United Slates
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-453/R-96-013C
October 1996
Air
5 EPA Study of Hazardous Air Pollutant
Emissions from Electric Utility Steam
Generating Units — Interim Final Report
Volume 3. Appendices H - M
-------
U.S. Environmental Protection Agency
Region 5, Library (PL-12J)
77 West Jackson Boulevard, 12(\\ 1 '•••*>»
Chicago, IL 60604-3590
-------
TABLE OF CONTENTS
Appendix
Appendix H
Appendix I
Appendix J
Appendix K
Appendix L
Appendix M
Page
Summary of Speciation, Environmental Chemistry, and Fate of Eight HAPs
Emitted from Utility Boiler Stacks » . . H-l
Summary'of EPRI's Utility Report 1-1
Parameter Justifications: Scenario Independent Parameters J-l
Parameter Justifications Scenario-Dependent Parameters K-l
Mercury Partition Coefficient Calibrations L-l
Description of Exposure Models M-l
L
u
...y
11
-------
Appendix H—Summary of Speciation, Environmental Chemistry, and
Fate of Eight HAPs Emitted from Utility Boiler Stacks
Appendix H is followed by Appendix H-l, List of Utility Boiler
Test Reports, and Appendix H-2, Utility Boiler Stack Emisisons of
Furans/Dioxins, PAHs, and Six Trace Metals.
-------
This page is intentionally blank.
-------
H.1 INTRODUCTION
Under Section 112(n)(1)(A) of the 1990 Clean Air Act
Amendments (CAAA), Congress mandates that the U.S. Environmental
Protection Agency (EPA) study the hazards to public health
reasonably anticipated to occur as a result of emissions of
hazardous air pollutants (HAPs) by electric utility steam
generating units (utilities). EPA was to present the results of
this study in a Report to Congress by November 15, 1995.
As part of this study, the EPA is conducting direct and
indirect human exposure modeling. This document was prepared as
part of EPA's indirect exposure modeling effort. It discusses
the environmental chemistry, speciation, and fate of eight HAPs:
arsenic, cadmium, chromium, lead, mercury, nickel,
dioxins/furans, and polycyclic aromatic hydrocarbons (PAHs).
The eight HAPs were investigated as to their form when emitted
from a utility boiler stack (Section H.2) and their reactions (if
any) and fate in the atmosphere, water, and soils (Section H.3)
to better understand what HAPs or reaction products of HAPs
humans are most likely to be exposed to via various routes of
exposure (e.g., oral [ingestion], inhalation, dermal [skin]) as a
result of emissions from utilities. This document summarizes the
available information.
In preparing this report, test reports, published and
unpublished literature, and other readily available information
such as government documents (e.g., Agency for Toxic Substances
and Disease Registry [ATSDR] toxicological profiles) and
reference books were reviewed to identify information on the
physical state, species, oxidative state, and anticipated aquatic
and soil reaction products of the eight HAPs.
H.2 UTILITY TEST RESULTS
As part of a Congressionally mandated study of HAPs from
electric steam generating units (utilities), the Department of
Energy (DOE), the Northern States Power Company (NSP), and the
Electric Power Research Institute (EPRI) conducted emissions
tests at over 40 power plants for a variety of HAPs potentially
present. Nine of the tests were performed for DOE, eight for
NSP, and the remainder for EPRI. The EPRI sites were identified
only by a site number; the other sites were identified by name.
Of the tests performed, 33 were on coal-fired boilers, 12 were on
oil-fired boilers, and 2 were on gas-fired boilers. The Agency
reviewed test reports from 47 recent tests performed at
43 utility boiler sites. (Appendix H-l lists the 47 test
reports.)
H-l
-------
Emission control systems tested for the coal-fired boilers
included 13 electrostatic precipitators (ESPs), 3 fabric filters
(FFs), 5 combined ESP flue-gas desulfurization (FGD) units,
6 combined FF FGD units, and one each of the following: a
combined hot-side, cold-side ESP, an ESP with ammonia injection,
a combined ESP and pulse-jet FF, and a fluidized-bed combustor
(FBC) boiler with FF. Emission control systems for the oil-fired
units were one ESP, one FF, one pulse-jet FF, two units that
combined magnesium oxide (MgO) slurry fuel additive and an ESP,
and one unit using MgO fuel additive and a selective catalytic
reduction (SCR) nitrogen oxides/sulfur oxides (NOX/SOX) removal
process. Seven of the oil-fired units had no emission control,
and neither of the gas-fired units had emission controls. For
some sites, more than one emission control system was tested,
either as a pilot unit in parallel with an existing system or
after replacement of an existing system with a new system. The
representativeness of the data should allow the descriptive
statistics presented here to withstand scrutiny.
All of the tests discussed here were performed in 1990 or
later, with most of them performed in 1992 and 1993. The timing
of the testing is important because of test method validity. For
example, mercury tests performed before the introduction of new
test methods in 1990 may provide suspect results.
The type of fuel, boiler, and emission controls can affect
HAP emissions from a boiler stack. The data presented in this
report could be analyzed to determine the qualitative effects of
each of these variables for the trace metals and possibly for the
dioxins/furans and PAHs.
H.2.1 Frequency of HAPs Occurrence
Appendix H-2 contains air emissions for the 6 trace metals
(including some speciation for mercury, arsenic, chromium, and
nickel), 16 individual polychlorinated dioxins and furan isomers
and 9 congeners, and 29 PAHs (classified as polycyclic organic
matter [POM] on the CAAA HAPs list) measured during the testing
at utility boiler stations discussed in Section 2.0. These
tables are based on 47 tests and 59 HAPs. Unlike other
industries that measure emissions as metric weight per dry
standard cubic meter (e.g., ng/dscm) , the unit of measure in
Appendix H-2 is lb/1012 British thermal unit (Btu) . This unit of
measure is commonly used in the utility industry to report
emissions relative to the amount of fuel burned.
At least one of the metals was measured in all of the
47 tests reported. In addition, mercury was measured in
speciated forms in 10 emission tests, speciated chromium in
H-2
-------
11 tests, speciated nickel in 2 tests, and speciated arsenic in
2 tests at one site. Dioxins and furans were measured at 12 test
sites, but many of the reported averages contained one or more
values below the detection limit. Of the 25 individual dioxin
and furan isomers and congeners, most were found at 10 of the
test sites. One or more of the 29 PAHs were found at 24 of the
tests. However, measurements were scattered among sites and
among individual PAHs. The most frequently measured PAHs were
2-methylnaphthalene, fluorene, fluoranthene, phenanthrene, and
pyrene. All 29 PAHs except 1-chloronaphthalene,
dibenz(a,j)acridine, 3-methylcholene,
7,12-dimethyIbenz(a)anthracene, nitranthracene, and perylene were
measured at least once.
H.2.2 Data Quality
The eight HAPs discussed in this report are present only in
trace quantities in utility boiler stacks. In many cases,
concentrations are near the detection limit of the analytical
method being used. Additionally, the sampling process is carried
out in large ducts (up to about 40 or 50 feet wide) carrying hot,
particulate-laden gas streams that usually have irregular flow
patterns. The combustion process may be irregular as to fuel
characteristics and will change with load changes-in the
generating system. For example, coal fired on one day may be
mined from the center of a seam rich in a trace metal, while coal
fired on another day may be mined from a different area of the
seam that is less rich in the trace metal. When power demand on
a boiler is reduced, less fuel is fired and the combustion zone
changes in dimension. However the size of the combustion chamber
remains the same. This situation might change the distribution
of combustion products in the flue gas and the partitioning of
elements between fly ash and bottom ash. All of these factors
combine to produce final analytical results that should be
accepted with caution.
Additional uncertainties associated with combining measured
concentrations of an analyte with averages of measured flow rates
in a series of tests made over several days need to be considered
when using these data. For example, the heating value of a coal
feed may be measured on a Monday, but the concentration of a
compound in the flue gas may not be measured until Tuesday. A
resulting value of emission rate in lb/1012 Btu will contain
errors caused by the time difference in measurements.
Because of low or nonexistent concentrations of analytes,
many measurements resulted in nondetects. Nondetects occurred
when concentration values were so low that the analytical method
could not determine if the analytes were present. For such
H-3
-------
situations, the presence of an analyte in any individual test of
a series (as shown by a concentration above the detection limit)
was taken to mean that the analyte was present. The question
then arose as to what concentration value to assign to any other
measurement in the series when the measurement was below the
detection limit. Common choices have been zero, half the
detection limit, or the detection limit. For this report, tests
showing nondetects were assumed to have an analyte concentration
equal to half the detection limit.
In Tables H-2-1 through H-2-3 of Appendix H-2, essentially
all values are calculated averages of more than one measurement.
If all measurements forming one average are above the detection
limit, the average is shown in an open box. If the average
contains one or more nondetect values, the box is shaded. All
averages contain at least one value above the detection limit,
either at the stack or at some point prior to the stack.
H.2.3 Speciation Data
For the HAPs examined in this report, all 25 isomers and
congeners of dioxins and furans are reported, as are most of the
individual compounds listed as PAHs. Species within the trace
metals are available for 20 tests and only for mercury, chromium,
nickel, and arsenic. Each of the three categories of HAPs is
discussed below. Numerical emission values are given in Tables
H-2-1 through H-2-3 of Appendix H-2.
H.2.3.1 Dioxins and Furans. Table H-l summarizes
Appendix H-2 emissions of dioxins and furans measured at utility
boilers.
In general, 2,3,7,8-TCDD and 2,3,7,8-TCDF emissions were
approximately 10"6 lb/1012 Btu. With a few exceptions at 1C)'5
lb/1012 Btu and 10'4 lb/1012 Btu and one exception at 10~3 lb/1012
Btu, other forms had similar values.
H.2.3.2 PAHs. Eight of the PAHs are of particular interest
due to their probable carcinogenicity under EPA's category B2
(weight of evidence from animal testing). These are
benz(a)anthracene, benzo(b)fluoranthene, benzo(b and
k)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, chrysene,
dibenz(a,h)anthracene, and indeno(l,2,3-c,d)pyrene. Table H-2
shows median values and ranges for each of the eight as well as
other frequently measured PAHs.
H-4
-------
Table H-l. Emissions of Dioxins/Furans from Utility Boilers
(lb/1012 Btu)
Specific Dioxin or Furan
2,3,7,8-Tetrach!orodibenzo-p-dioxin
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
1,2, 3,4,7, 8-Hexachlorodibenzo-p-dioxin
1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin
1,2,3,4,6, 7, 8-Heptachlorodibenzo-p-dioxin
Octachlorodibenzo-p-dioxin
2,3,7,8-Tetrachlorodibenzofuran
1,2,3,7,8-Pentachlorodibenzofuran
2,3,4,7,8-Pentachlorodibenzofuran
1,2,3,4,7,8-Hexachlorodibenzofuran
1,2,3,6,7,8-Hexachlorodibenzofuran
1,2,3,7,8,9-Hexachlorodibenzofuran
2,3,4,6,7,8-Hexachlorodibenzofuran
1,2,3,4,6,7,8-Heptachlorodibenzofuran
1,2,3,4,7,8,9-Heptachlorodibenzofuran
Octachlorodibenzofuran
Tetrachlorodibenzo-p-dioxin
Pentachlorodibenzo-p-dioxin
Hexachlorodibenzo-p-dioxin
Heptachlorodibenzo-p-dioxin
Tetrachlorodibenzofuran
Pentachlorodibenzofuran
Hexachlorodibenzofuran
Heptachlorodibenzofuran
Median
2.30x 10"6
4.38x 10'6
1 .05 x 1 0'5
5.44x 10'6
8.34x 10'6
1.23x 10'6
4.07 x 10*
3.94x 10"6
2.89 x 10'6
6.51 x 10*
8.08 x 10-6
3.99 x 10'6
6.30x 10'6
1.05x 10'5
1.67x 10'5
1.01 x 10'5
1.39 x 10'5
6.61 x 10'6
5.76x 10"6
1.60x 10'5
2.56x 10-5
9.68 x 10"6
1.16x 106
1.50 x 10'5
1.91 x 10'5
Maximum / Minimum
6.51 x 10*/3.50x 10-'
6.51 x 1 0-6 / 6.03 x 10-7
1.52x 10-5 / 1.21 x 10-6
1.83x 1 0-5 / 6.03 x 10-7
1.92x 1 0-5 / 6.03 x 10-7
3.27x 10-4 / 5.99 x 10-7
1.71 x 1 0-3 / 4.79 x 10-6
4.55 x 10-6/6.68x 10'7
2.09 x 10'5 / 6.89 x 10'7
4.83 x 10'5/1.62x 10'6
2.56x 10"/2.79x 10'6
8.77 x 10'5/7.33x 10'7
1.75 x 10* / 6.03 x 10'7
1.88 x 10-*/9.99 x 10'7
1.18x 10'3/ 1.36x 10'6
5.67x lO-4/!^! x 10'6
8.21 x 10'3/7.99x 10'7
5.97x 10'5/ 1.24x 10'6
4.22 x 10'5 76.89 x 10'7
1.80 x 10-* /8.76 x 10'7
6.00 x 10-*/2.34x 10-6
1.49x 10"1/ 1.37x 10"6
2.89 x 10-4 72.89 x 10'6
7.62 x 10-* 7 3.85 x 10"6
2.63 x 10'372.54x 10's
Btu = British thermal unit.
H-5
-------
Table H-2. Emissions of Selected PAHs from Fossil-Fuel-Fired
Utility Boilers
PAHa
Benz(a)anthracene (c)
Pyrene (f)
Benzo(b)fluoranthene (c)
Benzo(b and k)fluoranthene
(0
Benzo(k)fluoranthene (c)
Benzo(a)pyrene (c)
Chrysene (c)
Dibenz(a,h)anthracene (c)
lndeno(1,2,3-c,d)pyrene (c)
2-Methylnaphthalene (f)
Fluorene (f)
Fluoranthene (f)
Phenanthrene (f)
Frequency
of
detection
(from up to
47
individual
tests)
9
14
1
5
1
8
9
4
6
12
13
15
17
Median
(lb/1012Btu)
3.53x 10'3
1.26x 102
808 x 10'3
6.65 x 10'3
3.56x 10'3
1.02x 10'3
3.56 x 10'3
- 2.70 x 10'3
6.40 x 10'3
2.34 x 10'2
1.35 x 10'2
9.89 x 10'3
2.66x 102
Maximum/minimum
(lb/1012Btu)
1.41 x 10° / 1 x 10'3
1.60x 10'1 / 1.21 x 10'3
only one value
4.79 x 10-2/ 1.61 x 10'3
only one value
3.67 x 10'2/2.27x 10"4
5.99 x 10'2/3.30x 10"4
1.32x 10'2/3.30x TO"1
3.59 x TO'2/ 1.65 x 10'3
2.15 x TO'2/ 1.72 x 10'3
1.72 x 10'1 / 1.29 x 10'3
2.81 x 10'3/9.58x 10'2
3.08 x 10'1 79.37 x 10'3
Btu = British thermal unit.
' (c) indicates suspected carcinogen; (f) indicates measured frequency greater
than 10.
H-6
-------
With one exception, all of the median values are on the
order of 10"3 lb/1012 Btu. The exception, pyrene, is
approximately 1.3 x 10~2 lb/1012 Btu. Ranges run from 1CT4 lb/1012
Btu to 10'1 lb/1012 Btu.
Of the PAHs not listed in Table H-2, all, with the exception
of biphenyl, have median values in the 10"2 lb/1012 Btu or 10"3
lb/1012 Btu range. Biphenyl has a median value of about 1.8 x 10"
1 lb/1012 Btu. Ranges generally run from about 10'4 lb/1012 Btu to
10'1 lb/1012 Btu.
H.2.3.3 Trace Metals. Four of the metals (mercury,
chromium, nickel, and arsenic) had speciation data (see Table
H-3). All of the metals were measured in nonspeciated form at
nearly all sites. Most of the measurements were made in the test
site stack or ductwork ahead of the stack. However, a few
measurements were made at the outlet of a pilot or full-scale
device operating in parallel with ductwork or other control
devices. All of the measurements represent values that, in the
absence of chemical reaction, should be found being emitted to
the atmosphere after passing through the control device indicated
in Table H-2-3 (Appendix H-2).
The median value for lead was 3.78 lb/1012 Btu, with a range
of 1.1 x 10'1 lb/1012 Btu to 176 lb/1012 Btu. The median value for
cadmium was 1.24 lb/1012 Btu with a range of 2.33 x 10"2 lb/1012
Btu to 28.5 lb/1012 Btu. The four speciated metals are discussed
separately.
