steady-state concentration of 1.2 E -09 g/L. This rate constant
corresponds to a contaminant half-life of 18.7 s.
Thus, a range of uncertainty for contaminant half-lives of from
^20 s to infinity would result in near-field exposure concentrations for
the eastern reference river that are as uncertain as the PGMATCs estimated
in Sect. 3.2. Note that the shortest half-lives calculated for the unit
release risk analysis (Table 2.1-5) are VIOO h. Uncertainty of the
required magnitude would be possible only in the case of extremely reactive
contaminants whose environmental chemistry is essentially unknown. It
seems safe to conclude that, for the majority of contaminants of interest
in synfuels risk analyses, uncertainty concerning toxicological effects is
far greater than is uncertainty concerning near-field environmental
concentrations.
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51 ORNL/TM-9070
5.3 EVALUATION OF RISKS TO VEGETATION AND WILDLIFE
The primary purpose of this analysis is to examine the availability
of information on the toxicity of the full set of RACs to terrestrial
plants and animals. Not surprisingly, more information was found on
respiratory toxicity to animals than on phytotoxicity. Respiratory
toxicity data was found for all but eight RACs in existing published
data compilations (Table B-3). The untested RACs are
high-molecular-weight organics to which livestock and wildlife are
unlikely to be exposed in significant quantities. The animal toxicity
data set, however, is complete only for mammals. In addition, dietary
toxicity was not considered for lack of appropriate toxicity data and
exposure models for that route of exposure. Recognizing these
limitations, the most serious threat to wildlife from unit releases
would be posed by aldehydes and ketones (RAC 22), cadmium (34), arsenic
(31), and respirable particles (30) (Table 4.2-1).
Information concerning phytotoxicity of gaseous RACs is relatively
abundant for crop species (Table B-l). Data on effects on plant growth
are available for all of the gaseous RACs except ammonia, which is more
likely to act as a fertilizer than as a toxicant. Of the gaseous RACs,
acid gases (primarily hLS) are the greatest threat to plant
production in the unit release analysis. The low quotients for SOY
/\
and NO are due to emission from a tall stack, so these RACs would
A
contribute an increment to regional problems with combustion gases.
The lack of data on responses of plants to atmospheric concentrations
of most heavy metals and organic chemicals is a reflection of their low
concentrations in air, even in heavily polluted areas.
All but nine of the nongaseous RACs have been tested for their
effects on plants exposed in soil or in hydroponic solution. Because
of the extreme variability of the physical, biological, and chemical
properties of soils, the uncertainties in modeling the availability of
chemicals in soil to plant roots, and the dependence of the soil model
on deposition rates from the atmospheric dispersion model, the exposure
assessment for plant roots is undoubtedly the most uncertain in this
ecological risk analysis. In addition, the validity of tests conducted
in solution culture as predictors of responses in field soils is
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ORNL/TM-9070 52
uncertain. Our analysis indicates that the worst soil phytotoxicants
are arsenic (RAC 31), cadmium (34), and nickel (33). The actual
toxicities will be highly dependent on soil chemistry including the
background concentration of metals.
5.4 VALIDATION NEEDS
There are no uniquely correct methods of quantifying ecological
risks. There are several plausible ways to combine uncertainties for
differential sensitivities of fish taxa and acute-chronic relationships.
There are also many aquatic ecosystem models. 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 ability 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,
in press) 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 hydroponic phytotoxicity studies nor of the risk analysis
methodology as a whole. The results of validation studies would not
only indicate the level of confidence that can be placed in
environmental risk analyses, but also would indicate what research is
necessary for further development and validation of risk analysis
methods.
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ORNL/TM-9070
6. ACKNOWLEDGMENTS
The authors thank G. A. Holton and F. R. O'Donnell for performing
the atmospheric dispersion and deposition calculations used in this
report. We also thank J. W. Webb and the members of the Environmental
Protection Agency's Peer Review Panel for their thorough reviews of
this report. Finally, we thank A. A. Moghissi and S. G. Hildebrand for
their support and encouragement during this project.
53
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ORNL/TM-9070
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Washington, D.C.
U.S. Environmental Protection Agency. 1980d. Ambient Water Quality
Criteria for Toluene. EPA 440/5-80-075. Office of Water
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.
U.S. Environmental Protection Agency. 1980f. Ambient Water Quality
Criteria for Fluoranthene. EPA 440/5-80-049. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980g. Ambient Water Quality
Criteria for Phenol. EPA 440/5-80-066. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980h. Ambient Water Quality
Criteria for 2,4-Dimethylphenol. EPA 440/5-80-044. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980i. Ambient Water Quality
Criteria for Acrolein. EPA 440/5-80-016. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
-------
65 ORNL/TM-9070
U.S. Environmental Protection Agency. 1980J. Ambient Water Quality
Criteria for Phthalate Esters. EPA 440/5-80-067. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980k. Ambient Water Quality
Criteria for Acrylonitrile. EPA 440/5-80-017. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 19801. Ambient Water Quality
Criteria for Arsenic. EPA 440/5-80-021. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980m. Ambient Water Quality
Criteria for Mercury. EPA 440/5-80-058. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980n. Ambient Water Quality
Criteria for Nickel. EPA 440/5-80-060. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980o. Ambient Water Quality
Criteria for Cadmium. EPA 440/5-80-025. Office of Water
Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
U.S. Environmental Protection Agency. 1980p. Ambient Water Quality
Criteria for Lead. EPA 440/5-80-057. Office of Water Regulations
and Standards, Criteria and Standards Division, Washington, D.C.
U.S. Environmental Protection Agency. 1982. Air Quality Criteria for
Particulate Matter and Sulfur Oxides. EPA-600/8-82-029c.
Environmental Criteria and Assessment Office, Research Triangle
Park, N.C.
U.S. Geological Survey. 1977. Water Resources Data for Kentucky
WY-1976. US6S Water Data Report KY 76-1. Water Resources
Division, Louisville, Kentucky.
-------
ORNL/TM-9070 66
U.S. Geological Survey. 1979. Water Resources Data for Pennsylvania
WY-1978. Vol. 3. Ohio River and St. Lawrence River Basins. USGS
Water Data Report PA-78-3.Water Resources Division, Harrisburg,
Pennsylvannia.
U.S. Geological Survey. 1980. Water Resources Data for Colorado
WY-1979. Vol. 2. Colorado River Basin Above Dolores River. USGS
Water Data Report CO-79-2. Water Resources Division, Lakewood,
Colorado.
Vergnano, 0., and J. G. Hunter. 1953. Nickel and cobalt toxicities in
oat plants. Ann. Bot. 17:317-328.
Verschueren, K. 1977. Handbook of Environmental Data on Organic
Chemicals. Van Nostrand Reinhold Co., New York.
Wakabayashi, M., B. G. Bang, and F. B. Bang. 1977. Mucociliary
transport in chickens infected with newcastle disease virus and
exposed to sulfur dioxide. Arch. Environ. Health 32:101-108.
Waldron, L. J., and N. Terry. 1975. Effect of mercury vapor on sugar
beets. J. Environ. Qual. 4:58-60.
Walker, C. H. 1980. Species variations in some hepatic microsomal
drug metabolizing enzymes. Prog. Drug. Metab. 5:113-164.
Wallen, I. E., W. C. Greer, and R. Lasater. 1957. Toxicity to Gambusia
affinis of certain pure chemicals in turbid waters. Sewage Ind.
Wastes 29:695-711.
Warnick, S. L., and H. L. Bell. 1969. The acute toxicity of some
heavy metals to different species of aquatic insects. J. Water
Pollut. Control Fed. 41:280-284.
Weast, R. C. (ed.). 1980. Handbook of Chemistry and Physics. CRC
Press, Cleveland, Ohio.
Woolson, E. A., J. H. Axley, and P. C. Kearny. 1971. Correlation
between available soil arsenic, estimated by six methods, and
response of corn (Zea mays L.). Soil Sci. Soc. Am. Proc.
35:101-105.
Zahn, R. 1975. Begasungsuerusche mit N02 in Kleingewachshauserun.