H.2.3.3.1 Mercury. Although total mercury was measured at
most of the sites, elemental mercury and ionic mercury were
measured at only 10 sites. Several of the sites attempted
measuring methyl mercury with the Bloom method.1 However, the
method does not provide consistently accurate methyl mercury
measurements. A resolution to this problem is to add reported
methyl mercury values to reported ionic mercury values (for the
same test) and report the sum as ionic mercury. This procedure
is followed here.
Total mercury had a median value of 3.44 lb/1012 Btu, with a
range of 6.60 x 10'2 lb/1012 Btu to 22.9 lb/1012 Btu. The sum of
the median values for elemental mercury (1.44 lb/1012 Btu) and
ionic mercury (1.93 lb/1012 Btu) was about the same value as for
total mercury. This agreement is surprisingly good, considering
H-7
-------
[Q
M
0)
rH
•rl
O
(Q
•H
rH
-H
4J
D
•0
(U
M
•H
fe
I
rH
0)
-rH
(0
I
r-l
(C
0)
o
•0
(U
4J
u
(U
o
in
rt
o
[0
•H
I
i
K
•a
*9
1
CO
in
35 -8
,
i
S.
2
o
^
||
28
c "S
1«
|?
b
i
O 0)
c *B
I-
i
n
O
i
*•
B a
£«»
3
f
O
i
g -2
jl
isi
It
i
00
T—
^
z
CO
CO
z
in
CO
«-
CO
'o
•o
c
CO
To
c
u
_c
T
a
a
CO
CO
T-
^
z
CO
CO
z
CO
d
o
CO
"5
_c
•c
a
c
CO
CO
CO
^
z
CN
CO
CO
z
CO
CO
o
'c
1
CM
q
iri
^
Z
*
CO
z
CO
in
*t
CO
CO
'e
1
•5
c.
]
t
0
CM
CO
O
^
d
in
t'
CO
^
^
CM
CO
s
CO
1
'5
1
"a
c
|
t
U Elemental m
CO
CO
CO
o
T*
d
O)
CO
^
in
"m
m
T^
CO
CO
CM
CM
1
'c
CO
- To
c
u
c
>
U Ionic mercur
CM
in
CM
in
CO
O)
CO
z
CO
in
CO
CO
"5
c
I
0
CO
in
O
*^
O
in
O
•*
•-
o>
in
O
CO
A
O5
"5
c
u
_c
£•
c
CO
II Elemental m
V—
*
00
^
0
in
CO
O
CO
CO
CM
^
CO
CM
o>
"5
c
u
>>
|| Ionic mercur
in
CM
CO
d
^
Z
1
d
z
CO
in
CD
d
in
'c
_c
£•
i
-
^_
o
'c
c
t
1
CD
H Elemental m
^
z
z
z
^
z
CS
o
d
z
CM
q
d
-
'c
c
£•
c
.2
c
CO
^
z
CN
»-
Z
05
CM
O
'c
CO
|| Cadmium in coal
m
in
in
^
Z
0)
r-
Z
CM
CO
O
CO
|| Cadmium in coal
:
^
z
o
en
d
z
in
•-
O
|| Cadmium in oil
H-8
-------
•0
a =5
CD C
TI ™ _
>- o 2
o •- *•
II
1
i
K
CM
Z
in
CM
CM
Z
00
en
en
IV
'6
1
eo
"5
8
.£
o
<
CO
CM
Z
CO
in
Z
00
CO
to
To
8
u
'c
<
01
CO
CO
CM
O
d
00
in
CM
C>
o>
CO
£
6
X
CO
CO
*
en
CO
CD
00
>*•
d
a
*>
CM
"3
8
f
Trivalent arsenic ii
CO
CO
00
CO
o
d
CM
in
00
d
5
b
X
CM
^
CD
CM
d
CM
^_
CD
8
£
o
Pentavalent arseni
o
op
Z
CO
CD
CO
z
in
CM
CO
^
"o
_c
<
IV
en
CO
Z
in
in
Z
in
(O
CO
'6
I
CD
"5
8
c
i
Z
d
CO
Z
tn
Z
oi
CO
1
c
"o
Z
co
CO
Z
CM
rv
CO
Z
00
in
CM
'5
c
I
z
00
in
in
CO
CO
CO
00
tn
X
0
X
CO
CM
CD
00
in
00
CM
co
CM
—
Soluble nickel in o
CO
o>
00
00
O)
CO
b
X
00
r-
CO
co
00
|v
CO
CN
;^
Sulfidic nickel in o
-
-
-
-
-
-
0
ZSi
Metallic nickel in c
0)
CO
t—
CM
OS
in
O)
CO
E
O
X
CM
CM
*
CO
CM
CM
CM
_
'5
c
ID
a
2
X
o
"5
jf
o
Z
O)
00
CD
d
en
tv
CM
00
00
CM
CD
en
00
CO
in
'5
TJ
C
ID
"5
o
o
_c
1
o
u
^
CM
CO
O
d
01
CM
o
X
a>
00
00
,_
CO
CO
e
i
'6
CD
To
o
u
_c
i
D
Hexavalent chrom
o>
00
CM
0>
tv
d
in
CM
O
en
o
q
|V
O)
00
CO
in
CO
hromium in coal
O
^
in
O>
O
d
00
(V
CM
O
CM
IV
rv
d
T—
0
O)
tt
"5
o
o
c
£
3
Hexavalent chrom
o
00
rv
en
CM
d
en
in
IV
rv
00
CO
o
CO
en
in
*—
hromium in oil
O
o
CM
rv
O
d
?
CO
CN
TJ-
q
0
CM
00
r-
~
o
c
£
3
Hexavalent chrom
H-9
-------
•0
o
g
g
«n
K
(D
H
i
o
aximum/minimum (II
BTU)
S
•«
!
€
ID
en
m
1
o
on
i
1
~^
!s
11
•
e •§
8 Q
1-
?
b
i
e »
^ "S
S w
e
CD
b
£
3
to
X
o
X
CO
rx
0
to
in
CO
IX
§
CO
CD
00
CM
to
X
CD
CO
O
X
05
CM
CM
"o
CO
"a
o
u
o
u
c
'5
c
1
'c
To
C
O
_c
1
a
to
X
CO
to
X
00
CO
CO
6
f^
CD
CO
CM
05
CM
CO
O
CO
6
'5
1
a
"5
o
u
c
fc
1
I
_s
u
to
CM
tx
04
05
id
CM
CO
*
in
CO
CO
in
in
00
CO
CM
^
CM
CD
'c
^
g
U
8
_c
1
'e
o
10
CO
to
a
CM*
cs
I
1
o
CO
to
in
CO
10
CD
O>
to
••••
mS^S
CO
CO
CO
CM
Cn
•H
en
in
O
CO
CD
"5
8
c
1
i
I Elemental i
in
O
to
en
id
CO
CO
o
Tj;
in
^•Ml
^^^S
^.
CO
CO
Tt
•H
,_
in
CO
CM
6
~S
8
|
a
c
"c
_e
CD
CO
o
to
CM
"c
i
-
«-
-
^••1
SBB
*"
^^
•••i
"^
^
.c
g
Elemental i
z
z
z
IHHB
SS3
^
Z
^
Z
••Mi
^
Z
^
z
:=
O
C
1
_e
to
X
CO
CO
oi
b
X
in
00
CM
CO
CM
8
CJ
in
CO
CM
o
CO
to
05
CO
*•
o
X
o
X
q
i—
'6
1
0
•5
II Cadmium in co
•S
Cadmium in co
U Cadmium in oil
'E
•o
c
CO
I) Arsenic in coal
H-10
-------
g
•H
O
u
to
K
0
H
•s
IH
o
^
]
1
E
E
*x
ID
S
••^••i
555E5
£
i
in
en
••••
Sf^S
£
1
Q.
£
S
en
£
1
Q.
O
S
H
DO
••••••••
5555551
II
.«
B
N
I-
I
•••••••
5555555
0 «
P
« in
a.
3
f
o
£
ercent of
Spade*
a.
lb/10" BTU
1
j
|| Arsenic in coal
b
*"*
X
CO
q
CO
b
X
05
CO
CO
O
CO
en
d
•^••B
iiSSS
O
CO
CO
CO
d
••••
^^^—
en
CO
co
CO
cS
d
"5
8
|| Trivalent arsenic in <
^_
b
X
IV
CO
CO
«—
O
£
oi
••••
o
CO
"-
^MH
sssss
CO
q
d
•5
o
u
c
|| Pentavalent arsenic
q
IV
CM
"6
.0
1
M
b
X
CO
"b
X
in
«•;
CM
°5
•o
c
CO
•5
8
c
i
.s
CO
o
o
d
5
CO
"a
o
o
_c
~S
o
Z
O
CO
d
o
CM
'o
_c
13
o
Z
b
^
X
CO
CM
O
X
^*
CM
^
CO
in
O
in
q
CM
CO
in
5
*
CO
in
CO
(0
'5
c
i
u
"c
£
1
CO
CM
O
X
CO
CO
o
CM
^
CO
CM
O
*~
CO
CO
IV
CO
|| Sulf idle nickel in oil
-
_
_
-
Metallic nickel in oil
O
o>
o
X
in
CO
CO
en
CO
O
CO
CO
r-
O)
CO
0
^
en
CO
CM
'5
_c
1
'x
o
"5
u
Z
b
T—
X
o
X
CO
CO
«—
05
-.
in
O)
CO
CM
en
CO
CO
U Chromium in coal and oi
r«l
b
X
CO
CM
O
CO
en
CO
*
en
IV
q
>*
-
CO
(0
d
'5
1
CO
"5
o
o
c
E
|| Hexavalent chromiu
d
CO
CO
o
O)
CO
in
o
en
cri
en
CO
CO
in
|| Chromium in coal
CM
O
d
o
CO
o
CO
CD
o
CO
in
-
o
en
d
"a
8
_c
E
H Hexavalent chromiu
o
IV
en
05
o
CO
q
in
"~
0
CO
0
CO
0
CO
en
in
d
|| Chromium in oil
CO
CO
d
CO
CO
o
CM
in
CO
CO
o
CM
CO
CO
CO
0
CM
CO
d
=5
.c
i
Hexavalent chromiu
H-ll
-------
* S
in g1
» 0
* S
£ c
* 2
8
e
1 o
o~
=o
c 1
pa
•
2 S
tS ~
M_ CO
s
ig
S
D ™ os T* >0
O3O)iS
>>«Q.
« « H. E
in ^ 01
» 0) 9>
O) Q) O)
CO O> <0
£2 S
0) C V
O Q) (^
Q. 0> Q.
. 0- .
0) . V
(D 2 05
>»>
g
a>
>
*
c
-i „- rf
•- •- ^ W 0) « »
OJWBJn .S S .tt
I
•ri
^
0
o
m
K
O
H
I
uni
her
ish
BTU
en
ed
nonsp
NA = not applica
Open cells indica
^ 1! i §!!'!'! c ^ $-s£ I
<5J3 _EEo5O)fe(5«a' 'E.9'E_ou
c >» _2 .S .5 ^ *^ .2 .5 *-• ^ ^ .2 £ .2 -n .2
.
g rao-L^STo® gTBTo « cS'S.Z'B °T5
» ..
*n w ffl >% v*
0}w— +;0*;.. *;
ID
a
fe gg «* 10 g'
^-
05
TJO
t-
a. Si-zccccujooccco « o cc en ir u. cc
H-12
-------
that different test methods were used for measuring total mercury
and for measuring species. Ranges for elemental and ionic
mercury (mercury (+2)) were 4.60 x 1CT1 lb/1012 Btu to 14.7 lb/1012
Btu and 2.72 x 1CT2 lb/1012 Btu to 5.49 lb/1012 Btu, respectively.
The split between elemental and ionic mercury was not consistent
from one site to another. About half the sites had more ionic
mercury, while the rest have more elemental mercury. The average
proportion of elemental mercury from 10 test sites was 62
percent, while the ionic mercury was 38 percent. When median
values are used, 43 percent was elemental and 57 percent was
ionic. For coal only, the average values from 9 samples were 59
percent for elemental mercury and 41 percent for ionic mercury
(median percentages were 40 and 60 for elemental and ionic,
respectively). For oil, only one sample was found to contain
ionic mercury; elemental mercury was not detected. Statistics on
a percentage basis for mercury are given in Table H-4.
H.2.3.3.2 Chromium. Total chromium was measured at most
sites. The median value was 6.42 lb/1012 Btu, with a range of
4.11 x 10'1 lb/1012 Btu to 13.8 lb/1012 Btu. The median value for
the 11 tests showing chromium (VI) was 8.89 x 10'1 lb/1012 Btu
with a range of 2.37xlO'2 lb/1012 Btu to 6.04 lb/1012 Btu. The
amount of chromium (VI) for these 11 test sites averaged 11
percent (median 12 percent) of all speciated chromium, with a
range of 0.4 percent to 34 percent. Separate tests for chromium
(III) were not made. When divided between coal- and oil-fired
utilities, chromium (VI) averaged 11 percent (median 10 percent)
for the four coal-fired sites, with a range from 0.4 percent to
23 percent. Seven oil-fired boilers averaged 20 percent chromium
(VI) (median 23 percent), with a range from 5 percent to 34
percent. No other sites were available for testing.
It should be noted that chromium (VI) is easily reduced to
chromium (III) when in reducing conditions. Stack conditions
lead to rapid reduction of existing chromium (VI), as experienced
during field testing.2 This factor may influence (reduce) the
levels of chromium (VI) measured in the stack and downwind from
the stack while the chromium (VI) remains in reducing conditions.
Even in ambient conditions, the half-life of chromium (VI) is
about 15 hours. Table H-5 gives statistics on a percentage basis
for speciated chromium.
H-13
-------
CO
M
0)
i-H
•H
O
CQ
J-)
•H
r-l
•H
•O
0}
M
•H
fc
I
rH
0)
•H
W
O
W
C
o
•H
en
w
•H
U
M
(U
S
i
ffi
a;
tO
E E
3 S
E E
ii
-ll»
E s s °
°'!l*
ill
0 g 0
if!-!
*" • » W
Ih
o o o
11"
in
1!-.!
SB. 0-
£ VI
|| j
- ^ "«
I|l
p. a
° :i
I? 1 2
ilii
s
0
w
s?
<£>
O)
00
(O
(O
CM
(O
CM
(0
CO
(O
00
O
C
^
D
U
ffi
_ O
a ^
c c
a> a
Is
UJ U
CN
CO
CM
CO
CO
CO
00
CO
00
CO
in
00
CO
O5
o
•o
c
a
"5
o
u
_c
1
s
E
Jo
S
o>
f,.
(O
(O
(D
0)
in
o>
in
O
O)
in
00
a>
c
^.
g
i
"5
S
Is
UJ 0
CN
0>
CO
CO
CO
5
^
o
(0
5
O)
0>
"a
b
£•
i
E
.S
a
*
(D
(B
O
o
c
2-
c
1
s
c
o
H
^
Z
Z
Z
Z
o
o
o
o
Z
«-
•5
.£
£•
a
E
u
'c
_
a.
E
(0
(0
0)
o
•a
0)
^-*
u
0)
•o
>•
3 «
"1-
0) Q-
E •
E ro
V V
iD E
H-14
-------
H.2.3.3.3 Arsenic. Total arsenic was measured at nearly
all sites, but only one site provided data on the presence of
arsenic (III) and arsenic (V). Two tests were made at that site.
Total arsenic had a median value of 2.25 lb/1012 Btu and a range
of 4 x 1CT2 lb/1012 Btu to 104 lb/1012 Btu. The median value for
arsenic (III) was 4.86 x 10"1 lb/1012 Btu and for arsenic (V) was
7.72 x 10"1 lb/1012 Btu, or about half again as much arsenic (V) .
The discrepancy between the median value for total arsenic and
the sum of the median values for arsenic (III) and arsenic (V)
may be due to using different test methods for total and for
speciated arsenic. High and low values for arsenic (III) and (V)
were 3 . 03 x 10'1 lb/1012 Btu and 6.69 x 10'1 lb/1012 Btu, and 1.70 x
10'1 lb/1012 Btu and 1.38 lb/1012 Btu, respectively.
H.2.3.3.4 Nickel. Total nickel was measured at nearly all
sites, but only two sites provided data on speciated nickel. The
species measured were soluble nickel (water-soluble salts such as
nickel sulfate and nickel chloride), sulfidic nickel (such as
Ni3S2, NiS, and Ni3S4) , metallic nickel (including alloys) , and
oxidic nickel (including NiO, complex oxides, and silicates).
Total nickel had a median value of 5.45 lb/1012 Btu and a range
of 3.00 x 10'2 lb/1012 Btu to 2.15 x 103 lb/1012 Btu. Metallic
nickel was not detected at either site; the distribution of the
remaining three types of species, in lb/1012 Btu and in percent
of total nickel species, is shown in Table H-6.
H.2.3.3.5 Individual Test Site Data for Mercury and
Chromium Data. Individual test site data for mercury and
chromium data for each test contributing species information to
this report are given in Tables H-7 and H-8 for mercury and
Tables H-9 through H-ll for chromium.
When the one value of divalent mercury found in an oil-fired
boiler is removed, the percentiles for coal-fired boilers change
slightly, as shown in Table H-8.
Table H-10 shows the chromium data for four coal-fired
boilers. Table H-ll shows Chromium data for seven oil-fired
boilers.
H-15
-------
0)
rH
•H
O
OQ
i
rH
-H
O
T3
C
(0
(0
o
o
to
o
iw
03
u
•H
4->
CQ
•H
J->
(0
4->
W
-H
§
M
g
in
i
(D
iH
•s
E-"
EO E§
O O
g 5 -S 5
•ef 1 8
§ |||
•S *- "S
•el S '5
IS if
a o
"in
| § 11
fe a
a,a ""
CD
= °-S
S c .£
I § 8
£ ~"
C ° "w
.5 e .£
o o a>
S w g-
Is I
Ii°l
"5 —
S> e .£
C 01 O
CO O 0)
DC S; a
1 "
>• e S •=
S la § -
CB M. •g s ,x S <»
1 -§| 1
CA
.£
u
(A
100/66
0)
5)
CO
cn
CO
00
CO
cn
oo
CO
in
To
o
0
.£
E
3
1?
U £
CO
0)
O)
r-
^
CM
r-
t
co
£
"5
o
u
c
§ E
"j" Q (J
100/77
o
05
0
cn
0)
00
cn
CO
O
05
cn
co
CO
CN
CO
CD
o
u
c
E
hromiu
O
O
00
CO
o
T—
O
^
-
o
V—
CO
•*
"5
o
u
c
C c
"5 .2
ll
co
CD
ifl
O)
O
CO
O
CO
O
00
O
CO
rs.
r*
0
CO
cn
CM
£
_.
o
.£
E
hromiu
O
in
CO
o
CM
o
CM
o
CM
o
CM
CO
CM
o
CM
o
CN
r-
'5
c
SE
co .2
> c
co 5
x g
41
H-16
-------
Table H-6. Emission Statistics for Nickel Species From Two
Oil-Fired Utility Boilers (lb/1012 BTU/Percent of Species)
Statistic
Average
Maximum
Minimum
Standard deviation
Range
5th percentile
10th percentile
90th percentile
95th percentile
Soluble nickel
628/58
1,240/58
21.3/65
616/NA
1,219/7
31.5/58
63.1/58
1,740/58
2,050/58
Sulfidic nickel
37.8/3
73/3
2.60/8
49.8/NA
70/5
1.89/3
3.78/3
102/3
120/3
Nickel oxides
422/39
835/39
9.00/27
584/NA
826/12
21.1/39
42.2/39
1,170/39
1,380/39
Note: Data from two sites. Metallic nickel not detected. Examples of soluble nickel include water-
soluble salts such as nickel sulfate and nickel sulfide. Examples of sulfidic nickel include Ni3S2,
NiS, and Ni3S4. Examples of nickel oxides include NiO, complex oxides, and silicates. Most of
these compounds have a valence state of 2.
H-17
-------
Table H-J. Mercury Data for Nine Coal- and 1 Oil-Fired Boilers
Test site
1
2
3
4
5
6
7
8
9
10
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Elemental mercury (%)
31
24
85
71
18
9
54
88
96
Not detected
Divalent mercury (%)
69
76
15
29
82
91
46
12
4
up to 1 00
Summary Statistics
Average (%)a
Standard deviation (%)a
Range (%)a
Maximum (%)a
Minimum (%)a
5th percentile (lb/1012 Btula
10th percentile (lb/1012 Btula
90th percentile (lb/1012 Btu)a
95th percentile (lb/1012 Btu)a
62
33
87
96
9
0.170
0.340
9.23
10.9
38
33
87
91
4
0.106
0.212
4.55
5.24
Results from Site 10 are excluded.
Table H-8. Mercury Percentiles for Coal-Fired Boilers
Percentile
5th percentile (lb/1012 Btu)
10th percentile (lb/1012 Btu)
90th oercentile (lb/1012 Btu)
95th percentile (lb/1012 Btu)
Elemental mercury
0.170
0.340
9.23
10.9
Divalent mercury
0.118
0.235
4.73
5.40
H-18
-------
Table H-9.
Boilers
Chromium Data for Four Coal- and Seven Oil-fired
Test site
1
2
3
4
5
6
7
8
9
10
11
Fuel
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Elemental chromium
(%)
99.6
90
91
77
95
91
77
83
66
83
Not detected
Hexavalent chromium
(%)
0.4
10
9
23
5
9
23
17
34
17
up to 1 00
Summary Statistics
Average (%)a
Standard deviation (%)a
Range (%)a
Maximum (%)a
Minimum (%)a
5th percentile (lb/1012 Btu)a
10th percentile (lb/1012Btu)a
90th percentile (lb/1012 Btu)a
95th percentile (lb/1012 Btu)a
85
10
34
99.6
65 .8
0.67
1.34
42.8
51.1
15
10
34
34.2
0.4
0.082
0.163
4.07
4.76
8 Results from Site 11 are excluded
H-19
-------
Table H-10. Chromium Data for Four Coal-Fired Boilers
Test site
1
2
3
4
Elemental chromium
(%)
99.6
90
91
77
Hexavalent chromium
(%)
0.4
10
9
23
Summary Statistics
Average (%)
Standard deviation (%)
Range (%)
Maximum (%)
Minimum (%)
5th percentile (lb/1012 Btu)
10th percentile (lb/1012 Btu)
90th percentile (lb/1012 Btu)
95th percentile (lb/1012 Btu)
89
9
22
99.6
77.5
0.792
1.58
49.1
58.4
11
9
22
22.5
0.4
0.095
0.190
5.48
6.48
H-20
-------
Table H-ll. Chromium Data for Seven Oil-Fired Boilers
Test site
5
6
7
8
9
10
11
Elemental
chromium (%)
95
91
77
83
66
83
Not detected
Hexavalent
chromium (%)
5
9
23
17
34
17
up to 1 00
Summary Statistics
Average (%)"
Standard deviation (%)8
Range (%}a
Maximum (%)a
Minimum (%)"
5th percentile (!b/1012 Btu)a
10th percentile (lb/1012 Btu)a
90th percentile (lb/1012 Btu)a
95th percentile (lb/1012 Btu)a
82
10
29
95
66
0.297
0.594
13.0
15.0
18
10
29
34
5
0.074
0.148
3.33
3.85
8 Results from Site 11 are excluded.
H-21
-------
H.3 ENVIRONMENTAL CHEMISTRY AND FATE
The following sections provide a summary of the
environmental chemistry and fate of HAPs associated with
emissions from utilities. HAPs of interest include selected
PAHs, dioxins and furans, and trace metals.
H.3.1 Dioxins and Furans
This subsection discusses the processes that affect the
fate, degradation, and transformation of chlorinated
dibenzodioxins (CDDs) and chlorinated dibenzofuran (CDFs) in the
environment. In general, the discussion presented here focuses
on CDDs and CDFs as a group and not on individual dioxin and
furan congeners. Information is also presented on the homologue
groups of dioxins and furans (i.e., hexa-, hepta-, octa-
chlorodibenzodioxins, etc.) with discussion of congener-specific
fate and transformation processes limited to 2,3,7,8-TCDD.
H.3.1.1 Transportation and Fate. CDDs and CDFS are widely
dispersed throughout the environment by atmospheric transport and
deposition. During transport CDDs and CDFs partition between the
vapor and the particle-bound phase.3 Vapor pressure greatly
influences the extent to which CDD/CDF congeners are found in the
vapor phase. Table H-12 lists the vapor pressures and other
chemical properties of homologue groups of CDDs and CDFs. The
ratio of vapor- to particulate-bound CDDs and CDFs ranges from
6.69 for tetra-CDD (relatively high vapor pressure) to 0.02 for
octa-CDDs (relatively low vapor pressure)4
CDDs and CDFs can be physically removed from the atmosphere
through wet deposition, particle dry deposition, and gas phase
dry deposition.5 Wet deposition is very effective at removing
CDDs and CDFs from the atmosphere and can dominate removal
processes in areas of low suspended particulate matter. However,
in areas with high total suspended particulate matter, dry
deposition (gravity settling of particles) can dominate.6
As seen Table H-12, CDDs and CDFs have low water
solubilities and vapor pressures and high octanol water partition
coefficients (Kow) and organic carbon partition coefficients
H-22
-------
.
£
3
1
w
S
i "s
B. ^
a ~"
!
i
I
a
3
1
c
b
X
CO
»~
CO
CO
CD
b
X
CO
en
0
b
X
O
CM
CM
CO
O
8
00
CO
CM
CM
00
b
X
b
X
CM
CO
CO
in
CM
*
HpCDD
0
X
0
rs>
en
in
b
X
n
0
X
in
CM
00
00
g
*
Q
8
o
w
b
X
CO
00
CM
b
X
CM
't
O
X
in
CM
0
CO
0
CO
UL
O
O
0
X
CM
CO
b
X
CM
O
X
CM
§
CO
LL
Q
b
X
o
*~
b
X
CO
0
b
X
CO
CM
CD
•<*
CO
LL
Q
O
X
X
10
b
X
CO
in
o>
CO
o
o
*
LL
O
O
O
X
b
X
en
•"
00
00
(D
b
X
CM
N
O
X
in
tv
m
00
$
*
LL
Q
O
00
00
en
s
O
.a
p
.3 °
0 o
CD CM
| E
% o
=
If
>- o
o *-
£ «
•£ E
II
« s
C w
§1
li
a> >
?!
a c
5 S.
in IA
CD O
D. 2
11
ID O
•> m
CD CD
H-23
-------
(Koc).a,9>1° These chemical properties indicate that CDDs/CDFs are
strongly sorbed in soils.11 In subsurface soils-particularly in
soils with high organic carbon content—CDDs/CDFs show little or
no vertical migration, as they are resistant to both leaching and
volatilization. However, the mobility of CDDs/CDFs in soil has
been shown to increase with a carrier solution such as waste oil
or diesel fuel, although he research in this area is ambiguous.12
Because CDDs/CDFs are resistant to leaching and volatilization,
they are mainly transported to surface waters through soil
erosion.13
In the aquatic environment, CDDs and CDFs remain sorbed to
particulate matter, and dissolved CDDs/CDFs entering surface
waters will partition to suspended solids or dissolved organic
matter.14 Volatilization is not expected to be a significant
loss mechanism for the higher chlorinated CDDs/CDFs under normal
conditions.15 The primary removal mechanism for removing
CDDs/CDFs from the water column is through sedimentation and,
ultimately, burial in sediments. Research has shown that
concentration of solids settling through the water column was an
order of magnitude higher than the concentration in the suspended
particulate matter. This difference in the concentrations
indicates that the solids tend to scavenge CDDs/CDFs as they
settle. Once buried, little recirculation of CDDs/CDFs occurs
within the water column.16
Aquatic organisms are shown to bioaccumulate CDDs/CDFs when
exposed to contaminated sediments and to bioconcentrate CDDs/CDFs
dissolved in water.b'17-18'19'20'21 Although the pathways for
a The Kov is defined as the ratio of a chemical's concentration in the octanol phase
to its concentration in the aqueous phase of a two-phase octanol/water system. A
chemical's K^, gives a strong indication of its behavior in the environment.
Chemicals with low K^ values are considered hydrophilic and tend to have high water
solubilities and small soil/sediment adsorption coefficients and bioconcentration
factors. Conversely, chemicals with large K^ values are considered hydrophobic and
have the opposite tendencies.9 The K00 defines the extent to which an organic
chemical partitions itself between the solid and solution phases of a water-
saturated soil, runoff water, or sediment. It is the ratio of the amount of
chemical adsorbed per unit weight of organic carbon in the soil or sediment to the
concentration of the chemical in solution at equilibrium. Koc. is a useful parameter
because the degree of soil adsorption can affect a chemical's mobility in the
environment and other fate processes.10
Bioconcentration refers to the accumulation of a chemical from direct exposure to
water. In aquatic organisms, bioconcentration involves uptake via gills and skin;
elimination via gills, skin, urine, and feces; and metabolic transformation.17
Bioconcentration is important because a chemical that bioconcentrates in an
organism may affect ecological processes at aquatic concentrations that appear safe
for the organism according to bioassay criteria for acute or chronic exposure.18 A
bioconcentration factor (BCF) describes a chemical's tendency to bioconcentrate in
an organism and is defined as the ratio of chemical concentration in an aquatic
(continued...)
H-24
-------
accumulation are not fully understood, the accumulation may be
primarily food-chain-based since most of the CDDs/CDFs are
associated with sediments and dissolved organic matter. Most
likely, uptake starts with benthic organisms (e.g., mussels,
chironomids, etc.) through ingestion or filtering, and those
organisms (e.g., crabs, suckers, etc.) consuming benthic
organisms would then pass the contaminant up the food chain.22
H.3.1.2 Transformation and Degradation. CDDs and CDFs are
extremely stable in the environment under normal conditions.
CDDs/CDFs do not hydrolyze appreciably,23 and 2,3,7,8-TCDD is
recalcitrant to microbial degradation.24 2,3,7,8-TCDD is also
stable to oxidation in the ambient environment.25 The most
significant natural degradation of CDDs/CDFs is by
photodegradation.26 Photodegradation is slow in water and on dry
surfaces but increases in the presence of organic solvents.27
CDDs and CDFs photodegrade to lower chlorinated CDDs and CDFs.
In general, the rate of photolysis increases as the degree of
chlorination decreases and, within a congener group, as the
degree of ortho substitution decreases. Research has shown that
CDFs degrade much more rapidly than CDDs.28 In the soil,
photodegradation is limited to only the soil surface and is not
significant below the first few millimeters of soil depth.29
Atmospheric photodegradation of CDDs/CDFs is not well
characterized but appears to be the most significant mechanism
for degradation of CDDS/CDFs in the vapor phase. However,
measuring the rate of atmospheric photodegradation is difficult
because of the low volatility of CDDs/CDFs. For airborne
CDDs/CDFs sorbed to particles, photolysis appears to proceed very
slowly, and sorption of CDDs onto airborne particulate matter may
even act to stabilize CDDs from photodegradation. Research has
shown that the half-life of gaseous 2,3,7,8-TCDD was several
hours under simulated sunlight. When the 2,3,7,8-TCDD was sorbed
onto fly ash particles, Mill observed only an 8 percent loss of
2,3,7,8-TCDD after 40 hours of illumination.30
(...continued)
organism to that in water.19 Bioaccumulation also describes increases in the
concentration of chemicals in organisms. However, bioaccumulation may be
distinguished from bioconcentration in that it encompasses accumulation of a
chemical from all routes of exposure, including ingestion, and is not limited to
direct exposure to water or soil.20 Biomagnification is a related term and is the
systematic increase of a chemical in tissue concentrations at higher levels in the
food chain.21
H-25
-------
H.3.2 Polycyclic Aromatic Hydrocarbons
This section discusses the processes that affect the fate,
degradation, and transformation of selected PAHs in the
environment. PAHs selected were those that are probable
carcinogens and those that are present most frequently in utility
emissions. Probable carcinogens include benzo(a)anthracene,
benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(b and
k)fluoranthene,0 chrysene, benzo(a)pyrene,
dibenzo(a,h)anthracene, and indeno(l,2,3-c,d)pyrene; the most
frequently emitted PAHs are 2-methylnaphthalene, fluoranthene,
fluorene, pyrene, and phenanthrene.
H.3.2.1 Transportation and Fate. Transport and fate of
PAHs in the environment are determined to a large extent by the
physical and chemical properties of the individual PAH.
Table H-13 summarizes the properties of selected PAHs as
described in section H.3.2. Some of the properties correlate
roughly to the molecular weights of the PAHs, which can be
grouped into the following categories:
• Low-molecular-weight compounds (142-178 g/mol)--
2-methylnaphthalene, fluorene, and phenanthrene;
• Medium-molecular-weight compounds (202 g/mol)--
fluoranthene and pyrene; and
• High-molecular-weight compounds (228-278 g/mol)--
benzo(a)anthracene, benzo(b)fluoranthene,
benzo(k)fluoranthene, chrysene, benzo(a)pyrene,
dibenz(a,h)anthracene, and indeno(1, 2,3-c,d)pyrene.