Staub Reinhalt. Luft. 35:194-196.
Zepp, R. G., and P. M. dine. 1977. Rates of direct photolysis in
aquatic environment. Environ. Sci. Technol. 11:359-366.
-------
67 ORNL/TM-9070
Zepp, R. G., and P. F. Schlotzhauer. 1979. Photoreactivity of
selected aromatic hydrocarbons in water. IN P- W. Jones and
P. Leber (eds.), Polynuclear Aromatic Hydrocarbons. Ann Arbor
Science Publishers, Inc., Ann Arbor, Michigan.
-------
69 ORNL/TM-9070
APPENDIX A
Aquatic Toxicity Data
-------
Table A-1. Acute toxicity of synfuels chemicals to aquatic animals
Representative
RAC chemical (s)
1 Carbon monoxide
2 Sulfur oxides
Test
organism3
Duration
Test typeb (h)
Concentration
(ng/L)
Notesc
No toxicity data
Aquatic problems
Reference
3 Nitrogen oxides
4 H2S
Scud (Gammarus
pseudollmnaeus)
Bluegill
(adults)
juveniles)
fry, 35-d-old)
eggs)
Northern pike
(eggs)
TL,,,
TL™
1^
TLm
TL,,
TL,,,
96
96
96
96
72
96
96
0.022
0.0448
0.0478
0.0131
0.0190
0.034-0.037
0.009-0.026
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
Oseid and Smith 1974
Smith et al. 1976
Smith et al. 1976
Smith et al. 1976
Smith et al. 1976
Adelman and Smith 1970
Adelman and Smith 1970
5 Ammonia
6 Heptane
7 Formaldehyde
Rainbow trout
(fry. 85-d-old) TLp
(adults) TLJJ,
Rainbow trout LC$Q
Rainbow trout 1X50
Rainbow trout (fry)
(fingerlings)
Mosquitofish Tin,
Several fish LC$o
species
24
24
24
24
24
24
96
24
0.068
0.097
0.50
0.47
0.2
0.2
4924
50-120
Rice and Stokes 1975
Rice and Stokes 1975
Herbert and Shurben 1963
Lloyd and Orr 1969
EIFAC 1970
EIFAC 1970
Wall en et al. 1957
National Research
Council 1981
i
VD
O
-------
Table A-l. (continued)
Representative
RAC chemical (s)
8 Carbon tetrachloride
Chloroform
9 Acetic acid
10 Volatile 0- and S-
heterocyclics
11 Pyridine
12 Benzene
13 Cyclohexane
Test
organism3
Daphnia magna
Fathead minnow
Bluegill
Bluegill
D. magna
Bluegill
Bluegill
Rainbow trout
Fathead minnow
Mosquitofish
Ciliate (Tetrahymena
pyrifornia")
0. magna
D". magna
JJ. magna
D. magna
Fathead minnow
Fathead minnow
Mosquitofish
Rainbow trout
Fathead minnow
Fathead minnow
Fathead minnow
Bluegill
Test typeb
LC50
LC50
LCso
LC50
LC50
LC50
LC50
LC50
LC50
TLm
LC50
LC50
LC50
LC50
,Lr5°
LC50
LC30
LC50
LC50
LC50
TLm
TLm
TLm
Duration
(h)
48
96
96
96
48
96
96
96
96
96
72
48
48
48
48
96
96
96
96
96
96
96
96
Concentration
(mg/L)
35.2
43.1
27.3
125.0
28.9
100.0
115.0
43.8
88.0
251.0
1211.8
1165
1755
203.0-620.0
426.0
32.0
15.1
1300.0
5.3
93.0
30.0
32.0
31.0
Notes0 Reference
US EPA 1980a
Flow-through test US EPA 1980a
US EPA 1980a
US EPA 1980a
US EPA 1980b
US EPA 1980b
US EPA 1980b
US EPA 1980b
Mattson et al. 1976
Wallam et al . 1957
No toxicity data
50% growth Schultz et al . 1980
inhibition
Canton and Adema 1978
Canton and Adema 1978
US EPA 1980c
Canton and Adema 1978
US EPA 1980c
Flow-through test DeGraeve et al . 1982
Wallam et al . 1957
Flow-through test US EPA 1980c
Mattson et al . 1976
Pickering and
Henderson 1966a
Pickering and
Henderson 1966a
Pickering and
Henderson 1966a
I
<~o
O
^J
O
—I
ro
Indan
Fathead minnow
LC50
96
14.0
Mattson et al. 1976
-------
Table A-l. (continued)
RAC
14
15
16
17
Representative
chemical (s)
Toluene
Naphthalene
Xylene
Anthracene
Phenanthrene
Fluoranthene
Aliphatic amines
Aniline
3, 5-Dimethyl aniline
Test
organism3
D. magna
Fathead minnow
Fathead minnow
Bluegill
Bluegill
JD. magna
D. magna
Fathead minnow
Fathead minnow
Rainbow trout
Fathead minnow
Goldfish
D. magna
TJ. magna
Rainbow trout
(embryo-larva)
D. magna
Fluegill
D. magna
Uaphnia cucul 1 ata
D. magna
TJ. magna
Test typeb
LC50
TLm
TLm
LC50
LC50
,Lr5°
LC50
LC50
LC50
TLm
TLm
LC50
50
50
LC50
LC5Q
LC50
LC50
Duration
(h)
48
96
96
96
96
48
48
48
96
96
96
96
48
48
96
48
96
48
48
48
48
Concentration
(mg/L)
39.22
44.0
45.0
24.0
12.7
2.16
8.57
3.14
4.90-8.90
2.30
42.0
17.0
0.75
1.10
0.04
325.0
3.9
0.65
0.68
0.58
1.29
Notes0 Reference
Millemann, et al. 1984
Pickering and
Henderson 1966a
Pickering and
Henderson 1966a
Pickering and
Henderson, 1966a
US EPA 1980d
Millemann et al. 1984
US EPA 1980e
Millemann et al. 1984
2 tests US EPA 1980e
US EPA 1980e
Mattson et al. 1976
Brenniman et al. 1976
Not toxic to fish, McKee and Wolf 1963
even in super-
saturated solutions
Millemann et al. 1984
Parkhurst 1981
Birge and Black 1981
US EPA 1980f
US EPA 1 980f '
No toxicity data
Canton and Adema 1978
Canton and Adema 1978
Millemann et al. 1984
Millemann et al . 1984
CO
I
ID
O
-------
Table A-l. (continued)
RAC
18
19
20
21
Representative
chemical (s)
Qu incline
2-Methylquinoline
2,6-Dimethylquinoline
Neutral N-,0-,5-
heterocyclics
Benzoic acid
Phenol
2-Methyphenol
4-Methyl phenol
Test
organism3
Ciliate (T. pyriforma)
D. magna
Fathead mi nnow
Fathead minnow
Ciliate (T. pyriforma)
Ciliate (T. pyriforma)
Mosquitofish
0. magna
D. magna
D". magna (Young)
Copepod (Mesocyclops
leukarti')
Fathead mi nnow
Fathead minnow
Bluegill
Rainbow trout
D. magna
F. magna
Fathead minnow
Fathead minnow
Bluegill
Fathead minnow
Duration
Test type6 (h)
LCgg 72
LCgQ 48
LC50 48
LC50 96
EC^Q 72
EC^Q 72
TLm 96
LC§o 48
Lf-50
TLm 50
LC50
LC5Q 48
LC50 96
LC5Q
LC50
LC50 48
LCso 48
TLm 96
TLm 96
TLm 96
TLm 96
Concentration
(mg/L)
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
20.78
19.0
Notesc
50% growth
inhibition
50% growth
inhibition
50% growth
inhibition
No toxicity data
4 tests
6 tests
2 flow-through
tests
Soft water
Hard water
Soft water
0
70
i —
— \
i
o
Reference O
Schultz et al. 1980
Millemann et al. 1984
Millemann et al . 1984
Mattson et al. 1976
Schultz et al. 1980
Schultz et al. 1980
Wallam et al . 1957 ^J
Millemann et al . 1984
US EPA 1980g
Dowden and Bennett 1965
US EPA 1980g
Millemann et al . 1984
US EPA 1980g
US EPA 1980g
US EPA 1980g
US EPA 1980g
US EPA 1980g
Pickering and
Henderson 1966a
Pickering and
Henderson 1966a
Pickering and
Henderson 1966a
Mattson et al. 1976
-------
Table A-l. (continued)
RAC
22
23
24
25
26
Representative
chemical (s)
Mixed cresol isomers
2,4-Dimethylphenol
3,4-Oimethylphenol
2,5-Dimethylphenol
Acrolein
Acetaldehyde
Acetone
Nonheterocylic
organosulfur
Alcohols
Nitroaromatics
Di-2-ethylhexyl
phthalate
Test
organism3
Aquatic life
0. magna
Fathead minnow
(juvenile)
Bluegill
Fathead minnow
j). magna
D. magna
IT. magna
Mosquitofish
Bluegill
Bluegill
Brown trout
Rainbow trout
Largemouth bass
Bluegill
D_. magna
£. magna
Duration
Test typeb (h)
TLm 96
LCgQ 48
LCso 96
LCgQ 96
LCso 96
LCso 48
LC50 48
LC50 48
LC50 48
LC50 96
LC50 96
LC50 24
LCso 24
LC50 96
LCgo 96
LCso 48
LC50
Concentration
(mg/L) Notes0
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
Reference
Kingsbury et al. 1979
US EPA 1980h
US EPA 1980h
US EPA 1980h
Mattson et al. 1976
Millemann et al. 1984
US EPA 19801
US EPA 19801
National Research
Council 1981
US EPA 19801
US EPA 19801
National Research
Council 1981
National Research
Council 1981
US EPA 19801
National Research
Council 1981
Canton and Adema 1978
US EPA 1980J
en
O
o
-------
Table A-l. (continued)
Representative
RAC chemical (s)
Diethyl phthalate
Butylbenzl phthalate
Di-n-butyl phthalate
27 Amides
28 Acrylonitrile
29 Tars
30 Respirable particles
31 Arsenic
Test
organism3
D. magna
Fluegill
0. magna
D. magna
Fathead m i n n ow
Fathead minnow
Bluegill
Bluegill
Rainbow trout
Scud (G. pseudo-
1 imnaeus j
Fathead minnow
Bluegill
Rainbow trout
D. magna
Fathead mi nnow
Fathead minnow
Fathead minnow
Bluegill
Bluegill
D. magna
D~. magna
Daphnia pulex
Stonefly (Pteronarcys
californical
Fathead minnow
(juvenile)
Bluegill (juvenile)
Bluegill
Rainbow trout
Brook trout
Test type0
LCso
LC50
LC50
LC50
LC50
LC50
LCso
LC50
"-C50
LC5°
50
50
LCsQ
LC50
TLm
ECcn
LC5°
LC5Q
50
50
LC50
Duration
(h)
48
96
96
96
96
96
96
96
96
96
96
96
96
48
48
48
96
96
96
93
Concentration
(mg/L)
52.1
98.2
92.3
3.7
5.3
2.1
43.3
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
Notesc
Hardness: 160
Hardness: 40
No toxicity data
Flow-through test
No aquatic emissions
No aquatic emissions
Immobilization
Immobilization
Flow-through test
Flow-through test
Flow-through test
Reference
US EPA 1980J
US EPA 198'Oj
US EPA 1980J
Gledhill et al. 1980
Gledhill et al. 1980
Gledhill et al. 1980
US EPA 1980J
Gledhill et al . 1980
Gledhill et al . 1980
Mayer and Sanders 1973
Mayer and Sanders 1973
Mayer and Sanders 1973
Mayer and Sanders 1973
US EPA 1980k
US EPA 1980k
US EPA 1980k
US EPA 1980k
US EPA 1980k
US EPA 1980k
Hohreiter 1980
Anderson 1946
Sanders and Cope 1966
Sanders and Cope 1968
Cardwell et al . 1976
Cardwell et al. 1976
US EPA 19801
US EPA 19801
Cardwell et al. 1976
O
^J
O
-------
Table A-l. (continued)
Representative Test
RAC chemical (s) organism9
32 Mercury (inorganic) £. magna
Stonefly (Acroneuria
lycorius)
Fathead minnow
Rainbow trout
Coho salmon
Rainbow trout
(juvenile)
Methylmercury Rainbow trout
Rainbow trout
(sac fry)
(fingerling)
(Juvenile)
Brook trout
(juvenile)
(yearling)
33 Nickel 0. magna
17. magna
Mayfly (Ephemeral la
subvariT)
Stonefly (A. lycorius)
Oamselfly
(unidentified)
Midge
(Chironomus sp.)
Caddisfly
(unidentified)
Fathead minnow
Fathead minnow
Bluegill
Bluegill
Test typefc
LCso
TLm
LCso
50
50
LCso
LCso
LCgn
LCso
LCSO
LCSO
LC50
LCso
TLm°
TLm
TLm
TLm
TLm
LCso
TLm
TLm
TLm
Duration
(h)
48
96
96
96
96
96
96
96
96
96
96
96
96
96
Concentration
(mg/L)
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
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
Notesc
4 tests
Flow-through test
Flow-through test
Hardness: 51
Hardness: 100
Hardness: 42
Hardness: 40
Hardness: 50
Hardness: 50
Hardness: 50
Hardness: 20
2 flow-through
tests
Hardness: 210
flow-through test
Hardness: 20
2 tests
Hardness: 360
Reference
Biesinger and
Christensen 1972
Warnick and Bell 1969
US EPA 1980m
Hohreiter 1980
US EPA 1980m
US EPA 1980m
Hohreiter 1980
Hohreiter 1980
Hohreiter 1980
US EPA 1980m
McKim et al. 1976
McKim et al. 1976
US EPA 1980n
US EPA 1980n
Warnick and Bell 1969
Warnick and Bell 1969
Rehwoldt et al. 1973
Rehwoldt et al. 1973
Rehwoldt et al . 1973
US EPA 1980n
Pickering 1974
Pickering and
Henderson 1966b
Pickering and
I
10
O
-------
Table A-l. (continued)
Representative Test
RAC chemical(s) organism3 Test type'-
Rainbow trout LCso
Fish sp., general LC5Q
Fish sp., general 1X50
34 Cadmium D. magna LCso
IT. raagna LCso
D. magna \-£$Q
Mayfly (Ephemerella Tip,
grand isgrandi si
Mayfly (E- subvar i a ) TLn,
Stonefly (Pteronarcella TU,
badia)
Damselfly TL,,,
(unidentified)
Midge TLn,
(Chironomus) Caddisfly TLm
(unidentified)
Fathead minnow TL^,
Fathead minnow TLm
Bluegill TLm
Bluegill LCso
Rainbow trout LCso
(swim-up and parr)
Rainbow trout LCsg
Carp LCso
Chinook salmon (Parr) LCso
Brook trout LCso
Green sunfish LCso
Pumpkinseed LCso
Duration
(h)
96
96
96
96
96
96
96
96
96
96
96
96
96
96
Concentration
(mg/L)
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-
0.00175
0.00175
0.24
0.0035
0.0024
2.84
1.5
Notes'
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
Hardness: 31;
flow-through test
Hardness: 55
Hardness: 23
Hardness: 44
(sodium sulfate)
Hardness: 20
Hardness: 55
Reference
Henderson 1966b
Hale 1977
Hohreiter 1980
Hohreiter 1980
US EPA 19800
US EPA 19800
US 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
US EPA 19800
US EPA 19800
US EPA 1980o
US EPA 1980o
US EPA 19800
US EPA 1980o
US EPA 1980o
US EPA 19800
O
•-J
O
—I
00
-------
Table A-l. (continued)
Representative Test
RAC chemical (s)
35 Lead
36 Fluorine
aLatin binomials are
bLCso = concentration
organism3
D. magna
D". magna
Fathead minnow
Fathead minnow
Bluegill
Bluegill
Rainbow trout (fry)
Rainbow trout
Rainbow trout
Rainbow trout
Brook trout
D. magna
SbldTtsn"
Goldfish
Goldfish
Rainbow trout
listed in Appendix C.
required to kill 50* of test
L Duration
Test type" (h)
LC50
LC50
LCgg 96
TLm 96
TLm 96
TLm 96
LC50 96
LC5o 96
LC50 96
LCso 96
LCgQ 96
48
96
12-29
60-102
TLm 240
organisms.