Much of the discussion of the transport and fate of PAHs is based
on these categories.31
PAHs in the atmosphere are present in the gaseous phase or
sorbed onto particles. The ratio of particulate to gaseous PAHs
varies for different PAHs. For example, the results of a study
of air samples collected in Antwerp, Belgium, indicated that the
ratio of particulate to gas phase PAHs ranged from 0.03 for
anthracene to 11.5 for benzo(a)fluoranthene and benzo(b)
fluoranthene combined. Additionally, atmospheric residence time
and transport distance depend on the size of particles to which
PAHs are sorbed. Larger particles released from urban sources
deposit close to their source of origin and can become part of
urban runoff. Conversely, smaller submicron-diameter soot
Information on the environmental chemistry and fate of benzo (b and k) fluoranthene
was not readily available from the literature for this study.
H-26
-------
U
o
in
CN
w
ffi
•O
0)
U
0)
rH
a)
w
4-1
O
W
0)
•H
JJ
0)
o
04
rH
10
U
•H
0)
u
u
-H
CO
I
K
0)
rH
43
(0
I
C ?
8 5
II
z
**
H)
0
^
o
3
en
E
a
3
O
CO
1
a
£
S z
S.
a
a
\
cp
II
o
S
1
•
to
b
X
in
b
CO
in
r_
o>
in
q
d
6
X
CM
CO
CO
CM
CM
Benz(a)anthracene (c)
•o
O
X
CM
CM
*~
S
m
0
00
in
in
o
O
d
u
b
2
*~
o
f
u
CN
CO
CM
in
CN
u
U Benzo(b)fluoranthene I
S
r*
b
X
CO
^~
£
in
CO
o
CO
CO
o
8
d
u
0
b
X
cn
CO
CM
CO
CM
in
CM
u
|| Benzo(k)fluoranthene 1
3
0
X
CO
b
CO
in
in
CO
"
b
X
CM
CM
1
in
b
X
CO
•*
&
CO
CN
CN
II Chrysene (c)
.
b
X
*
£
co
O
CO
00
CO
o
o
d
n
b
CO
in
u
CN
CO
CM
in
CM
II Benzolalpyrene (c)
,
•
b
^<
CO
^
CM
in
CO
in
rv
CD
CD
O
8
d
0
b
X
00
CM
CO
CO
CO
CM
O
|| Dibenz(a,h)anthracene
•o
0
V—
X
in
0)
CO
b
CM
CO
CO
in
CO
CM
CO
o
d
b
o
o
CM
to
o
0
1
00
CO
CO
CM
"S
c
CD
1
0
CO
CM"
I
CD
I
•
b
X
in
O
in
CO
cn
CO
*
CO
CD
00
CO
in
CM
CO
q
d
u
CM
CM
;T-
2-Methylnaphthalene {
S
0
X
"-
•0
CO
CO
CO
CO
*
b
cn
u
b
in
rv
CO
CM
CO
CD
II Fluorene (f)
.
0
b
X
cn
CO
in
^
CO
^
in
CN
CO
d
0
X
in
*
CO
CM
CM
O
CM
O
. 0)
si
•B E
fc— (/)
2 w
O)*;
||
CD g^
9) 0)
o cn
c a.
« y
^ _Q)
•g »
= «
cn •£
co ^
P °
E >•-
cn cn
5S
•D C
C O
— o
fc 5
.. CD
c —
V cn
O £•
C C
•5 «
co •*•
O T3
•g i«^
Q. D O3
cn cn ^
= « — . .
<° £ < CN CO
cn - - ~ ~
5 t
CD "
3
3
•o
CD
eference
eference
lcula
CO
5P- OC QCU
H-27
-------
particles associated with urban air have residence times of weeks
and are subject to long-range transport. PAHs can be removed
from the atmosphere by wet and dry deposition, and the relative
importance of each removal process varies according to the PAHs
involved. For example, dry deposition of benzo(a)pyrene accounts
for three to five times more removal than does wet deposition.34
In water, PAHs can be removed by volatilization to the
atmosphere and by sorption onto suspended and bottom sediment.
Low-molecular-weight PAHs, which have Henry's law constants
ranging from 10"3 to 10"5 atm-mVmol, are associated with
significant volatilization from water. Only limited
volatilization occurs for medium- to high-molecular-weight PAHs,
which have Henry's law constants of less than 10'5. However, as
exhibited by their Koc values in Table H-13, high-molecular-
weight PAHs strongly sorb onto the organic carbon present in the
suspended and bottom sediments. Low-and medium-weight PAHs have
only a moderate potential for sorption onto organic carbon.35
PAHs in soil can be removed by volatilization.
Volatilization of low-molecular-weight PAHs may be substantial.
In one study, volatilization was found to account for 20 percent
of 1-methylnaphthalane and 30 percent of the loss of naphthalene;
both compounds are low molecular weight PAHs. Volatilization of
higher molecular weight PAHs is not expected to be a significant
removal process from soil.36
Because of the tendency of high-molecular-weight PAHs to
sorb strongly to soil, they are resistant to leaching. However,
leaching can occur in the lower-weight PAHs. In a study
conducted on land cleared by burning, PAHs were found to move
into the soils by partitioning and leaching. Phenanthrene and
fluoranthene at the site were incorporated into the soil to a
greater extent than the higher-molecular-weight PAHs, such as
indenod, 2, 3-c,d)pyrene.37
Plants and animals can bioconcentrate PAHs. Table H-14
provides BCFs of selected species for the PAHs chosen due to
suspected carcinogenicity and frequency of occurrence in emission
tests. Generally, bioconcentration factors (BCFs) are larger for
the higher-molecular-weight PAHs. In water, bioconcentration and
bioaccumulation of PAHs vary greatly depending on the type of
aquatic organism. Fish and crustaceans readily bioaccumulate
PAHs from contaminated food, whereas mollusks and polychaete
worms have limited assimilation. Biomagnification is the
systematic increase in tissue concentrations at higher levels in
H-28
-------
Table H-14. PAH Bioconcentration Factors (BCF) for
Selected Speciesa/b
PAHs"
Benz(a)anthracene (c)
Benzo(b)fluoranthene (c)
Benzo(k)fluoranthene (c)
Chrysene (c)
Benzo(a)pyrene (c)
Dibenz(a,h)anthracene (c)
2-Methylnaphthalene (f)
Fluorene (f)
Pyrene (f)
Fluoranthene (f)
Phenanthrene (f)
BCF
10,233
10,000
9.1
10,000
14.1
13,183
14.8
6,095
20,417
2,818
12,882
54,954
8,912,509, 3,235,936
13.8
10,000
2,399
407, 447
502
5.20
2,692
44,668
5.7
1,738
79,433
5.6
324
15,136, 19,055
Species
Daphnia magna
Fathead minnow
polychaet sp
daphnia magna
polychaet sp
daphnia magna
Polychaet sp
Daphnia magna
P. hoyi
Daphnia magna
Daphnia magna
P. hoyi
P. hoyi
Polychaet sp
daphnia magna
algae
None cited
Daphnia magna
Polychaet sp
Daphnia magna
P. hoyi
Polychaet sp
Daphnia magna
P. hoyi
Polychaet sp
Daphnia magna
P. hoyi
No BCF was available for indeno(1,2,3-c,d)pyrene.
All references cited are from Mackay, et al, 1992 (Reference 32).
(c) indicates suspected carcinogen; (f) indicates measured frequency of detection greater than 10. (See Table H-2)
(Reference 8).
H-29
-------
the food chain, and because many aquatic animals such as mollusks
rapidly eliminate PAHs, biomagnification of PAHs has not been
reported in aquatic animals.38
Some terrestrial plants can uptake PAHs through the roots or
foliage. Factors that influence the uptake rate of plants
include concentration of PAH in soil, the solubility and the
molecular weight of PAHs, and the plant species. Evidence
suggests that plant uptake of PAHs from deposition on foliage
greatly exceeds uptake from roots. Research has shown that about
30 percent to 70 percent of atmospheric PAHs deposited into a
forest were sorbed onto the leaves and needles of the trees and
then deposited as litterfall. Furthermore, PAHs can accumulate
in terrestrial animals through the food change or by soil
ingestion.39
H.3.2.2 Transformation and Degradation. PAHs in the
atmosphere react with a variety of pollutants such as ozone, NOX,
S02, and peroxyacetylnitrate. Reactions of PAHs with ozone or
peroxyacetylnitrate yield diones40 (compounds having two ketone
groups). Nitro and dinitro PAHs are formed from reactions with
NOX. S02 reacts with PAHs to form sulfonic acids.41
Additionally, some PAHs have been shown to degrade by oxidation
reactions.42
Photodegradation of many atmospheric PAHs forms nitrated
PAHs, quinones, phenols, and dihydrodiols. Some degradation
products are mutagenic. PAHs absorbed onto particulates with
high organic carbon are more resistant to photodegradation than
the pure compounds. The dark, high-organic-content particulates
on which the PAHs sorb may absorb more light, thereby making less
light available for photodegradation.43
Photodegradation is also an important degradation process of
PAHs in water. The rate and extent of photodegradation vary
widely among PAHs. For example, one study reported that
anthracene, phenanthrene, and benzo(a)anthracene were susceptible
to photodegradation in water, while chrysene, benzo(a)pyrene,
fluorene, and pyrene were resistant and are also dependent on
water depth, temperature, and turbidity. The most common
products of PAH photodegradation in water are peroxides,
quinones, and diones.44
PAHs can be chemically oxidized in water by chlorination.
Research has shown that pyrene and benzo(a)anthracene degrade
rapidly upon chlorination. Indeno(1,2,3-c,d)pyrene has an
intermediate degradation rate. Of the chemicals considered in
this study, benzo(k)fluoranthene and fluoranthene were the
H-30
-------
slowest to degrade. The byproducts of PAH chlorination are not
fully known but include anthraguinone, a chlorohydration of
fluoranthene, and monochloro derivatives of fluorene and
phenanthrene.45
PAHs in water can also be oxidized by ozonation. However,
ozonation is generally slower and less efficient than
chlorination in degrading PAHs. Some products of PAH ozonation
include benzo(a)anthracene to 7,12-guinone; benzo(a)pyrene to
3,6-, 1,6-, and 4,5-diones; and fluorene to fluorenone.46
Biodegradation is another removal process of PAHs from
water. Bacteria degrade PAHs into cis-dihydrodiol via a
dioxetane intermediate, and trans-dihydrodiols are formed through
arene oxide intermediates in fungi. Arene oxides have been
linked to the carcinogenicity of PAHs. Additionally, algae'were
found to transform benzo(a)pyrene to oxides, peroxides, and
dihydrodiols.47
Microbial metabolism is the major degradation process of
PAHs in soil. Photolysis, hydrolysis, and oxidation are not as
significant. However, evidence suggests that photolysis and
volatilization may be important for PAHs containing fewer than
four aromatic rings.48
The rate and extent of biodegradation in soil are affected
by environmental factors, the physical and chemical properties of
the PAHs, and the level of contamination at the site.
Environmental factors include temperature, pH, oxygen content,
soil type, moisture, and the presence of nutrients and other
substrates that can act as cometabolites. In one study, the type
of PAH and its properties also influenced the biodegradation
rate. Biodegradation half-lives of PAHs composed of two and
three aromatic rings ranged from 2 to 60 days, while PAHs with
four and five aromatic rings had half-lives greater than
300 days.49 In another study, the mean half-lives correlated
positively with Kow. The rate of biodegradation may also be
altered by the degree of contamination at the site. Half-lives
may be longer at highly contaminated sites, since other
contaminants may be lethal to microbes.50 Table H-15 gives
half-lives of selected PAHs in soils and other environmental
compartments.
For some PAHs, the products of microbial degradation in soil
are well known, while for others only limited information is
available. As in water, bacteria degrade PAHs in soil to
cis-dihydrodiols through dioxetane intermediates, and fungal
H-31
-------
in
»n
i
W
Q)
S
•••
'5
5(0
£
|
1
I
H
„
•i
^
Type/conditions
I
n
g
O
^O
H
1
«
3
0.
1
'o
CD
a
CO
s
Z
•o
o
in
CM
to
4
Based on estimated
photolysis in water
CO
.t
CO
r -o .22 c
0 |J. B
W *43 O !±
(0
||
m 'I
CQ in
0
CM
CO
co"
7 -c
CO
5
CM
Direct photolysis
CO
o
d
(0
to —
C g
CO *
II
^ _
•D
co
CM
No sediment water
partitioning/direct
photolysis
•o
o
CM
0
>«
re
(A
•c ^
0 I
2 «
TJ
CM
(O
«—
Sediment water
partitioning/direct
photolysis
Ground water, based
on aqueous aerobic
biodegradation
half-life
o
^
TJ •*-* ^ m
-o § § i
8 |S~
(0 CA «C (0
CQ CD Q. £
£
to
2
CO
2
i
CD
O
O
.3
^
2
"o
M
1
>•
•g
re
10
c —
1 g
0 1
2 o
T5
^
CM
Ground water, based
on aqueous aerobic
biodegradation
half-life
CO
c .,
.2 o
** O
11
O o
CO
0
CD
£
1
O
3
«•—
.£
"o
N
C
cS
0 |
§ j
re 'o
00 (A
, _£
° °
co co
^ *
CM in
Based on photolysis
half-life in water
.C
en
en
CO
CO
c
.2
re
Ills
-------
•8
•H
I
O
in
H
Z
| Type/conditlo
2
>
^fl
I
^
O.
Not specified
™
in
in
A
Direct photolysis
^
^
^
_g
Based on
estimated
photooxida
half-life
CM
q
co
CM
o
00
d
1
CO
10 —
c o
co <»
II
«2 5
•o
CO
No sediment water
partitioning/direct
photolysis
^
CO
«—
>.
McLaurin sand
loam soil
T3
00
CO
Sediment water
partitioning/direct
photolysis
T3
CO
CO
|s
!!
_£
CO
^
O t)
"S 03
0 ^_
5 i
C CO
CO O
CO co
£
O
o
q
CM
4
o
05
CO
•a ..
Groundwater, base
on aqueous aerobic
biodegradation
half-life
£
o
o
o
CO*
00
0
-
^^
u^
CD
C
CD
CO
^
U
Not specified
CM
A
No sediment
partitioning
T3
CM
CO
M^
(0
c TJ .S2
b OJ CO .t
0 IS -£• 5
^ 1 S.£
|S J «
CD 05 Q. :=
£
^
^
CO
d
•o
CD (o
CO H;
(5 »
"D (0
5 .2
•n
O)
O
co
With sediment
partitioning
•a
CO
T—
>-
McLaurin sand
loam soils
T3
05
CN
CN
Based on photolysi:
half-life in water
_£.
^~
,J
1
CO
d
o 5
!5 ®
0 ~
§ |
if
co '5
CO (A
0
CM
f**
CM
CO
co
CO
*~
Based on estimatec
aqueous aerobic
biodegradation half
life
£.
0
^
^"
in
CM
1
CD
CO
CM"
CO
CD
11
E- §.
lo
™ w
o c
LL O
^
in
d
CD
C
CD
Q.
"^0
O
N
CD
00
H-33
-------
•0
o
•H
I
u
in
H
i
0
H
•8
H
'o
1
(D
,t
s
i
1
1
Type/conditions
•5
>
_
o
JZ
.«
I
1!
3
"5
*v.
^
CL
>-
•c
CO
CO •_
Kinman
loam so
CD
CO
Based on photolysis
half-life in water
.c
CN
00
co
"O
c
co
to
c —
1 g
2 E
•D
o
CM
aw
JK
Q **
• io"
_ 5
c co
CO Q
00 co
O
co
to
CN
co
CO
00
Ground water, based
on aqueous aerobic
biodegradation
half-life
to
O
CM
t—
in
^f
ob
CM
".
r*-"
c
.g
03
f "^ "^
jp QJ "v
^ +* o CD
CD CO .C CO
CD CD Q. Jl
00
CM
^
CO
CN
d
CD
O
CO
£.
c
_jo
•C^
JO
I
&
o
CO
•§
0
CO
4-1
CO
T3
o
• 4- tl
f|l CD 1
i ~2 |^
1 I ?'S *.?JB
* 1 i 1 | § i
o g S S .i 2 S
£ !; r o -o t o
•- O CO J= CD CO JI
O Z a a co a. Q.
j= -D T5
^•0 o
» 5 I
CO
to
•o
o
Z
ID
CO
•o
O
Z
C1
i
CD
co
f-
a
CO
_c
^,
4^
CD
^
CN
o Tt>
~ ^~*
® i?