Concentration
(rag/L)
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
Notes6
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
US EPA 1980p
US EPA 1980p
US EPA 1980p
Pickering and
Henderson 1966b
Pickering and
Henderson 1966b
Pickering and
Henderson 1966b
Hohreiter 1980
Davies et al. 1976
Hohreiter 1980
US EPA 1980p
US EPA 1980p
Hohreiter 1980
Hohreiter 1980
Hohreiter 1980
Hohreiter 1980
Angelovic et al . 1961
TL,,, = median tolerance limit.
ECgo = effective concentraton causing a designated
cHardness values are
given in milligrams per liter
effect on 20* of test organ ismsn.
as CaC03. DO = dissolved
oxygen .
^xj
^o
O
73
-z.
1
—1
2
1
ID
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-Dimethylphenol
Acrolein
Di-2-ethylhexyl
phthalate
Butyl benzyl
phthalate
Acrylonitrile
Arsenic
Mercuric chloride
Methylmercuric
chloride
Test
organism3
Fathead minnow
Rainbow trout
Rainbow trout
Rainbow trout
Daphnia magna
Fathead minnow
Fathead minnow
Fathead minnow
Fathead minnow
D. magna
D. magna
Fathead minnow
JJ. magna
Rainbow trout
D. magna
Fathead minnow
D. magna
Fathead minnow
D. magna
D. magna
Fass sp., general
Pink salmon
D. magna
D_. magna
Fathead minnow
Brook trout
Duration
Test typeb (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) Notesc
3.4
1.2 Hardness: 200
2.0 Hardness: 50
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
US EPA 1980a
US EPA 1980b
US EPA 1980b
US EPA 1980b
US EPA 1980c
US EPA 1980e
US EPA 1980g
US EPA 1980h
US EPA 1980h
US EPA 1980i
National Research
Council 1981
US EPA 1980i
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980k
US EPA 1980k
US EPA 19801
Hohreiter 1980
Hohreiter 1980
Hohreiter 1980
US EPA 1980m
US EPA 1980m
Hohreiter 1980
US EPA 1980m
I
to
o
•^j
o
oo
o
-------
Table A-2. (continued)
Representative Test
RAC chemical (s) organisma
33 Nickel
34 Cadmium
35 Lead
36 Fluorine
D. magna
D. magna
(Clistqronia
magnif ica)
F athead minnow
Fathead minnow
Rainbow trout
J). magna
D. magna
If. magna
Midge (Tanytarsus
dissimilis)
Fathead minnow
Bluegill
Brook trout
Brook trout
0. magna
0. magna
Stonefly (Acroneuria
lycoriasl
Mayfly (Ep"hemerella
subvarTa]
Caddisfly (Hydropsyche
betteri )
Bluegill
Rainbow trout
Rainbow trout
Rainbow trout
Rainbow trout
Duration
Test typeb (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) Notes0
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:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
Hardness:
100* kill,
hardness:
100% kill,
Hardness:
yearling
51
105
50
44
210
50
53
103
209
201
207
36
187
52
151
41
28
35
45
320,
trout
Reference
US EPA 1980n
US EPA 1980n
US EPA 1980n
US EPA 1980n
US EPA 1980n
US EPA 1980n
US EPA 19800
US EPA 1980o
US EPA 19800
US EPA 19800
US EPA 1980o
US EPA 1980o
US EPA 1980o
US EPA 1980o
US EPA 1980p
US EPA 1980p
Hohreiter 1980
Hohreiter 1980
Hohreiter 1980
US EPA 1980p
US EPA 1980p
US EPA 1980p
Hohreiter 1980
Hohreiter 1980
aLatin binomials are listed in Appendix C.
bLC5Q = concentration requred to kill 50% of test organisms.
TL,,, = median tolerance limit.
cHardness values are given in milligrams per liter as CaC03.
CO
o
--J
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-Dimethyl phenol
Test
organism Test type3
Chlorella vulgaris ECi;n
C. vulgaris ECqn
Selenastrum ECc;n
capricornutum
C. vulgaris ECc;o
Chalamydomonas ECfii
angulosa
S. capricornutum ECqn
S. capricornutum ECsn
Agmenellum
quadruplicatum
A. quadruplicatum
Coccochloris elabens
Eucapsis sp.
Oscillatori a williamsii
S. capricornutum
S. capricornutum ^50
Nitzschia linearis ECqn
Chlorella pyrenoidosa EC-joo
C. vulgaris ECgo
~t_. pyrenol3bsa ECloo
Duration
(h)
48
24
96
48
24
96
96
24
120
48
80
48
Concentration
(mg/L)
525.0
245.0
433.0
33.0
34.4
54.4
54.6
0.010
0.010
0.010
0.010
0.010
20.0
40.0
258.0
1500.0
470.0
500.0
Notes
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 cell
numbers
Reduction in
chlorophyll a
production
Diffusion from disk
onto algal lawn
inhibited growth
for 3-7 d
Diffusion from disk
onto algal lawn
inhibited growth
for 3-7 d
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
6°f chlorophyl 1
Reference
US EPA 1980C
US EPA 1980d
US EPA 1980d
US EPA 1980e
US EPA 1980e
US EPA 1980f
US EPA 1980f
Batterton et al .
1978
Batterton et al .
1978
US EPA 1980g
US EPA 1980g
US EPA 1980g
US EPA 1980g
US EPA 1980g
US EPA 1980g
o
—I
o
CO
ro
-------
Table A-3. (continued)
RAC
26
31
32
33
34
Representative
chemical (s)
Butyl benzyl phthalate
Dimethyl phthalate
Di ethyl phthalate
Arsenic
Mercuric chloride
Methyl mercuric
chloride
Nickel
Cadmium
Test
organism
S. capricornutum
S. capricornutum
Microcystis aeruginosa
Navicula pelliculosa
S. capricornutum
S. capricornutum
S. capricornutum
S. capricornutum
Cladophora, Spirogyra,
Zygnema sp.
Scenedesmus sp.
C. vulgaris
Spring diatom
assemblages
Coelastrum
microporum
Chlamydompnas,
Chlorella,
Haematococcus,
Scenedesmus sp.
Phormidium ambiguum
Scenedesmus
Scenedesmus sp.-
Scenedesmus sp.
C. pyrenoidosa
C. vulgaris
5". capricornutum
Mixed species
Test type3
EC50
EC50
EC50
EC50
EC50
EC50
EC50
EC50
ECiQO
EC50
EC50
EC50
ECie
EC50
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 cell
numbers
Reduction in cell
numbers
Reduction in cell
numbers
Reduction in
chlorophyll a
Reduction .in cell
numbers
Reduction in
chlorophyll a
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
CaCOs
Growth inhibition
Threshold effects
Reduction in cell
numbers
Growth inhibition
Growth inhibition
Growth reduction
Growth reduction
Population reduction
Reference
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 1980J
US EPA 19801
Cushman et al .
1977
US EPA 1980m
US EPA 1980m
US EPA 1980m
US EPA I980n
Cushman et al . 1977
Cushman et al . 1977
US EPA 1980o
Cushman et al . 1977
US EPA 1980o
US EPA 1980o
US EPA 19800
US EPA 1980o
CO
oo
I
<£>
o
-vj
o
-------
I
10
o
Table A-3. (continued)
Representative Test
RAC chemical (s) organism
35 Lead Ankistrodesmus sp.
Chlorella sp.
Scenedesmus sp.
Selenastrum sp.
Anabaena sp.
Chlamydomonas sp.
Cosmarium sp.
Navicula sp.
Scenedesmus sp.