" I
14
to rs
CO o
CD co
^T
O
t—
CO
CO
Surface water, based
on aerobic soil
dieaway test
£i
O
^
06
co
Ground water, based
on aqueous aerobic
biodegradation
half-life
_£-
00
CO
CN
CO
CO
10
c
o
+J
(C
(- "O "O
5 fl) "x
O +-> 55 flj
(0 O **1
•C J^ O •""•
03 fc +J ^^
w -«3 o *:
co to .c co
oa CD a j=
^
^
00
CO
1
00
CO
^^
•**•
CD
i
o
3
LL
H-34
-------
•8
B
•H
I
U
in
H
0
H
•8
'5
w
1
^
£
5
Type/condit
i
Type/conditions
0)
1
,
I
g
1
H
§
>
•co
Z
<
Q.
•D
CD
'o
0)
0.
CO
4-*
o
.c.
in
CO
CO
Direct photolysissis
00
in
d
o to
"5 ®
i ^
P
co :=
(0 o
CO co
j.
is
O CD
in in
No sediment water
partitioning/direct
photolysis
TJ
CN
T3
CO
CO —
m co
II
2 J2
T3
0
co
CM
Sediment water
partitioning/direct
photolysis
T3
05
in
>
•D
CO
CO
C ss
'C O
3 CO
10 _
_i E
II
•o
O)
O5
Groundwater, based
on aqueous aerobic
biodegradation
half-life
, £
f|
"~ 05
CD
to
3
.. -o .52 c
5 CD W —
0 •£ > «
_ to — *^
CO (0 £ CO
CD c Q. £
i -C
00 *±
o S
**-
CD
C
CD
5^
°-
1
U-
'i
Q.
to
4-*
O
•o
CM
00
^
Direct photolysis
CM
o to
!A CD
co *"
O '
CD "^
co :=
CO o
03 to
_,.
6 o
co co
co in
co" o"
No sediment water
partitioning/direct
photolysis
T3
O
CO
•o
CO
CO —
S 8
II
•o
CO
Sediment water
partitioning/ direct
photolysis
•o
o
o
CM
>
•a
to
to
c =
~ o
3 CO
2 E
II
T3
00
CO
CM
Groundwater, based
on aqueous aerobic
biodegradation
half-life
.c
6 o
CM CM
to" <-"
CM
«
03
c -o .2 c
§§£>'«
TT ^ ••=. — H-
^J ^ OJ Q '— •
CD t s: <-" ~
co -43 c O *:
CO (0 3 f to
CD CD « Q. .C
^:
CM
d
CM
CM
q
CM
c
CD
a
6
3
LL
H-35
-------
1
§
U
in
H
I
'o
S
a
5
|
1
1
£
1
Type/conditions
1
(A
|
e
u
1
^
g
•_
3
^
a.
•o
o
1
<0
4-(
O
•o
CO
CN
in
CN
Direct photolysis
.c
00
u to
s >
I |
CD O
CO to
£
1 Q
00 O
CO »
No sediment wate
partitioning/direct
photolysis
TJ
CO
in
T)
CO
CO —
i S
ii
i
CO
t
CO
<0
C =
*w O
3 co
a _
II
TJ
»—
•§ o
Groundwater, bas
on aqueous aerobi
biodegradation
half-life
I
co
o>
00
co
g
ro
•o c o =
CD E <-• ~
co -43 o *:
CD (0 ^ (D
oo CD a .c
JI
CD
CN
q
CN
CO
c
£
^
to
c
J=
a.
_CD
.Q
CD
05
C
CO
CO
CD
D>
C
g
o
CD
O
C
CD
•o
CD
(A
CO
CD
£
CO
o
XJ
CD
O)
O
C
'5
CO
o
o
CD
Q.
M
CO
CD
*J
CO
u
H-36
-------
degradation produces trans-dihydrodiols through arene oxide
intermediates. Arene oxides have been linked to the
carcinogenicity of PAHs.51
H.3.3 Trace Metals
The environmental chemistry and fate of trace metals emitted
from utility boiler stacks are discussed in the following
sections. Trace metals include arsenic, cadmium, chromium, lead,
mercury, and nickel.
H.3.3.1 Arsenic. This section discusses the processes that
affect the fate, degradation, and transformation of arsenic in
the environment.
H.3.3.1.1 Transportation and Fate. Arsenic is found as a
component of sulfidic ores and as arsenides or diarsenides of
metals. Trace levels of arsenic are found in soils and rocks.52
In the environment, arsenic can be transported by wind or water
erosion of small particles and by leaching into rainfall or
snowmelt. However, leaching transports arsenic only short
distances, because many arsenic compounds readily absorb to soils
and sediments.53
Arsenic in the atmosphere exists as particulate matter,
primarily with diameters less than 2 urn. A typical residence
time of particulate arsenic is 9 days, depending on the particle
size and meteorological data. Arsenic is removed from the
atmosphere by wet or dry deposition.54
Transport and partitioning of arsenic in water depend upon
the chemical form of arsenic and on its interactions with other
substances in the water. Soluble forms of arsenic can be
transported long distances in rivers and water. However, arsenic
can be adsorbed onto suspended and bottom sediments in water.
Chemical and biological interconversions of sediment-bound
arsenic may return arsenic to the water.55
Bioconcentration of arsenic occurs in aquatic organisms,
primarily in algae and lower invertebrates. The BCFs for several
arsenic compounds have been shown to range from 0 to 17 for
freshwater invertebrates and fish, and a BCF of 350 was observed
in marine oysters. Although some fish and invertebrates contain
high levels of arsenic, biomagnification in aquatic food chains
does not appear to be significant. Terrestrial plants accumulate
arsenic by root uptake from soil or by absorption of airborne
arsenic deposited on the leaves, and certain species may
accumulate substantial levels.56
H-37
-------
H.3.3.1.2 Transformation and Degradation. Arsenic is
released into the atmosphere primarily as arsenic trioxide and,
less frequently, in arsines or in one of several other volatile
organic compounds. Atmospheric arsenic is typically a mixture of
trivalent and pentavalent arsenic. Trivalent arsenic and methyl
arsenic oxidizes to the pentavalent state in the atmosphere.
Photolysis of atmospheric arsenic compounds is insignificant.57
Arsenic in water can undergo a complex series of
transformations, including oxidation-reduction reaction, ligand
exchange, and biotransformation. Rate constants for these
various reactions are not readily available, but the factors that
most strongly influence fate processes in water include the
oxidation-reduction potential, pH, metal sulfide and sulfide ion
concentrations, iron concentrations, temperature, salinity, and
distribution and composition of the aquatic biota. Arsenic in
water exists predominantly as arsenate, but aquatic
microorganisms may reduce the arsenate to arsenite and a variety
of methylated arsenicals.57 As in the atmosphere, trivalent
arsenic in aerated surface waters can be oxidized to pentavalent
arsenic, while pentavalent arsenic in the aquatic environment can
be reduced to trivalent arsenic in the presence of an oxidizing
agent at acidic conditions.58
Transformations of arsenic in soil are similar to those
occurring in aquatic systems. Pentavalent arsenic predominates
in aerobic soils, and trivalent arsenic predominates in slightly
reduced soils, such as temporarily flooded areas. Arsine,
methylated arsenic, and elemental arsenic are the predominant
forms found in extremely reduced soils such as swamps and bogs.59
H.3.3.2 Cadmium. This section discusses the processes that
affect the fate, degradation, and transformation of cadmium in
the environment.
H.3.3.2.1 Transportation and Fate. Cadmium released from
the combustion of coal and petroleum products is usually
associated with small particles that are in the respirable range
of less than 10 urn in diameter.d/6° These small particles are
persistent in the atmosphere and can be transported great
distances, with typical residence times from 1 to 10 days. Wet
and dry deposition (gravitational setting) are the primary
removal mechanisms for cadmium in the atmosphere.60 Cadmium
compounds usually found in the atmosphere are oxide, sulfide,
sulfate, and chloride.61
Only limited data were analyzed for this report concerning the size distribution of
cadmium emitted from utility boilers. As a result, comparing the current data to
the value cited in the reference (reference 60) is not appropriate.
H-38
-------
Cadmium is relatively mobile in water when compared to other
heavy metals. Most cadmium in water exists as the hydrated ion
(Cd(+2) • 6H20) . Cadmium complexed with humic substance is
another important form of cadmium in water. The concentration of
cadmium in water is usually inversely related to the pH value and
the amount of organic material present. Cadmium is removed from
water by sorption onto organic materials and mineral surfaces,
and sediment bacteria may assist in the partitioning of cadmium
from water to sediments. Cadmium does not volatilize from the
aquatic environment.62
Cadmium is readily released from acidic soils and is
susceptible to leaching. Soil particles containing bound cadmium
also may erode into air or water.63
Cadmium is bioaccumulated at all levels of the food chain
in the aquatic environment. Cadmium accumulates in freshwater
and marine organisms at concentrations hundreds and thousands of
times greater than in the water. Bioconcentration of cadmium is
greatest for invertebrates, followed by fish and aquatic plants.
BCFs range from 113 to 18,000 for invertebrates and from 3 to
2,213 for fish.64 In terrestrial animals, cadmium accumulates at
all levels of the food chain. However, data on the
biomagnification of cadmium are not conclusive.65
H.3.3.2.2 Transformation and Degradation. Limited
information is available on cadmium reactions in the atmosphere.
Cadmium compounds in the atmosphere (oxide, sulfate, chloride)
are stable and do not undergo chemical reactions quickly.
Photodegradation is insignificant for atmospheric cadmium.
However, atmospheric cadmium compounds can be transformed by
solubility in water and dilute acids.66
Because cadmium is present predominantly in the 2 +
oxidative state in water, aqueous cadmium is not strongly
influenced by the oxidation or reduction potential of water.67
However, cadmium sulfide, which is formed under reducing
conditions, has a low solubility and tends to precipitate out in
water.68 Photolysis and biological methylation of cadmium is not
likely to occur in water.69
Transformation of cadmium in soil includes precipitation,
dissolution, complexation, and ion exchange. Transformation is
affected by the pH; the soil content of clay, minerals, and
organic matter; and the presence of carbonated materials, oxides,
and oxygen.70
H-39
-------
H.3.3.3 Chromium. This subsection discusses the processes
that affect the fate, degradation, and transformation of chromium
in the atmosphere.
H.3.3.3.1 Atmospheric Transportation and Fate. Chromium
exists in the atmosphere primarily in particulate form. The mass
mediam diameter of particulate chromium is approximately 1 urn in
the ambient atmosphere. Chromium is removed from the atmosphere
by wet and dry deposition. However, the size of the chromium
particles and chromium's mean deposition velocity of 0.5 cm/s
favor dry deposition. Wet deposition can occur, and acid rain
may facilitate removal of acid-soluble chromium compounds.
Chromium in water cannot be volatilized into the atmosphere, but
chromium in soil may be transported to the atmosphere as an
aerosol.71
H.3.3.3.2 Transformation and Degradation in the
Atmosphere. Chromium occurs in the atmosphere primarily in two
oxidative states, chromium (III) and chromium (VI).72 In natural
systems these are also the two most stables forms of chromium.73
Chromium released from combustion processes and ore processing
industries is present mainly as chromium (III) oxide (Cr203),74
and reports show that chromium (VI) makes up approximately 0.2
percent of the total chromium emitted from power plants.75
Chromium (VI) is readily reduced to chromium (III) by
vanadium (V2+, V3+, or V02+) , iron (Fe2+) , hydrogen sulfite (HSO3~) ,
and arsenic (As(III)). Conversely, the oxidation of Cr(III) to
Cr(VI) may occur at a significant rate in the atmosphere only if
chromium is found in a salt other than Cr203, in the presence of
manganese oxide.76 The time required for these reactions to
occur is unknown, given all the other species present.77
H.3.3.4 Lead. This section discusses the processes that
affect the fate, degradation, and transformation of lead in the
environment.
H.3.3.4.1 Transportation and Fate. Atmospheric lead
exists primarily as lead sulfate (PbS04) and lead carbonate
(PbC03) ,78 and it is predominantly found in the particulate phase.
The transport of lead in the atmosphere is affected by the size
distribution of lead particles. Particles larger than 2 um in
diameter tend to settle out of the atmosphere quickly and are
deposited near their source of origin. Smaller particles can be
transported great distances and can also coagulate into larger
particles.79
Wet and dry deposition is the primary removal mechanism of
lead from the atmosphere. Of the two types, wet deposition is
more important, with approximately 40 to 70 percent of removal
H-40
-------
resulting from wet deposition. For instance, the ratios of wet
to dry deposition were calculated as 1.63, 1.99, and 2.50 in a
study conducted at various sites in Canada.80
The concentration of dissolved lead is low in most surface
waters. At the proper pH, lead will readily react with anions,
such as hydroxides, carbonates, sulfates, and phosphates, to form
compounds that precipitate out of water (section H.3.3.4.2),
which limits its soluability. Lead is also removed frcm water by
binding with mineral particles in suspended sediment. The ratio
of lead in suspended solids to dissolved lead varies from 4:1 in
rural streams to 27:1 in urban streams.81
Lead readily adsorbs to the organic matter in soils.
Generally, lead is strongly retained in soils and resistant to
leaching, so that little is transported into surface or
groundwater. However, lead can enter water as a result of
erosion of soil particles containing lead, and leaching of lead
can occur in acidic soils. As in water, lead can form many
different complexes in soil depending, in part, on the soil type
and pH, and the mobility of the different forms of lead in soil
varies. At a pH of 6 to 8 and in high-organic soils, lead may
form insoluble organic lead complexes. In acidic soils, these
organic lead complexes can become soluble and are subject to
leaching. In soils with less organic mater and a pH of 6 to 8,
lead can form hydrous lead oxide complexes or may precipitate out
with carbonate or phosphate ions. Additionally, lead at the soil
surface may be converted to lead sulfate, which is relatively
soluble when compared to lead carbonate or phosphate.82
Lead bioconcentrates in plants and animals. Terrestrial
plants uptake lead via roots and foliage, and animals can intake
lead via inhalation or ingestion of plants or soil. In the
aquatic environment, higher BCFs are usually found in algae and
benthic organisms. Low BCFs are associated with upper-level
predators, such as carnivorous fish. Thus, biomagnification of
lead does not occur. In terms of toxicity to organisms,
organolead compounds are more toxic than inorganic forms of
lead.83
H.3.3.4.2 Transformation and Degradation. Only limited
information is available concerning the chemistry of lead in air.
Lead oxides in the atmosphere form lead carbonates and sulfates.
Trialkyl and dialkyl lead compounds can decompose into organic
lead by a combination of photolysis and reactions with hydroxyl
radicals and ozone.84
The chemistry of lead in water is complex because lead can
be found in a multiplicity of forms. At the proper pH, lead can
produce insoluble compounds with many of the anions found in
H-41
-------
natural waters. Hydroxide, carbonate, sulfide, and sulfate may
act as solubility controls in precipitating lead from water.
Lead forms lead sulfate in the presence of sulfate ions at pH
less than 5.4, while lead carbonates form at pH greater than 5.4.
As noted previously, these lead compounds are insoluble and tend
to precipitate out of solution.85
Organic lead compounds undergo a variety of degradation and
transformation reactions in water. In anaerobic lakes,
biological alkylation of organic and inorganic lead by
microorganisms may form the volatile organometal lead compound
and tetramethyl lead. In aerobic waters, the tetramethyl lead
oxidizes, thereby reducing its volatilization from sediment.
Tetraalkyl lead compounds photodegrade to trialkyl compounds, and
degradation proceeds to dialkyl lead and then to inorganic lead.
Tetraethyl lead is also susceptible to photodegradation in the
water.86
In soil, evidence suggests that atmospheric lead is
deposited on the soil as lead sulfate or is converted rapidly to
lead sulfate at the soil surface. As noted previously, lead
forms different compounds depending on the soil type and the pH
(section H.3 .3 .4.1) ,87
H.3.3.5 Mercury. This subsection discusses the processes
that affect the fate, degradation, and transformation of mercury
in the environment.
H.3.3.5.1 Transportation and Fate. Mercury can exist in
three oxidation states in the environment: Hg° (elemental), Hg22+
(mercurous ion) , Hg2+, (mercury II, divalent mercury) . An
analysis of stack test results conducted for this report shows
that mercury is found in stack emissions in both the elemental
and ionic form (See Appendix H-2). Mercury in all three
oxidative states can be methylated by natural process in the
environment to form organic compounds, such as monomethylmercury
(methylmercury) .88
In the atmosphere, between 95 percent and 99 percent of
mercury exists as gaseous Hg°. The remainder generally comprises
methylmercury and mercury associated with particles. The latter
category includes Hg2*, which is thought to occur primarily as
mercuric chloride. Gaseous Hg2+ may also exist in the
atmosphere, especially near mercury emission sources.89
The form of mercury affects both the rate and mechanisms by
which mercury is removed from the atmosphere. Although Hg° is
found more abundantly in the atmosphere than either methylmercury
or Hg2+, these two forms of mercury make up a higher proportion
of the, mercury that is deposited. Methylmercury is probably more
H-42
-------
effectively removed by dry deposition than is Hg°. Additionally,
Hg2+ and methylmercury are more soluble and are scavenged by
precipitation more easily than Hg°, with Hg2+ being the
predominant form in precipitation. Wet deposition is apparently
the primary mechanism for transporting mercury from the
atmosphere to the surface waters and land.90
Once in the surface water, mercury can exist in dissolved
or particulate form. Elemental mercury and dimethylmercury can
be easily volatilized from the surface waters to the atmosphere.