Test type3
Fr24
pr53
"35
"52
EC50
EC50
EC50
EC50
Duration
(h)
24
24
24
24
Concentration
(mg/L)
1.00
0.50
0.50
0.50
15.0-26.0
17.0
5.0
17.0-28.0
2.5
Notes
Growth inhibition
Growth inhibition
Growth inhibition
Growth inhibition
Reduction in C02
fixation
Reduction in C02
fixation
Reduction in C02
fixation
Reduction in C02
fixation
Threshold effects
Reference
US EPA 1980p
US EPA 1980p
US EPA 1980p
US EPA 1980p
US EPA 1980p
US EPA 1980p
US EPA 1980p
US EPA 1980p
Cushman et al . 1977
CO
aECn = concentration causing a designated effect on a given percentage of test organisms.
-------
85 ORNL/TM-9070
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 dioxide 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
Response15 (h)
-C02 uptake
-20% N fixation
-Growth
Defoliation
-44% yield
-42% yield
-26% foliage
-22% foliage
-40% total weight
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% yield
552
24
72/week
72/week
100
100
2070
8
6
1680
334
639
1900
620
357
4
2070
3
64
-39% yield 672-840
-66% yield
-weight and
linear growth
-38% sugar
+43% sugar .
2112
5904
3216
3216
Concentration
(ug/m3)
1.8 E+03
1.1 E+05
1.1 E+07
2.3 E+07
3.9 E+02
3.9 E+02
1.3 E+02
1.3 E+02
1.78 E+02
9.2 E+01
6.5 E+01
1.3 E+02
2 E+03
2 E+03
2-3 E+03
2 E+03
4 E+03
3.8 E+03
2.1 E+02
7.0 E+02
2.8 E+02
4.2 E+02
4.2 E+02
4.2 E+02
4.2 E+02
4.2 E+01
Notesb«c
Detached leaves
Field, growing season
Field, growing season
5 h/d, 5 d/weeks,
4 weeks
5 h/d, 5 d/weeks,
4 weeks
103.5 h/weeks,
20 weeks
Sensitive clone
-17% linear growth in
following year
103.5 h/week,
20 week
4 h/d, 4 d/week
for 4 week
Continuous fumigation
Continuous fumigation
Continuous fumigation
Continuous fumigation
Continuous fumigation
Reference
National Research
Council 1977a
National Research
Council 1977a
National Research
Council 1977a
National Research
Council 1977a
US EPA 1982
US EPA 1982
US EPA 1982
US EPA 1982
US EPA 1982
US EPA 1982
US EPA 1982
US EPA 1982
Zahn 1975
Zahn 1975
Zahn 1975
Zahn 1975
Zahn 1975
Heck and Tingey 1979
Ashenden and
Mansfield 1978
Taylor in press
Taylor in press
Thompson and Kats 1978
Thompson and Kats 1978
Thompson and Kats 1978
Thompson and Kats 1978
Thompson and Kats 1978
CO
^j
O
70
•z.
r—
***••.
—I
3
I
VD
0
-~J
-------
Table B-l. (continued)
RAC
5
6
7
8
12
13
14
17
22
23
Representative
chemical
Ammonia
Ethyl ene
Formaldehyde
Vinyl chloride
Benzene
Cyclohexene
Toluene
Aniline
Acrolein
Carbonyl sulfide
Test
organism*
Mustard
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
Exposure ^
Duration
Response11 (h)
Injury
Epinasty
Flowers do not open
Growth inhibition
Growth inhibition
Growth inhibition
Injury
Necrosis and leaf
symptoms
Injury
Red-bordered spots
LDso, toxicity
to leaves
Bronze color
Damage
Oxident-type damage
LDgo, toxicity
to leaves
-13% growth
4
20
72
720
168
240
5
48
168
0.6
1
0.6
3
9
1
64
Concentration
(yg/m3) Notesb.c
2.1 E+03
1.15 E+00
1.15 E+02
6.85 E+02
8.60 E+02
2.39 E+03
4.9 E+02
2.47 E+02
2.6 E+05
3.0 E+04
1.12 E+12
1.88 E+05
2.7 E+02
2.5 E+02
2.7 E+03
4.9 E+02 4 h/d, 4 d/week
for 4 weeks
Reference
National Research
Council 1979b
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
National Research
Council, 1981
Kingsbury et al . 1979
Heck and Pi res 1962
Kingsbury et al . 1979
Ivens, 1952
Kingsbury et al . 1979
Cheeseman and Perry 1977
Kingsbury et al. 1979
Ivens 1952
Taylor, in press
i
to
o
o
00
OD
-------
Table B-l. (continued)
Representative
RAC chemical
32 Mercury (metallic)
Mercuric chloride
Dimethylmercury
Exposure
Test
organism3
Rose
Sugar beet
English ivy
Coleus, Thevetia
and Ricinus
Thevetia and
Ricinus
Coleus, Thevetia
and Ricinus
Response15
Severe damage
Damage
Damage
Abscission
Necrosis
Abscission
Duration
(h)
5
12
168
168
36
Concentration
(ug/m3) Notesb>c
1.0 E+01
2.8 E+02
1.5 E+04
1.0 E+01
1.0 E+01
1.0 E+01
Reference
Stahl 1969
Waldron and Terry 1975
Waldron and Terry 1975
Siegel and Siegel 1979
Siegel and Siegel 1979
Siegel and Siegel 1979
Latin binomials are listed in Appendix C.
Minus sign designates a reduction in the measured response.
cl)nless "field" is noted, results are for laboratory studies.
to
i
10
o
--J
o
-------
Table B-2. Toxicity of chemicals in soil or solution to vascular plants.
RAC
9
11
13
14
15
16
19
Representative
chemical
Acetic acid
Methyl pyridine
Hexene
Xylene
Benzo(a)pyrene
3,4-benzopyrene
1 ,2-benzanthracene
1,2,5,6-di-
benzanthracene
Dimethyl al kyl ami ne
Benzothiophene
Indole,
3-ethyl-lH
Indole-3-
acetic acid-lH
Test organism5
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)
Test medium
Solution in sand
Solution
Solution
Solution
Solution
Soil
Soil
Soil
Solution
Solution
Solution
Solution
Solution
Solution
Response Duration
Root growth inhibition 5 d
Root growth inhibition 4 d
Mortality
Root growth inhibition 2 d
Root growth stimulation 6 h
78% growth stimulation 60 d
80% growth stimulation 60 d
130% growth stimulation 60 d
Mortality
9% root growth inhibition 4 d
Growth inhibition
Growth inhibition
Mortality 11 d
Germination reduced by 505! 8 h
Concentration
(pg/g)
600
93.1
25.2
100
0.0005
0.01
0.02
0.02
7.0
10
100
100
35
10
I
O
0
Reference
Lynch 1977
Naik et al. 1972
Chen and El of son 1978
Allen et al . 1961
Deubert et al. 1979
Graf and Nowak 1966
Graf and Nowak 1966 Q
Graf and Nowak 1966
Dutta et al. 1972
Schlesinger and Mowry 1951
Davies et al. 1937
Davies et al. 1937
Hilton and Nomura 1964
Shukla 1972
-------
Table B-2. (continued)
RAC
20
21
22
23
24
27
31
Representative
chemical
Benzoic acid
2-hydroxy-
benzoic acid
Phenol
4-hydroxy-
benzaldehyde
Carbon disulfide
Ethanol
H,H-d i methyl -
formamide
2 -methyl -
benzamide
Arsenicb
Test organisma
and
life stage
Lettuce (seedling)
Rice (seedling)
Lettuce (seedling)
Durum wheat (seed)
Lettuce (seedling)
Apple
Lettuce (seed)
Lettuce (seed)
Poppy, chickweed,
carrot, ryegrass
corn, lucerne
(mature)
Corn
(seedling)
Cotton
(mature)
Cotton
(mature)
Test medium
Solution on
filter paper
Soil
Solution on
filter paper
Solution
Solution on
filter paper
Soil
Solution
Solution
Soil
Soil
Soil (fine sandy
loam)
Soil (clay)
Response Duration
23* growth inhibition
Seedling growth inhibition 5 d
61% growth inhibition
Germination inhibition 4 d
26% growth inhibition
Root injury
Germination inhibition 44 h
Nearly total suppression 24 h
of germination
13-87% reduction in yield 3-5 w
10* growth reduction 4 w
(wet tissue weight)
Approx. 55% reduction 6 w
in yield
Approx. 40* reduction 6 w
in yield
Concentration
(p.g/9)
25
1.6
25
2000
100
420
1,000,000
1 ,000,000
220,000
64
28=
Reference
Chou and Patrick 1976
Gaur and Pareek 1976
Chou and Patrick 1976
Badilescu et al. 1967
Chou and Patrick 1976
Underhill 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
to
O
73
i —
O
O
-------
Table B-2. (continued)
Representative
RAC chemical
32 Mercury
33 Nickel
Test organism3
and
life stage
Soybean
(mature)
Soybean (mature)
Cowpea
Barley
(seed-sprout)
Barley
(seed-sprout)
Lettuce
(seed-sprout)
Corn
(mature)
Sunflower
(mature)
Oats
(seeds-seedlings)
Oats (mature)
Barley (seedling)
Test medium
Soil (fine sandy
loam)
Soil (clay)
Solution
Solution
Solution
Solution
Solution
Solution in
coarse sand
Soil
Solution in sand
Response
Approx. 4558 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
Stunted growth
Decreased grain yield
Over 50% reduction in whole-
plant fresh weight
Concentration
Duration (ug/g)
6 w 3C
6 w 12C
1C
Reference
Deuel and Swoboda 1972
Deuel and Swoboda 1972
Albert and Arndt 1932
7 d post- 5 Mukhiya et al . 1983
germination (as Hg++)
7d post- 1
germination (as PMA)d
5 d post- 109 (as
germination HgCl?)
7 d 5
7 d 0.8
22 d post- 10
germination
Whole life 50
3 weeks 281
(NiS04-7H20)
Mukhiya et al . 1983
Nag et al. 1980
Carlson et al. 1975
Carlson et al. 1975
Vergnano and Hunter 1953
Hal stead et al. 1969
Agarwala et al. 1977
34 Cadmium
i
to
o
—I
o
i-D
ro
Corn (mature)
Sunflower (mature)
Solution
Solution
10% decrease in
net photosynthesis
10% decrease in
net photosynthesis
7 d
7 d
0.9 Carlson et al. 1975
0.45 Carlson et al. 1975
-------
Table B-2. (continued)
Test organism3
Representative and
RAC chemical life stage
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)
34 Cadmium Sycamore
(sapling)
35 Lead Soybeans
(mature)
Test medium
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)
Soil (6:1 silty clay
loam and perlite)
Solution in sand
and vermiculite
Response
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)
25% reduction in new stem
growth
35% decrease in fresh
weight of pods
Concentration
Duration (ug/g) Reference
90 d
3 weeks
3 weeks
3 weeks
3 weeks
3 weeks
3 weeks
3 weeks
3 weeks
3 weeks
5 weeks
5 weeks
Whole life
90 d
90 d
Z
0.2
0.2
0.2
1.2
0.9
4.8
5.6
2.0
9.0
2.5
2.5
2.5
39
62
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
Carlson and Bazzaz 1977
Huang et al. 1974
to
CO
0
1—
—1
I
o
o
-------
Table B-2. (continued)
Test organism3
Representative and
RAC chemical life stage
Lettuce
(44 d old)
Corn
(25-d seedling)
Soybean
(25-d seedling)
Sycamore
(sapling)
Test medium
Soil (silty clay
loam)
Vermiculite and
solution
Vermiculite and
solution
Soil (6:1 silty clay
loam and perlite)
Response
25% reduction in yield
20* decrease in
photosynthesis
20* decrease in
photosynthesis
25* reduction in new
stem growth
i
>£>
O
--J
O
Concentration
Duration (ug/g) Reference
30 d 1000 John and VanLaerhoven
Pb(N03)2 1972
11-21 d 1000 Bazzaz et al . 1974
vo
11-21 d 2000 Bazzaz et al . 1974 "**
90 d 500 Carlson and Bazzaz 1977
aLatin binomials are listed in Appendix C.
bArsenic shows a stimulatory effect on plants when present at low concentrations (40-50 ug/g total As or 5 ug/g extractable As in soil) (Woolson
et al. 1971).
concentration of water extractable contaminant.
dPMA = phenyl mercuric acetate.
-------
Table B-3. Toxiclty of chemicals in air to animals
Representative Test
RAC chemical organism*
1 Carbon monoxide Rabbit
Dog
Chicken
Rabbit
Human
2 Sulfur dioxide Guinea pig
Guinea pig
Dog
Chicken
Sulfuric acid Guinea pig
Guinea pig
Dog
3 Nitrogen dioxide Guinea pig
Rat
Rat
Mouse
Rat and mouse
4 Hydrogen sulfide Canaries, rats,
and dogs
Dogs
Exposure
Response'3
Aortic lesions
Heart damage
75* egg hatch
90* neonate survival
Lethality
Increased airway
resistance
LTso
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
Lethality
Duration Concentration
(h) (ug/m3) Notes
4
1,008
432
720
1
1.1
5,400
1
8
4,725
1
5,120
24
24
Chronic
Subacute
10-18
1.51 E+05
4.3 E+04
4.9 E+05 Egg exposed
1.0 E+05 Mother exposed
9.2 E+08
4.2 E+02
5.8 E+06
1.3 E+04
3.7 E+03 Intermittent
exposure, 7 d
1.0 E+02
1.8 E+04
8.9 E+02
1.5 E+05
2.3 E+04
2.8 E+04
3.8 E+03
9.4 E+02 Also decreased
resistance to
infection
7.0 E+04 No established
chronic effects
2.1 E+05
Reference
National Research
Council 1977a
National Research
Council 1977a
National Research
Council 1977a
National Research
Council 1977a
Cleland and Kingsbury
1977
US EPA 1982
US EPA 1982
US EPA 1982
Wakabayashi et al .
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
National Research
10
01
•yO
•z.
r~
i
VD
O
•^ 1
Council 1979a
-------
Table B-3. (continued)
Representative
RAC chemical
5 Ammonia
6 Acetylene
7 Formaldehyde
8 Chloroform
9 Acetic acid
10 Furan
Thiophene
11 Pyridlne
2-Ethylpyridine
Exposure
Test
organism3
Chicken
Pig
Rabbit
Mouse
Human
Human
Rat
Guinea pigs
Rat
Mouse
Human
Mouse
Human
Human
Rat
Mouse
Rat
Rat
Duration Concentration
Response15 (h) (ug/m3)
Increased disease
susceptibility
Respiratory irritation
l-Tso
Lethal threshold
Throat irritation
Unconsciousness
LC50
Increased airway
resistance
Respiratory and eye
irritation and
liver weight loss
LCgo
Enlarged liver
LC5Q
Irritation
Respiratory, stomach
and skin irritation
Lethal threshold
Lethal threshold
LCr50
L(000
72
840
33
16
Immediate
0.08
4
1
1,400
Chronic
1
0.05
Chronic
8-48
8-48
4
3
1.3 E+04
4.3 E+04
7.0 E+06
7.0 E+05
2.8 E+05
3.7 E+08
5.7 E+05
3.6 E+02
1.0 E+03
1.4 E+05
4.9 E+04
1.4 E+07
2.0 E+06
1.5 E+05
2.4 E+08
3.0 E+07
1.3 E+07
2.4 E+07
Notes Reference
Newcastle virus National Research
Council 1979b
National Research
Council 1979b
National Research
Council 1979b
National Research
Council 1979b
National Research
Council 1979b
National Research
Council 1976
National Research
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
12 Benzene
Human
Lethal threshold
Chronic
1.9 E+05 Workplace exposure
National Research
Council 1976
1
ID
<£>
-------
Table B-3. (continued)
RAC
13
14
15
16
17
Representative
chemical
Pentane
Cyclopentane
Hexane
Cyclohexane
Heptane
Butadiene
Cyclopentadine
Toluene
Ethyl benzene
p-Xylene
Tetrahydro-
naphthalene
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,
Ethyl ami ne
1-Aminopropane
Aniline
Dimethyl anal ine
Rat
"Animals"
Rat
Rat
Mouse
Exposure
Duration Concentration
Response13 (h) (ug/m3) Notes
Lethality
Lethality
Lethality
Dizziness
Lethality
Narcosis and* convulsions
Dizziness
Respiratory and eye
irritation
Liver and kidney
damage
Lethal threshold
Psychological effects
Lethal threshold
Eye irritation
Lethal threshold
Lethal threshold
Eye irritation and
damage
but several members of this RAC
Lethal threshold
Lung, liver, and 1,
kidney damage
LC50
LCso
LC50
0.