Mercury can also undergo transformations as discussed below. An
important reservoir for mercury is contaminated sediments.
Sediment-bound mercury can recycle back into the aquatic
ecosystem for decades or longer.91
Mercury bound to soil forms compounds that can limit
mercury's mobility and its availability for uptake by living
organisms. Mercury has long retention times in soil, and
accumulated mercury may continue to be released to surface waters
for long periods of time.92
H.3.3.5.2 Transformation and Degradation. The chemical
transformation and movement of mercury within and among the air,
surface water, and soil is termed mercury cycling. A discussion
of the movement of mercury in the environment was discussed
above. This subsection focuses on the transformations of mercury
that occur during the cycling of mercury in the environment.
In the environment, mercury reacts to form inorganic
compounds (e.g., HgCl2, mercuric chloride) and organic compounds
(methylmercury). Mercury in all three oxidative states can be
methylated by natural process in the environment to form
methylmercury. Methylmercury is of special concern because if it
is stable, it can readily accumulate in fish due to efficient
uptake from dietary sources and to low rates of elimination.
Methylmercury is also the most toxic form of mercury.93
Hg2+ in the surface waters can be reduced to Hg°, and the
volatile Hg° can be released to the atmosphere.94 Hg2* can also be
methylated in sediments and, to a lesser extent, the water column
to form methylmercury.95 Each of these reactions is also
reversible, and the net rate of production of each species of
mercury depends on balance between forward and reverse
reactions.96
Methylation of mercury in sediments by anaerobic
sulfur-reducing bacteria is a major source of methylmercury in
many aquatic environments. The rate of mercury methylation
varies with physical and chemical factors, including mercury
H-43
-------
loadings, the amount of suspended sediment, nutrient content, pH
and redox conditions, temperature, and other variables.97
As in water, mercury in soil can be transformed to other
forms of mercury. Hg2+ deposited from the air can bind with the
top layer of forest soils or form stable complexes with soils
particles of high organic or sulfur content and with humic and
f ulvic acids.98 Bacteria or organic substances can reduce Hg2+ to
Hg°, releasing volatile inorganic mercury into the atmosphere.
The various forms of mercury can also be methylated by bacteria
or organic substances. Demethylation of mercury by bacteria can
occur depending on soil conditions.99
H.3.3.6 Nickel. This section discusses the processes that
affect the fate, degradation, and transformation of nickel in the
atmosphere.
H.3.3.6.1 Atmospheric Transportation and Fate. Nickel is
released to the atmosphere mainly as particulates and aerosols
that cover a broad spectrum of sizes. For instances, a study
conducted in Ontario showed that nickel is associated with
relatively large particles, 5.6±2.5 urn, while another study
conducted in six U.S. cities in 1970 demonstrated that the mass
median diameter of nickel was ~ 1.0 um. The latter study,
however, may have underestimated larger nickel particles.100
Additionally, particulate nickel emitted from power plants tend
to be smaller than that emitted from smelters.101
Once in the atmosphere, nickel can be removed by wet and
dry deposition. Researchers disagree as to which type of
deposition is more significant for nickel. One study found the
same percentage of wet and dry deposition for nickel, while in a
study conducted in the Ontario providence, the wet deposition was
found to be 2.2 times greater than dry deposition.102 In general,
though, the importance of wet deposition relative to dry
deposition increases with decreasing particle size.103
Small nickel particles in the atmosphere can have
atmospheric half-lives of up to 30 days and can be transported
over large distances. Evidence for long-range transport of
nickel is provided by the fact that emission sources in North
America, Greenland, and Europe are responsible for elevated
atmospheric nickel concentrations in the Norwegian arctic during
both summer and winter.104
H.3.3.6.2 Atmospheric Transformation and Degradation.
Little information is available on the speciation and chemical
and physical transformations of nickel in the atmosphere, due to
limitations in the analytical methods. In the absence of
specific information, elements of anthropogenic origin,
H-44
-------
especially those emitted from combustion sources, are assumed to
be present as the elemental oxide, and nickel oxide has been
identified in industrial emissions.105
H.4 SUMMARY AND CONCLUSIONS
EPA's study of the health effects from indirect exposure to
the seven aforementioned HAPs requires information on (1) the
various chemical species of each HAP as it is emitted from a
utility stack, (2) how each species behaves chemically once it is
released to the atmosphere, and (3) what the ultimate fate of the
HAP is as it is transported through the atmosphere, water, and
soil. This section summarizes the information collected on these
three topics and provides conclusions that will be considered for
input to EPA's Indirect Exposure Model (IEM) and the Agency's
long-range transport modeling. The results of this modeling—in
conjunction with results of EPA's Human Exposure Model (HEM) for
the direct inhalation of these and other selected HAPs--will be
used to assess the potential risk to public health of HAP air
emissions from utilities. The following paragraphs contain
summaries and conclusions drawn about HAP air emissions and their
environmental chemistry and fate.
H.4.1 Dioxins and Furans
Dioxins and furans leave utility boiler stacks at
concentrations near the detection limit for present analytical
methods. Median values are on the order of 10"6 lb/1012 Btu.
Most of the 25 dioxin and furan congeners were found at 10 test
sites. Emissions of 2,3,7,8-TCDD from the utility boilers
reported here had a maximum value of 6.51 x 10"6 lb/1012 Btu.
Emissions of 2,3,7,8-TCDF had a maximum value of 4.55 x 10"5
lb/1012 Btu.
In general, dioxins and furans have low water solubilities
and vapor pressures and high Kows and Kocs. They tend to sorb
strongly to soils and sediments.106 In soils, they resist
leaching and are transported to surface water via soil
erosion.107-108 In the aquatic environment, dioxins and furans are
removed from the water column via sedimentation and burial.109
Dioxins and furans are stable under normal conditions, and
photodegradation appears to be the most significant degradation
of these compounds.110'111 Aquatic organisms bioaccumulate dioxins
and furans.112
H.4.2 PAHs
PAHs were measured at concentrations on the order of 10"3 or
10"2 lb/1012 Btu. The PAHs measured most frequently in the tests
were 2-methyl naphthalene, fluorene, fluroanthrene, phenanthrene,
and pyrene. Pyrene was detected with the greatest frequency (14
times), having a maximum measured emission value of 0.16 lb/1012
H-45
-------
Btu. Benz(a)anthracene was second, having nine measured values
with a maximum of 0.144 lb/1012 Btu.
The PAHs discussed above have low to moderate molecular
weights when compared to the other PAHs considered in this
report. As such, they tend to have a moderate to high potential
for volatilization from soil and water and a moderate potential
for adsorption to soil.113 These PAHs are similar to other PAHs
in that they are subject to phot ©degradation, chlorination, and
ozonation in the environment. Monochloro derivatives are formed
from the chlorination of fluorene and phenanthrene in water, and
fluorene forms fluorenone due to ozonation in water .114'115'116'117
Environmental half-lives of PAHs range from less than 1 hour to
more than 300 days depending on the type of PAH and the
environmental conditions (Table H-14) ,118 Microbial metabolism is
the major degradation process of PAHs in soil.119
H.4.3 Trace Metals
Nearly all of the trace metals were measured in their total
element category at all 43 test sites. The trace metals examined
had median concentrations of about 1 to 6 lb/1012 Btu. Trace
metals, especially when sorbed to small particles (less than
about 1 or 2 urn), may persist in the atmosphere. For larger
particles, or as particles agglomerate, they may also leave the
atmosphere by wet or dry deposition and may bioaccumulate. The
trace metals generally sorb to soil or mud particles, but may be
leached or transformed depending on pH conditions and the
surrounding environment.
Total mercury was measured 38 times with values as high as
22.9 lb/1012 Btu. In 10 speciation tests, elemental mercury was
measured at a maximum of 14.7 lb/1012 Btu, and ionic mercury had
a maximum measured value of 3.17 lb/1012 Btu.
Chromium was detected at 40 test sites with a maximum value
of 138 lb/1012 Btu. Chromium (VI) was measured in 11 tests and
had values as high as 6.04 lb/1012 Btu. The most important
states of chromium in the atmosphere are chromium (III), and
(VI) . Chromium (VI) can be reduced to chromium (III) in the
atmosphere.120-121'122
Total arsenic was detected in tests at 43 sites, with
values as high as 10* lb/1012 Btu. In speciation tests, its
trivalent species was measured at a maximum of 6.69 lb/1012 Btu;
its pentavalent species was measured at 1.38 lb/1012 Btu. In the
atmosphere, arsenic usually exists as a mixture of trivalent and
pentavalent states, and trivalent arsenic can oxidize to the
pentavalent state.123 The predominant species of arsenic in soils
varies, with As (V) dominating in aerobic soils, As (III)
dominating in slightly reduced soils, and arsine, methylated
H-46
-------
arsenic, and elemental arsenic dominating extremely reduced
conditions.124 In water, arsenic can undergo complex
transformations, but the predominant form of arsenic is usually
arsenate .125
Total nickel was detected at 41 sites, with values as high
as 2.15 lb/1012 Btu. In speciation tests, soluble nickel
compounds (such as sulfates and chlorides) were measured at the
maximum of 1.24 x 103 lb/1012, sulfidic nickel compounds were
measured 7.3 x 101 lb/1012, and nickel oxides were measured at
8.35 x 102 lb/1012. Metallic nickel was not detected.
Lead and cadmium were not measured in their speciated
forms; however, total lead and cadmium were measured at nearly
all of the more than 43 test sites. Lead was measured at a
maximum of 176 lb/1012 Btu, and cadmium's highest value measured
was 28.5 lb/1012 Btu. The predominant forms of lead in the
atmosphere are lead carbonate and lead sulfate.126 Limited
information is available on the chemistry of atmospheric lead.127
In water, lead undergoes many complex reactions. At pHs above
5.4, lead tends to form lead carbonates, and at pHs below 5.4,
lead sulfates are formed.128
Lead in soil can form many different complexes depending on
the pH and the soil type. Different soil pHs also affect the
mobility of lead in soil.129'130 Additionally, plants and animals
bioconcentrate lead, but biomagnification of lead has not been
detected.131
Cadmium compounds are stable in the atmosphere. The most
common cadmium compounds are oxide, sulfate, sulfide, and
chloride.132 In water, cadmium is found only in the 2+ oxidative
state and can exist as the hydrated ion or as ionic complexes
with other inorganic or organic material.133 The mobility of
cadmium in soil dependens upon the soil pH, with cadmium
compounds' being prone to leaching in acidic soils. Cadmium is
not reduced or methylated by microorganisms in water) .134
H-47
-------
H.5 REFERENCES
1. Bloom, N., and W. Fitzgerald. Determination of Volatile
Mercury Species at the Picogram Level by Low-Temperature Gas
Chromatography with Cold-Vapor Atomic Fluorescence
Detection. Analytica Chimica Acta, 208 151-161. 1988.
2. Grohse, P., Research Triangle Institute (RTI). Personal
communication with J. Turner. RTI. 1995.
3. U.S. Environmental Protection Agency. Estimating Exposure
to Dioxin-Like Compounds. Volume II. Properties, Sources,
Occurrences, and Background Exposures. Draft. EPA/600/6-
88/005Cb. Office of Research and Development, Washington,
D.C. 1994a. p. 2-22.
4. Reference 3, p. 2-23.
5. Reference 3, p. 2-24.
6. Reference 3, p. 2-26.
7. Reference 3, pp. 2-8 to 2-11.
8. U.S. Environmental Protection Agency. Hazardous Waste TSDF.
Background Information for Proposed RCRA Air Emission
Standards. Office of Air Quality Planning and Standards,
RTF, NC. 1988. p. D-53 to D-57.
9. Lyman, W., W.F. Reehl, and D.H. Rosenblatt. 1982. Handbook
of Chemical Property Estimation Methods. McGraw Hill, Inc.,
New York, NY. 1982. pp. 1.1 to 1.3.
10. Reference 9, pp. 4.1 to 4.1.
11. Reference 3, p. 2-21.
12. Reference 3, pp. 2-26 to 2-28.
13. Reference 3, p. 2-28.
14. Reference 3, p. 2-28.
15. Reference 3, p. 2-30.
16. Reference 3, p. 2-29.
17. U.S. Environmental Protection Agency. 1993. Interim Report
on Data and Methods for Assessment of 2,3,7,8-
Tetrachorordibenzo-p-dioxin Risks to Aquatic Life and
Associate Wildlife. EPA/600/R-93155. Office of Research
H-48
-------
and Development, Duluth, MM. 1993. p. 3-1.
18. Reference 9, p. 5-1.
19. Reference 19 , p. 3-2.
20. Suter, G.W., L.W. Barnthouse, S.M. Bartell, T. Mill, D.
Mackay, and S. Peterson. Ecological Risk Assessment. Lewis
Publishers, Boca Raton, FL. 1993. p. 498.
21. Reference 9, p. 5-2.
22. Reference 3, p. 2-30.
23. Reference 3, p. 2-21.
24. Reference 3, p. 2-36.
25. Reference 3, p. 2-35.
26. Reference 3, p. 2-30.
27. Reference 3, p. 2-31.
28. Reference 3, p. 2-34.
29. Reference 3, p. 2-33.
30. Reference 3, pp. 2-34 to 2-35.
31. Reference 3, p. 147.
32. Mackay, D., W.Y. Shiu, K.C. Ma. Illustrated Handbook of
Physical-Chemical Properties and Environmental Fate for
Organic Chemicals. Volume II. Polynuclear Aromatic
Hydrocarbons, Polychlorinated Dioxins, and Dibenzofurans.
Lewis Publishers, Boca Raton, FL. 1992. pp. 76-79, 125-
129, 132-140, 163-177, 182-186, 195-203, 206-208, and 236-
238.
33. U.S. Public Health Service. Toxicological Profile for
Polycyclic Aromatic Hydrocarbons. Agency for Toxic
Substances and Disease Registry, Atlanta, GA. 1993e. pp.
132-137.
34. Reference 33, pp. 147-148.
35. Reference 33, p. 148.
36. Reference 33, pp. 148-149.
H-49
-------
37. Reference 33, p. 148.
38. Reference 33, p. 149.
39. Reference 33, p. 150.
40. Reference 33, p. 150.
41. Reference 33, p. 150.
42. Reference 33, p. 151.
43. Reference 33, pp. 150 to 151.
44. Reference 33, p. 151.
45. Reference 33, p. 151.
46. Reference 33, p. 151.
47. Reference 33, p. 152.
48. Reference 33, p. 152.
49. Reference 33, p. 152.
50. Reference 33, p. 153.
51. Reference 33, p. 152.
52. U.S. Environmental Protection Agency. Health Assessment
Document for Inorganic Arsenic. Final Report. EPA-600/8-
83-02IF. Office of Health and Environmental Assessment,
Washington, B.C. 1984. p. 3-1.
53. U.S. Public Health Service. Toxicological Profile for
Arsenic. Agency for Toxic Substances and Disease Registry,
Atlanta, GA. 1993b. p. 103.
54. Reference 53, p. 103.
55. Reference 53, p. 103.
56. Reference 53, p. 103.
57. Reference 53, p. 103.
58. Reference 52, p. 3-13.
59. Reference 53, p. 104.
H-50
-------
60. U.S. Public Health Service. Toxicological Profile for
Cadmium. Agency for Toxic Substances and Disease Registry,
Atlanta, GA. 1993. p. 89.
61. Reference 60, p. 90.
62. Reference 60, pp. 89 to 90.
63. Reference 60, P. 90.
64. Reference 60, p. 90.
65. Reference 60, p. 90.
66. Reference 60, p. 90.
67. Reference 60, p. 89.
68. Reference 60, p. 91.
69. Reference 60, p. 91.
70. Reference 60, p. 91.
71. U.S. Public Health Service. Toxicological Profile for
Chromium. Agency for Toxic Substances and Disease Registry,
Atlanta, GA. 1993. p. 151.
72. U.S. Environmental Protection Agency. Noncarcinogenic
Effects of Chromium. Update to Health Assessment Document.
EPA-600/8-87/048F. Office of Health Effects and Assessment,
RTF, NC. 1990. p. 3-6.
73. Reference 71, p. 129.
74. Reference 71, p. 145.
75. Reference 72, p. 3-13.
76. Reference 71, p. 153.
77. Reference 71, p. 3-13.
78. U.S. Public Health Service. Toxicological Profile for Lead.
Agency for Toxic Substances and Disease Registry, Atlanta,
GA. 1993. p. 190.
79. Reference 78, p. 188.
80. Reference 78, p. 188.
H-51
-------
81. Reference 78, p. 189.
82. Reference 78, p. 189.
83. Reference 78, p. 190.
84. Reference 78, pp. 190 to 191.
85. Reference 78, p. 191.
86. Reference 78, p. 191.
87. Reference 78, p. 191.
88. U.S. Environmental Protection Aagency. An Ecological
Assessment for Anthropogenic Mercury Emissions in the United
States. EPA 452/R-96-003. Office of Air Quality Planning
and Standards and the Office of Research and Development,
Research Triangle Park, NC. 1994. P. 2-1.
89. Reference 88, p. 2-3.
90. Reference 88, pp. 2-3 to 2-4.
91. Reference 88, p. 2-4.
92. Reference 88, pp. 2-4 to 2-5.
93. Reference 88, pp. 2-2 to 2-3.
94. Reference 88, p. 2-4.
95. Reference 88, p. 2-2.
96. Reference 88, p. 2-4.
97. Reference 88, p. 2-2.
98. Reference 88, p. 2-4.
99. Reference 88, p. 2-5.
100. U.S. Public Health Service. Toxicological Profile for
Nickel. Agency for Toxic Substances and Disease Registry,
Atlanta, GA. 1993. pp. 89 to 90.
101. Reference 100, p. 81.
102. Reference 100, p. 90.
103. Reference 100, p. 89.
H-52
-------
104. Reference 100, p. 89.
105. Reference 100, p. 93.
106. Reference 3, p. 2-22.
107. Reference 3, pp. 2-26 to 2-28.
108. Reference 3, p. 2-28.
109. Reference 3, p. 2-29.
110. Reference 3, p. 2-21.
111. Reference 3, pp. 2-34 to 2-35.
112. Reference 3, p. 2-30.
113. Reference 32, pp. 76 to 79, 125 to 129, 132 to 140, 163 to
177, 182 to 186, 195 to 203, 206 to 208, and 236 to 238.
114. Reference 33, pp. 132 to 137.
115. Reference 33, pp. 150 to 151.
116. Reference 33, p. 151.
117. Reference 33, p. 151.
118. Reference 32, pp. 76 to 79, 125 to 129, 132 to 140, 163 to
177, 182 to 186, 195 to 203, 206 to 208, and 236 to 238.
119. Reference 33, p. 152.
120. Reference 72, p. 3-6.
121. Reference 72, p. 129.
122. Reference 71, p. 153.
123. Reference 53, p. 103.
124. Reference 53, P. 104.
125. Reference 53, p. 103.
126. Reference 78, p. 190.
127. Reference 78, pp. 190 to 191.
H-53
-------
128. Reference 78, p. 191.
129. Reference 78, p. 189.
130. Reference 78, p. 191.
131. Reference 78, p. 190.
132. Reference 60, p. 90.
133. Reference 60, pp. 89 to 90.
134. Reference 60, p. 91.
H-54
-------
APPENDIX H-l
LIST OF UTILITY BOILER TEST REPORTS
H-55
-------
TABLE H-l-1. List of Utility Boiler Test Reports
Provider
DOE
DOE
DOE
DOE
DOE
DOE
DOE
DOE
NSP
NSP
NSP
NSP
NSP
NSP
NSP
NSP
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
Site
Baldwin
Boswell
Cardinal
Coal Creek
Niles
Nile/N0x
Springerville
Yates
A.S. King
Black Dog 1,3,4
Black Dog 2
HB 3,4,5,6
Riverside 6,7
Riverside 8
Sherburne 1,2
Sherbourne 3
Site 10
Site 102
Site 1 1
Site 1 1 0
Site 1 1 0A
Site 1 1 1
Site 1 1 2
Site 114
Site 1 1 5
Site 1 1 7
Site 1 1 8
Site 1 1 9
Contractor
Roy F. Weston
Roy F. Weston
Energy and Environmental
Research Corp.
Battelle
Battelle
Battelle
Southern Research Institute
Radian
Interpoll
Interpoll
Interpoll
Interpoll
Interpoll
Interpoll
Interpoll
Interpoll
Radian
Radian
Radian
Southern Research Institue
Southern Research Institute
Radian
Carnot
NA
Carnot
Carnot
Carnot
Carnot
Report date
12/93
12/93
12/93
12/93
12/93
12/93
12/93
12/93
11/91
1/92
5/92
1/92
2/92
9/92
7/90, 10/91
6/90, 10/91
10/92
2/93
10/92, 10/93
10/93
10/93
1/94
1 2/93, 3/94
NA
NA
1/94
1/94
1/94
H-56
-------
TABLE H-l-1. (continued)
Provider
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
EPRI
Site
Site 1 2
Site 120
Site 121
Site 13
Site 14
Site 1 5
Site 1 6/OFA
Site 1 6/OFA/LONOX
Site 18
Site 19
Site 21
Site 22
Site 103
Site 1 04
Site 105
Site 106
Site 107
Site 108
Site 109
Contractor
Radian
NA
NA
Radian
Radian
Radian
Radian
Radian
Radian
Radian
Radian
Radian/Carnot
Radian
Radian
Radian
Radian
Radian
Radian
Radian
Report date
11/92, 10/93
NA
NA
2/93
11/92
10/92
11/93
11/93
4/93
4/93
5/93
3/93, 2/94
3/93
3/93
3/93
3/93
3/93
3/93
3/93
NA = Not available
H-57
-------
APPENDIX H-2
UTILITY BOILER STACK EMISSIONS OF FURANS/DIOXINS, PAHS,
AND SIX TRACE METALS
H-58
-------
The following tables give utility boiler emissions, in
lb/1012 Btu, for furans/dioxins, PAHs, arsenic, cadmium,
chromium, lead, mercury, and nickel. Limited speciation data are
given for arsenic, chromium, mercury, and nickel.
The data in the tables are taken from a series of 47 test
reports (resulting in 48 lines of data) provided by the
Department of Energy (DOE), the Electric Power Research Institute
(EPRI), and Northern States Power Company (NSP). The tests were
performed at 43 sites. A legend and notes are provided at the
end of the table. The table structure gives each test site in
the first column, succeeded by type of emission control and stack
emission values (in lb/1012 Btu) in the remaining columns.
Dioxins/furans are given first, followed by PAHs and trace
metals.
H-59
-------
Table H-2-1. Speciation Data from Utility Boiler Tests for Dioxins and Furans (lb/1012 Btu)
rest Site
DOE Baldwin
JOE Boswell
JOE Cardinal
DOE Creek
DOE Nites2
DOE NitesNox2 (FF)
DOE NilesNox2 (WSA)
JOE Spring
XJE Yates
MSP-A.S. King
^SP-BD-1-3-4
NlSP-BD-2
NSP-HB-3-4-5-6
^SP-Riv-6-7
>JSP-Riv-8
^SP-Sher-1-2
^SP-Sner-3
Site 10
Site 102
Site 11
Site 110
Srte110A
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/Oil
Average
Median
Max
Min
See notes at the end of table
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
2378-TCDD = 2,3,7,8-Tetrachlorodibenzo-p-dioxin
12378-PeCDD = 1 ,2,3,7,8-Pentachlorodibenzo-p-dioxin
123478-HxCDD = 1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin
123678-HxCDD = 1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin
123789-HxCDD = 1,2,3,7,8,9-HexacniorodiDenzo-p-dioxin
Control
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
2378
TCDD
2.3QE-06
3.50E-07
2.30E-06
9.78E-07
6.51 E-06
2.49E-06
2.30E-06
6.51 E-06
3.50E-07
12378
PeCDD
6.03E-07
4.26E-06
4.38E-06
5.01 E-06
6.51 E-06
4.15E-06
4.38E-06
6.51 E-06
6.03E-07
123478
HxCDD
1.21 E-06
1.52E-05
1.05E-05
8.83E-06
1.24E-05
9.63E-06
1 .05E-05
1 .52E-05
1.21 E-06
123678
HxCDD
6.03E-07
2.87E-06
8.72E-06
1.83E-05
5.24E-06
5.64E-06
6.90E-06
5.44E-06
1 .83E-05
6.03E-07
(continued....)
123789
HxCDD
6.03E-07
2.71 E-06
1.19E-05
1.92E-05
8.73E-06
7.95E-06
8.52E-06
8.34E-06
1 .92E-05
6.03E-07
H-60
-------
Table H-2-1. Speciatson Data from Utility Boiler Tests for Dioxins and Furans (lb/1012 Btu)
Test Site
DOE Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOE NilesZ
DOE NilesNox2 (FF)
JOE NilesNox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
MSP-HB-3-4-5-6
NSP-Riv-6-7
NSP-Riv-8
NSP-Sher-1-2
NSP-Sher-3
Site 10
Site 102
Site 11
Site 110
Site110A
Stem
Site 11 2 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
•PRI-1 OS/Oil
EPRI-109/Oil
Average
Median
Max
din
S«e notes at the end of table
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
1234678-HpCDD = 1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin
OCDD = Octachlorodibenzo-p-dioxin)
2378-TCDF = 2,3,7,8-Tetrachlorodibenzofuran
12378-PeCDF = 1 ,2,3,7,8-Pentachlorodibenzofuran
23478-PeCDF - 2,3,4,7,8-Pentachlorodibenzofuran
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
1234678
HpCDD
1.19E-06
5.99E-07
2.28E-06
3.21 E-06
1.67E-05
2.60E-05
1.21E-04
3.27E-04
2.74E-05
1.23E-05
5.73E-06
4.94E-05
1.23E-05
3.27E-04
5.99E-07
OCDD
8.29E-06
4.79E-06
1.50E-05
1.83E-05
1.29E-04
1.99E-04
1.71E-03
2.56E-04
2.32E-05
5.82E-05
2.42E-04
4.07E-05
1.71E-03
4.79E-06
2378
TCDF
1.15E-06
2.59E-06
6.68E-07
9.51 E-06
4.63E-06
3.25E-06
4.55E-05
2.75E-05
1.97E-06
7.23E-06
1.04E-05
3.94E-06
4.55E-05
6.68E-07
12378
PeCDF
6.89E-07
2.39E-06
2.09E-05
1.26E-05
2.89E-06
5.79E-06
2.44E-06
6.81 E-06
2.89E-06
2.09E-05
6.89E-07
(continued....)
23478
P0CDF
1.62E-06
2.19E-06
1.04E-05
4.83E-05
2.92E-05
3.11E-06
6.51 E-06
1.45E-05
6.51 E-06
4.83E-05
1.62E-06
H-61
-------
Table H-2-1. Speciation Data from Utility Boiler Tests for Dioxins and Furans (lb/1012 Btu)
restStta
JOE Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOE Niles2
XDE NilesNox2 (FF)
X>E NilesNox2 (WSA)
XDE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
^SP-HB-3-4-5-6
NSP-Riv-6-7
NSP-Riv-8
MSP-Sner-1-2
NSP-Sher-3
Site 10
Site 102
Site 11
Site 110
Site110A
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
:PRI-105/Oil
EPRI-106/OH
EPRI-107/Oil
:PRI-108/Oil
EPRI-109/Oil
Average
Median
riax
Min
See notes at the end of table
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
123478-HxCDF = 1,2,3,4,7,8-Hexachlorodibenzofuran
123678-HxCDF = 1, 2,3,6,7 ,8-Hexachlorodibenzofuran
123789-HxCDF = 1,2,3,7,8,9-Hexachlorodibenzofuran
234678-HxCDF = 2,3,4,6,7,8-Hexachlorodibenzofuran
1234678-Hpuoi- = 1, 2,3,4,6, /, 8-HeptacnioroaiDenzoturan
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
123478
HxCDF
6.58h-06
2.79E-06
9.57E-06
1.62E-05
2.56E-04
1.55E-04
6.30E-06
5.79E-06
5.73E-05
8.08E-06
2.56E-04
2.79E-06
123678
HxCDF
7.33E-07
9.59E-07
3.99E-06
8.77E-05
5.30E-05
3.34E-06
4.34E-06
2.20E-05
3.99E-06
8.77E-05
7.33E-07
123789
HxCDF
6.03E-07
6.30E-06
1.75E-05
1.06E-05
6.30E-06
5.35E-06
7.78E-06
6.30E-06
1 .75E-05
6.03E-07
234678
HxCDF
1 .38E-06
9.99E-07
1.62E-05
1.88E-04
1.14E-04
4.77E-06
5.42E-05
1.05E-05
1.88E-04
9.99E-07
(continued....)
1Z34678
HpCDF
1.36E-06
1.99E-06
3.96E-05
1 .67E-05
2.27E-05
1.18E-03
7.12E-04
9.40E-06
4.70E-06
2.21 E-04
1 .67E-05
1.18E-03
1 .36E-06
H-62
-------
Table H-2-1. Speciation Data from Utility Boiler Tests for Dioxins and Furans (lb/1012 Btu)
TestSKe
XJh Baldwin
DOE Boswell
DOE Cardinal
X»E Creek
X»E Niles2
XDE NilesNox2 (FF)
DOE NilesNox2 (WSA)
DOE Spring
XDE Yates
NISP-A.S. King
NlSP-BD-1-3-4
•JSP-BD-2
^ISP-HB-3-4-5-6
NSP-Riv-6-7
-------
Table H-2-1. Speciation Data from Utility Boiler Tests for Dioxins and Furans (lb/1012 Btu)
Test Site
DOE Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
)OE Niles2
DOE NilesNox2 (FF)
DOE NilesNox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
NSP-HB-3-4-5-6
NSP-Riv-6-7
-------
LEGEND AND NOTES TO TABLES H-2-1 through H-2-3
Chemical abbreviations are shown below. Numerical entries are
averages of (usually) three measurments in lb/1012 Btu. Shaded
values in the table include one or more measurements below the
detection limit. However, at least one mesurement in each
average is above the detection limit. See Section H.2.2 for a
further discussion of nondetects.