1
1
0.
8
245
4
4
0.
4
136
are
4
008
4
4
7
3.8 E+08
1.1 E+08
1.2 E+08
17 1.8 E+07
9.2 E+07
4.5 E+07
10 4.1 E+06
1.8 E+07
1.4 E+06 Expsoure = 7 h/d
for 35 d
1.5 E+07
3.8 E+05
1.7 E+07
08 8.8 E+05
1.5 E+07
1.5 E+06 8 h/d for 17 d
7.9 E+04
carcinogens.)
5.5 E+06
1.8 E+05
5.6 E+06
9.5 E+05
7.4 E+05 Mixed isomers
Reference
Kingsbury et al.
Kingsbury et al.
Kingsbury et al.
Kingsbury et al .
Kingsbury et al.
Kingsbury et al .
Kingsbury et al.
Kingsbury et al.
Kingsbury et al .
Kingsbury et al .
Kingsbury et al .
Kingsbury et al.
Kingsbury et al .
Kingsbury et al.
Kingsbury et al .
Kingsbury et al .
Kingsbury et al .
Kingsbury et al .
Kingsbury et al .
Kingsbury et al .
Kingsbury et al.
Kingsbury et al.
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
1979
18 (No data on respiratory toxicity)
19 (No data on respiratory toxicity)
20 (No data on respiratory toxicity)
21 (No data on respiratory toxicity)
O
-vj
O
-------
Table B-3. (continued)
RAC
22
23
24
Representative
chemical
Acrolein
Acetaldehyde
Proprionaldehyde
Butyraldehyde
Butanone
Methyl mercaptan
Ethyl mercaptan
N-Butyl mercaptan
Thiophenol
Carbon disulfide
Methanol
Exposure r3
Test
organism9
Rat
Monkey
Mice, rabbits,
guinea pigs
Rat
Rat
Rat
Mouse
Rat
Rat
Human
Rat
Human
Rat
Human
Monkey
Human
Responseb
LC50
Respiratory system
damage
and 1X50
LC50
Reduced weight gain
LC50
LC50
Lethal threshold
LCso
Central nervous
system effects
LC§Q
"Toxic effect"
LC50
Central nervous
system effects
LC5Q
Central nervous
Duration
(h)
4
2,160
4
0.5
36
0.5
0.75
4
3
4
Concentration
(yg/n)3) Notes
1 .8 £04
5.1 £02
2.0 £06
6.2 £07
3.1 E06 6 h/d for 6 d
1.7 £08
6.1 £08
2.0 E07
1.1 E07
1.0 E04
1.5 £07
1.0 £04
1.5 EOS
5.0 £04 7-year exposure
1.3 £06
7.5 £04
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
del and and Kingsbury
1977
Kingsbury et al. 1979
Kingsbury et al. 1979
i
O
O
IO
CO
25
26
Ethanol
Human
(No data on respiratory toxicity.)
Methyl acetate Human
Methyl methacrylate Rat
Butyl acetate Human
Human
N-Amyl acetate Human
27 (No data on respiratory toxicity.)
system effects
Eye and respiratory
irritation and
mental effects
Severe toxic effects
Throat irritation
Toxic effects
Toxic threshold
1
1
1
0.5
1.9 E06
1.5 E06
1.5 E07
9.6 £05
9.6 £06
1.0 £06
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
Kingsbury et al. 1979
-------
Table B-3. (continued)
Representative
RAC chemical
28 Acetonitrile
Acrylonitrile
Test
organism3
Rat
Human
Rat
Exposure
Duration
Response0 (h)
Lethal threshold 4
Bronchial effects
Lethal threshold 4
Concentration
(ug/m3) Notes
1.3 E07
2.7 EOS
1.1 E06
29 (No data on respiratory toxicity)
30 Fly ash
31 Arsenic trioxide
32 Mercury (metal)
33 Nickel carbonyl
34 Cadmium oxide fumes
Cadmium oxide dust
Cadmium
35 Lead
Monkey
Rat
Human
Rabbit
Human
Rat
Human
Human
Human
Human
Slight lung fibrosis 13,390
Weight lag and 24
physiological
effects
Toxic threshold
Toxic threshold
Central nervous
system effects
LC50 0.5
Lethality 8
Impaired lung function
Pulmonary and renal
effects
Threshold of overt
poisoning
4.6 E+02
2.5 E+01
1.0 E+03
2.9 E+04
1.7 E+02 40-year exposure
2.4 E+05
5.0 E+03
3.15 E+03 20-year exposure
1.0-27 E+01 Occupational exposure
5.0 E+02 Occupational exposure
Reference
Kingsbury et al. 1979
Kingsbury et al . 1979
Kingsbury et al. 1979
Kingsbury et al. 1979
National Research
Council 1979c
National Research
Council 1977c
Cassidy and Furr 1978
Cassidy and Furr 1978
Kingsbury et al. 1979
National Research
Council 1975
Hamnons et al . 1978
Hammons et al. 1978
Kingsbury et al . 1979
National- Research
Council 1972
aLatin binomials are listed in Appendix C.-
bLC5o/LCigo = concentration required to kill 503S/1003! of test organisms.
LTgQ = time to lethality for 50!t of organisms tested.
i
O
o
-------
101 ORNL/TM-9070
APPENDIX C
Common and Scientific Names of Animals and Plants
-------
103
ORNL/TM-9070
Common and Scientific Names of Animals and Plants
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 truTta
Serinus canarius
Cyprinus carpio~
Ictalurus punctatus
Gallus gall us
Oncorhynchus tshawytacha
Oncorhynchus kisutch
Can is 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
Oryctolacjus cuniculus
Salmo gairdneri
Rattus rattus
Ictiobus bulbalus
Morone chrysops
Plants
Common name
African marigold
Alfalfa
Apple
Barley
Bean
Broadbean
Bush bean
Cabbage
Carnation
Carrot
Chickweed, common
Cocksfoot
Scientific name
Tagetes sp.
Medicago sativa
Malus sylvestris
Hordeum vulgare
Fhaseolus vulgaris
Vicia faba
Phaseolus yulgaris
Brassica'oleracea
Dianthus caryophyllos
Daucus carota
Stellaria media
Dactyl is glomerata
-------
ORNL/TM-9070
104
Appendix C (continued)
Plants
Common name
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
Sugar beet
Sunflower
Sycamore
Thevetia
Tobacco
Tomato
Turnip
Wheat
White pine
Scientific name
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
Avena fatua
Psoralea corylifolia
Capsicum frutescens
Petunia sp.
Phaseolus vulgaris
Acacia farnesiana
Papaver sp.
Raphanus sativus
Trifoliurn pratense
Oryza sativa
Ricinus communis
Rosa sp.
Phaseolus vulgaris
Lolium mintiflorum
Glycine max
Picea abies
Cucurbita sp.
Beta vuTg^aris
Helianthus annuus
Platanus occidentalis
Thevetia neriifolca
Nicotiana tabacum
Lycopersicon esculentum
Brassica napus
Triticum durum
Pinus strobus
-------
105 ORNL/TM-9070
APPENDIX D
Species-Specific Results of the Analysis of Extrapolation Error
-------
Table D-l. Predicted geometric mean maximum allowable toxicant concentrations (PGMATCs) for each RAC and each species of fish.
PGMATC3 (mg/L)
4
5
6
7
8
9
10
11
12
13
H
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
32A
33
34
35
RAC
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
Cadmi urn
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
b
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
b
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
b
2001
182
4.9
b
b
b
133.0
b
65
b
b
229
14.0
4.5
433
0.5
77
Green
sunfish
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
sunfish
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
t
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
b
566
252
b
b
125
68
65
74
b
b
159
b
1317
208
4.0
b
b
b
145.9
b
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
b
281
12.0
4.4
296
0.3
102
apGMATCs were not calcuated for RACs 1-3.
t>No data.
i
10
O
-------
ORNL/TM-9070
108
Table D-2. Probabilities of chronic toxic effects on fish populations due to
RAC 4 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
P6MATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.0345
0.0345
0.0345
0.0261
0.0915
0.0451
0.0980
0.1186
0.1940
0.0839
0.0839
0.0649
0.0649
0.0649
0.0597
0.1068
0.0468
0.0927
0.1336
0.2261
0.1117
0.1117
Class
Class
Class
Class
a
Genus
Species
Family
Family
Class
Class
aBluegill - Perciformes
-------
109
ORNL/TM-9070
Table D-3. Probabilities of chronic toxic effects on fish populations due to
RAC 5 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.0069
0.0069
0.0069
0.0092
0.0168
0.0168
0.0168
0.0168
0.0168
0.0144
0.0149
0.0097
0.0097
0.0097
0.0196
0.0185
0.0185
0.0185
0.0185
0.0185
0.0090
0.0149
Class
Class
Class
Class
Class
Class
Class
Class
Class
Species
Family
-------
ORNL/TM-9070
110
Table D-4. Probabilities of chronic toxic effects on fish populations due to
RAC 15 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.0016
0.0016
0.0016
0.0022
0.0038
0.0025
0.0030
0.0035
0.0136
0.0030
0.0030
0.0019
0.0019
0.0019
0.0047
0.0018
0.0006
0.0004
0.0015
0.0262
0.0021
0.0021
Class
Class
Class
Class
a
Genus
Species
Family
Family
Class
Class
aBluegill - Perciformes
-------
Ill
ORNL/TM-9070
Table D-5. Probabilities of chronic toxic effects on fish populations due to
RAC 22 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western' site:
Rainbow trout
Brook trout
0.0238
0.0238
0.0238
0.0258
0.0617
0.0282
0.0559
0.0372
0.1263
0.0550
0.0507
0.0392
0.0392
0.0392
0.0540
0.0783
0.0266
0.0494
0.0296
0.1711
0.0538
0.0628
Class
Class
Class
Class
Class
Genus
Species
Species
Family
Species
Family
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ORNL/TM-9070
112
Table D-6. Probabilities of chronic toxic effects on fish populations due to
RAC 26 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.0092
0.0011
0.0011
0.0019
0.0023
0.0075
0.0114
0.0132
0.0374
0.0015
0.0023
0.0062
0.0007
0.0007
0.0031
0.0010
0.0040
0.0051
0.0117
0.0667
0.0002
0.0009
Family
Class
Class
Class
Class
Genus
Species
Family
Family
Species
Family
-------
113
ORNL/TM-9070
Table D-7. Probabilities of chronic toxic effects on fish populations due to
RAC 32 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
P6MATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
SmalImouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.0088
0.0088
0.0088
0.0112
0.0216
0.0216
0.0216
0.0216
0.0216
0.0186
0.0184
0.0130
0.0130
0.0130
0.0242
0.0252
0.0252
0.0252
0.0252
0.0252
0.0132
0.0197
Class
Class
Class
Class
Class
Class
Class
Class
Class
Species
Family
-------
ORNL/TM-9070
Table D-8. Probabilities of chronic toxic effects on fish populations due to
RAC 32A at annual median ambient concentrations for unit release
Ratio of ambient Probability of
concentration to exceeding the Level of
Species PGMATC PGMATC extrapolation
Eastern site:
Carp 0.0259 0.0428 Class
Bigmouth buffalo 0.0259 0.0428 Class
Smallmouth buffalo 0.0259 0.0428 Class
Channel catfish 0.0277 0.0575 Class
White bass 0.0675 0.0853 Class
Green sunfish 0.0675 0.0853 Class
Bluegill sunfish 0.0675 0.0853 Class
Largemouth bass 0.0675 0.0853 Class
Black crappie 0.0675 0.0853 Class
Western site:
Rainbow trout 0.0948 0.0964 Species
Brook trout 0.0498 0.0478 Species
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115 ORNL/TM-9070
Table D-9. Probabilities of chronic toxic effects on fish populations due to
RAC 33 at annual median ambient concentrations for unit release
Ratio of ambient Probability of
concentration to exceeding the Level of
Species PGMATC PGMATC extrapolation
Eastern site:
Carp 0.0032 0.0012 Family
Bigmouth buffalo 0.0003 0.0001 Class
Smallmouth buffalo 0.0003 0.0001 Class
Channel catfish 0.0007 0.0009 Class
White bass 0.0007 0.0001 Class
Green sunfish 0.0021 0.0005 Genus
Bluegill sunfish 0.0024 0.0003 Species
Largemouth bass 0.0027 0.0011 Family
Black crappie 0.0115 0.0225 Family
Western site:
Rainbow trout 0.0004 0.0000 Species
Brook trout 0.0008 0.0001 Family
-------
ORNL/TM-9070
116
Table D-10. Probabilities of chronic toxic effects on fish populations due to
RAC 34 at annual median ambient concentrations for unit release
Species
Ratio of ambient
concentration to
PGMATC
Probability of
exceeding the
PGMATC
Level of
extrapolation
Eastern site:
Carp
Bigmouth buffalo
Smallmouth buffalo
Channel catfish
White bass
Green sunfish
Bluegill sunfish
Largemouth bass
Black crappie
Western site:
Rainbow trout
Brook trout
0.
0.
0.
0.
0.0271
0.1957
.1957
.1516
.5739
.0039
0.0053
0.0059
0.0204
1.1682
0.7237
0.0192
0.2235
0.2235
0.2052
0.3908
0.0008
0.0014
0.0036
0.0388
0.5332
0.4308
Species
Class
Class
Class
Class
Species
Species
Family
Family
Species
Species
-------
117 ORNL/TM-9070
APPENDIX E
Detailed Methods and Assumptions for
Ecosystem Uncertainty Analysis
-------
119 ORNL/TM-9070
APPENDIX E
DETAILED METHODS AND ASSUMPTIONS FOR
ECOSYSTEM UNCERTAINTY ANALYSIS
E.I ORGANIZING TOXICITY DATA
The first step in ecosystem uncertainty analysis (EUA) is the
selection of appropriate toxicity data and association of the data with
components of the Standard Water Column Model (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 ten algal populations if no other information is
available. If data are available on diatoms and blue-green algae, a
further division is possible, based on physiological parameters in the
model and past experience with SWACOM. Like diatoms, species 1 to 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 are available for D_- 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 increase in blue-green algae growth is one of our endpoints, we
assign the greatest sensitivity to the consumer (i.e., 15) that is most
abundant during the summer of the simulated year.
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ORNL/TM-9070 120
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.
Tne 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, an 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 Categories (RACs), 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 involves
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 the response of organisms 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 GSS. However,
some organics might have a "narcotic," effect which would be opposite
to the reaction assumed here.
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121 ORNL/TM-9070
The GSS 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 the 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 (microcosm simulation).
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 we alter the
parameters simultaneously in the direction indicated by the GSS until
we duplicate the original experiment. Thus, for an LCrg (96 h), we
find the percentage change that halves the population in 4 d.
At the conclusion of the Microcosm simulations, we have the
percentage change in the parameters that matches the experiment.
We must now make an additional assumption to arrive at the expected
response for concentrations below the LC5Q or EC5Q. We assume a
linear dose response. Thus, an environmental concentration that is
one-fifth 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 ±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.
-------
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ORNL/TM-9070 124
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125 ORNL/TM-9070
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ORNL/TM-9070 126
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