DIOXINS AND FURANS
2378
12378P
123478
123678
123789
1234678H
OCTA DI
2378T
12378P
23478P
123478
123678
123789
234678
1234678H
1234789H
OCTA F
TETRAPDI
PENTAPDI
HEXAPDI
HEPTAPDI
TETRAF
PENTAF
HEXAF
HEPTAF
2,3,7,8-TETRACHLORODIBENZO-P-DIOXIN
1,2,3,7,8-PENTACHLORODIBENZO-P-DIOXIN
1,2,3,4,7,8-HEXACHLORODIBENZO-P-DIOXIN
1,2,3,6,7,8-HEXACHLORODIBENZO-P-DIOXIN
1,2,3,7,8,9-HEXACHLORODIBENZO-P-DIOXIN
1,2,3,4,6,7,8-HEPTACHLORODIBENZO-P-DIOXIN
OCTACHLORODIBENZO-P-DIOXIN
2,3,7,8-TETRACHLORODIBENZOFURAN
1,2,3,7,8-PENTACHLORODIBENZOFURAN
2,3,4,7,8-PENTACHLORODIBENZOFURAN
1,2,3,4,7,8-HEXACHLORODIBENZOFURAN
1,2,3,6,7,8-HEXACHLORODIBENZOFURAN
1,2,3,7,8,9-HEXACHLORODIBENZOFURAN
2,3,4,6,7,8-HEXACHLORODIBENZOFURAN
1,2,3,4,6,7,8-HEPTACHLORODIBENZOFURAN
1,2,3,4,7,8,9-HEPTACHLORODIBENZOFURAN
OCTACHLORODIBENZOFURAN
TETRACHLORODIBENZO-P-DIOXIN
PENTACHLORODIBENZO-P-DIOXIN
HEXACHLORODIBENZO-P-DIOXIN '
HEPTACHLORODIBENZO-P-DIOXIN
TETRACHLORODIBENZOFURAN
PENTACHLORODIBENZOFURAN
HEXACHLORODIBENZOFURAN
HEPTACHLORODIBENZOFURAN
PAHs
1CLNAPH
1MTHLNAPH
2CLNAPH
2MTHLNAPH
3MTCHOL
7,12DIMET
ACENAPH
ACENAPHY
ANTHRACN
BNZaANTH
BNZOaP.YR
1-CHLORONAPHTHALENE
1-METHYLNAPHTHALENE
2-CHLORONAPHTHALENE
2-METHYLNAPHTHALENE
3-METHYCHOLANTHRENE
7,12-DIMETHYLBENZ(a)ANTHRACENE
ACENAPHTHENE
ACENAPHTHYLENE
ANTHRACENE
BENZ(a)ANTHRACENE
BENZO(a)PYRENE
H-65
-------
BNZOePYR
BENZObFLN
BNZObkFLN
BNZOkFLN
BNZOghiPERY
BIPHENYL
CHRYSENE
DBNZajACR
DBNZOahANT
FLURANTHN
FLUORENE
INDNOPYRN
NTRANTHRCN
NTRBNZFLU
NTROCHRYS
PERYLENE
PHNANTREN
PYRENE
BENZO(e)PYRENE
BENZO(b)FLUORANTHENE
BENZO(b+k)FLUORANTHENE
BENZO(k)FLUORANTHENE
BENZO(g,h,i)PERYLENE
BIPHENYL
CHRYSENE
DIBENZ(a,j)ACRIDINE
DIBENZO(a,h)ANTHRACENE
FLUORANTHENE
FLUORENE
INDENO(1,2,3-c,d)PYRENE
NITRANTHRACENE/PHENANTHRENE
NITROBENZOFLUORANTHENE
NITROCHRYSENE/BENZANTHRACENE
PERYLENE
PHENANTHRENE
PYRENE
TRACE METALS AND ARSENIC
LEAD
MERCURY
HG ELEM
HG ION
CADMIUM
ARSENIC
AS III
AS V
CHROM
CHROM6
NICKEL
SOLY N
SULF N
MET N
OX N
TOTAL LEAD
TOTAL MERCURY
ELEMENTAL MERCURY
IONIC MERCURY
TOTAL CADMIUM
TOTAL ARSENIC
TRIVALENT ARSENIC
PENTAVALENT ARSENIC
TOTAL CHROMIUM
HEXAVALENT CHROMIUM
TOTAL NICKEL
SOLUBLE NICKEL
SULFIDIC NICKEL
METALLIC NICKEL
OXIDIZED NICKEL
H-66
-------
Abbreviations under the "CONTROL" column are:
CESP Cold-side electrostatic precipitator
COHPAC Compact hybrid particulate collector
ESP Electrostatic precipitator
FBC Fluidized bed combustor
FF Fabric filter
FGD Flue-gas desulfurization
HESP Hot-side electrostatic precipitator
JBR Jet bubbling reactor [S02 control]
PJFF Pulse-jet fabric filter
SCR Selective catalytic reduction [NOX control]
SDA Spray drier absorber [S02 control]
WSA Wet sulfuric acid [condenser]
Note 1. Magnesium oxide is added to the fuel oil. Because Site
112 had an apparently low-efficiency ESP (less than
4 percent), and sampling problems were found at the ESP
outlet, reported values are taken from the ESP inlet.
Note 2. Ammonia is added to the flue gas to improve ESP
performance.
H-67
-------
Table H-2-2. Speciation Data from Utility Boiler Tests for PAHs (lb/10" Btu)
Test Site
DOE: Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
X)E Niles2
DOE Nilesnox2 (FF)
DOE Nilesnox2 (WSA)
DOE Spring
JOE Yates
MSP-A.S. King
^SP-BD-1-3-4
^SP-BD-2
^SP-HB-3-4-5-6
^SP-RIV-6-7
^SP-RIV-8
NSP-SHER-1-2
NSP-SHER-3
Site 10
Site 102
Site 11
Site 110
Site 11 OA
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site16/OFA/LNB(b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRl-109/Oil
Average
Median
Ulax
Win
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
See notes at the end of table H-2-1
1MTHLNAPH = 1-Methylnaphthalene
2CLNAPH = 2-Chloronaphthalene
2MTHLNAPH = 2-Methylnaphthalene
ACENAPH = Acenaphthene
ACENAPHY = Acenaphthytene
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
1MTHLNAPH
5.55E-03
1.14E-02
8.48E-03
8.48E-03
1.14E-02
5.55E-03
2CLNAPH
1 .44E-04
8.07E-02
4.04E-02
4.04E-02
8.07E-02
1.44E-04
2MTHLNAPH
7.87E-02
1.03E-02
1.99E-02
2.72E-02
1.90E-02
2.70E-02
3.03E-02
2.34E-02
7.87E-02
1.03E-02
ACENAPH
7.37E-02
7.85E-02
5.35E-03
7.76E-03
8.10E-03
6.00E-03
2.99E-02
7.93E-03
7.85E-02
5.35E-03
(continuea...)
ACENAPHY
4.16E-03
4.16E-03
2.80E-02
3.00E-03
3.40E-03
8.55E-03
4.16E-03
2.80E-02
3.00E-03
H-68
-------
Table H-2-2. Speciation Data from Utility Boiler Tests for PAHs (lb/1012 Btu)
rest Site
DOE Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOE Niles2
DOE Nilesnox2 (FF)
DOE Nilesnox2 (WSA)
DOE Spring
DOE Yates
MSP-A.S. King
>JSP-BD-1-3-4
^SP-BD-2
NSP-HB-3-4-5-6
^SP-RIV-6-7
JSP-RIV-8
^SP-SHER-1-2
>ISP-SHER-3
Site 10
Site 102
Site 11
Site 110
SiteHOA
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/OII
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/Oil
Average
Median
tflax
Min
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
See notes at the end of table H-2-1
ANTHRACN = Anthracene
JNZaANTH = Benz(a)anthracene
JNZOaPYR = Benzo(a)pyrene
JNZOePYR = Benzo(e)pyrene
BNZObFLN = Benzo(b)fluoranthene
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
L_ ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
ANTHRACN
1.72E-03
2 15E-02
3.70E-03
4.60E-03
7.87E-03
4.15E-03
2.15E-02
1.72E-03
BNZaANTH
2.05E-03
2.14E-03
8 84E-03
1.00E-03
3.51 E-03
2.10E-03
8.84E-03
1.00E-03
BNZOaPYR
2.27E-04
3.67E-02
6.60E-04
9.40E-04
1.89E-03
1.10E-03
6.92E-03
1.02E-03
3.67E-02
2.27E-04
BNZOePYR
1.21 E-03
1.21 E-03
1.21 E-03
1.21 E-03
1.21 E-03
(continued...)
BNZObFLN
8.08E-03
8.08E-03
8.08E-03
8.08E-03
8.08E-03
H-69
-------
Table H-2-2. Spociation Data from Utility Boiler Tests for PAHs (lb/1012 Btu)
rest Site
)OE Baldwin
DOE Boswell
DOE Cardinal
JOE Creek
DOE Nites2
DOE Nitesnox2 (FF)
XDE Nitesnox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
^SP-BD-1-3-4
^SP-BD-2
NSP-HB-3-4-5-6
NSP-RIV-6-7
NSP-RIV-8
^SP-SHER-1-2
^SP-SHER-3
Site 10
Site 102
Site 11
Site 110
Site110A
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/OH
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
:PRI-107/Oil
EPRI-108/Oil
EPRI-109/Oil
Average
Median
4ax
din
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
See notes at the end of table H-2-1
3NZObkFLN = Benzo(b+k)fluorantnene
SNZOkFLN = Benzo(k) Fluoranthene
BNZOghiPERY = Benzo(g,h, )perylene
3IPHENYL = Biphenyl
CHRYSENE = Chrysene
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
BNZObkFLN
1.61E-03
1.61E-03
1.61E-03
1.61E-03
1.61E-03
BNZOkFLN
3.56E-03
3.56E-03
3.56E-03
3.56E-03
3.56E-03
BNZOghlPERY
4.20E-03
2.20E-03
3.20E-03
3.20E-03
4.20E-03
2.20E-03
BIPHENYL
3.43E-01
3.43E-01
3.43E-01
3.43E-01
3.43E-01
(continued...)
CHRYSENE
2.71 E-03
3.56E-03
7.87E-04
2.50E-03
2.39E-03
2.61 E-03
3.56E-03
7.87E-04
H-70
-------
Table H-2-2. Speciation Data from Utility Boiler Tests for PAHs (lb/1012 Btu)
Test Site
JUb Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOENiles2
DOE Nilesnox2 (FF)
DOE Nitesnox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
NSP-HB-3-4-5-6
*4SP-RIV-6-7
NSP-RIV-8
•4SP-SHER-1-2
•JSP-SHER-3
Site 10
Site 102
Site 11
Site 110
Site110A
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oi!
EPRI-104/Oil
EPRI-1 OS/Oil
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/OH
Average
Median
Max
Win
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
See notes at the end of table H-2-1
DBNZOahANT = Dibenzo(a.h)anthracene
FLURANTHN = Fluoranthene
FLUORENE = Fluorine
INDNOPYRN = Indeno (1 ,2,3-c,d)pyrene
MTRBNZFLU = Nitrobenzofluorantnene
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
DBNZOahANT
3.30E-04
3.30E-04
3.30E-04
3.30E-04
3.30E-04
FLURANTHN
7.29E-03
5.13E-03
2.91 E-02
5.12E-03
4.72E-03
2.40E-02
1.26E-02
6.21 E-03
2.91 E-02
4.72E-03
FLUORENE
1.35E-02
1 .32E-02
1.72E-01
4.33E-03
1.20E-02
4.30E-02
1.32E-02
1.72E-01
4.33E-03
INDNOPYRN
4.20E-03
8.60E-03
6.40E-03
6.40E-03
8.60E-03
4.20E-03
(continued...)
NTRBNZFLU
1.50E-02
1.50E-02
1.50E-02
1.50E-02
1.50E-02
H-71
-------
Table H-2-2. Speciatlon Data from Utility Boiler Tests for PAHs (lb/1012 Btu)
Test Site
DOE Baldwin
DOE Boswell
DOE Cardinal
XDE Creek
DOE Niles2
DOE Nitesnox2 (FF)
DOE Nitesnox2 (WSA)
DOE Spring
DOE Yates
*4SP-A.S. King
^SP-BD-1-3-4
*JSP-BD-2
slSP-HB-3-4-5-6
>JSP-RIV-6-7
NSP-RIV-8
NSP-SHER-1-2
NJSP-SHER-3
Site 10
Site 102
Site 11
Site 110
Site 11 OA
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/ON
EPRI-104/Oil
LPRI-105/Oil
EPRI-106/OH
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/OH
Average
Median
rfax
tfin
See other notes at the end o
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
table H-2-1
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
MTROCHRYS = Nitrochrysene/Benzanthracene
'HNANTREN = Phenanthrene
3YRtNt = pyrene
NTROCHRYS
1.60E-02
1.60E-02
1.60E-02
1.60E-02
1.60E-02
PHNANTREN
2.38E-02
3.15E-02
1.29E-01
1.70E-02
1.20E-02
2.01 E-02
6.90E-02
4.32E-02
2.38E-02
1.29E-01
1.20E-02
PYRENE
1.21E-03
1.29E-02
4.72E-03
1 .60E-01
4.47E-02
8.83E-03
1.60E-01
1.21E-03
H-72
-------
Table H-2-3. Speciation Data from Utility Boiler Tests for Metals (lb/1012 Bti)
Test Site
LKJb Baldwin
JOE Boswell
DOE Cardinal
DOE Creek
DOE NilesZ
DOE NitesNox2 (FF)
DOE NilesNox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
NSP-HB-3-4-5-6
NSP-Riv-6-7
NSP-Riv-8
1SP-Sher-1-2
NSP-Sher-3
Site 10
Site 102
Site 11
Site 110
Srte110A
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/Oil
Average
Median
Max
Min
SM notes at the end of table H-2-1
LEAD = Total Lead
MERCURY = Total Mercury
HG ELEM = Elemental Mercury
HG ION = Ionic Mercury
CADMIUM = Total cadmium
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
Lead
2.86E+01
2.44E+00
3.78E+00
6.63E-01
1.61 E+00
3.66E-01
5.22E-01
6.76E-01
3.55E-01
2.76E+00
1.40E+01
5.08E+01
2.60E+00
6.80E+00
2.49E+00
5.90E+00
7.07E+00
2.52E+00
2.70E+00
6.78E+00
1.83E+01
1.72E+01
2.61 E+00
7.56E+01
4.34E-01
9.10E+00
1.78E+00
4.48E+00
2.64E-01
4.68E-01
3.00E-01
2.82E+00
4.75E+00
3.33E+01
1.13E+01
6.32E+00
1.10E-01
3.71 E+00
2.23E+00
8.94E+00
2.78E+01
1.04E+01
1.92E+01
1.35E+01
3.78E+00
1.76E+02
1.10E-01
Mercury
3.36E+00
4.69E+00
5.31 E-01
9.09E+00
1.45E+01
1.95E+01
2.29E+01
3.93E+00
3.07E+00
1.83E+00
3.10E+00
2.60E+00
3.92E+00
4.87E+00
3.52E+00
1.05E+01
3.62E+00
1.78E+00
6.78E+00
6.20E+00
4.99E+00
2.38E-01
4.66E+00
3.50E-01
4.80E-01
4.00E-01
1.33E+00
3.95E-01
6.60E-02
2.00E+00
1.04E+00
6.38E+00
4.85E+00
7.10E+00
6.20E+00
8.40E-01
3.80E+00
2.11 E+00
4.53E+00
3.44E+00
2.29E+01
6.60E-02
HG ELEM
1.44E+00
5.34E-01
2.80E+00
5.35E+00
1.24E+00
4.60E-01
ND
3.61 E+00
5.38E-01
1.47E+01
3.40E+00
1.44E+00
1.47E+01
4.60E-01
HGION
3.17E+00
1.69E+00
5.10E-01
2.16E+00
5.49E+00
4.43E+00
2.72E-02
3.05E+00
7.47E-02
5.99E-01
2.12E+00
1.93E+00
5.49E+00
2.72E-02
(continued...)
Cadmium
2.69E+00
1.10E+01
8.00E-01
6.78E-02
6.30E-02
1.07E-01
6.00E-01
1.13E-01
1.06E+00
6.55E+00
5.70E+00
2.85E+01
8.23E+00
8.07E+00
2.12E-01
1.26E+00
1.74E+00
1.72E+00
2.13E+00
3.23E-01
1.81 E+00
2.33E-02
1.30E-01
1.80E-01
1.87E+00
9.60E-01
3.05E+00
5.02E-01
3.64E+00
3.10E+00
1.30E-01
5.70E-01
1.60E-01
3.19E+00
7.17E-01
7.03E-01
1.22E+00
1.56E+00
3.97E+00
3. 11 E+00
2.79E+00
1.24E+00
2.85E+01
2.33E-02
H-73
-------
Table H-2-3. Speciation Data from Utility Boiler Tests for Metals (lb/1012 Btu)
Test Site
x_)t Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOE Niles2
DOE NilesNox2 (FF)
XDE NilesNox2 (WSA)
XDE Spring
X5E Yates
vlSP-A.8. King
^SP-BD-1-3-4
NSP-BD-2
NSP-HB-3-4-5-6
NSP-Riv-6-7
'JSP-Riv-S
^SP-Sher-1-2
NSP-Sher-3
Site 10
Site 102
Site 11
Site 110
Site 11 OA
Site 111
Site 11 2 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site 16/OFA/LNB (b)
Site 18
Site 19
Site 21
Site 22
•PRI-103/Oil
EPRI-104/Oil
EPRI-105/Oil
EPRI-106/Oil
EPRI-107/Oil
:PRI-108/Oil
•PRI-109/Oil
Average
Median
Aax
din
See notes at the end of table H-2-1
ARSENIC = Total Arsenic
AS III = Trivalent Arsenic
AS V = Pentavatent Arsenic
CHROM = Total Chromium
CHROM6 = Hexavalent Chromium
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
ARSENIC
1.21E+01
3.44E+01
3.10E+00
1.18E+00
4.23E+01
9.09E+00
5.22E-01
1.37E-01
1.20E+00
2.25E+00
4.27E-01
5.33E-01
3.32E+00
1.11 E+00
3.91 E-01
2.83E-01
4.85E-02
7.45E-01
2.91 E+00
6.04E-01
1.40E+00
9.48E+00
2.13E-01
2.46E+00
7.13E+00
7.53E-01
2.90E+00
5.50E-01
1.77E+00
1.58E-01
1.17E-01
4.00E-02
5.00E-01
1.34E+01
9.45E+01
1.04E+02
3.60E+01
7.90E+00
6.17E+00
8.70E-02
3.63E+00
6.53E+00
4.13E+00
2.70E+01
1.27E+01
7.76E+00
1.01 E+00
9.98E+00
2.25E+00
1.04E+02
4.00E-02
AS III
6.69E-01
3.03E-01
4.86E-01
4.86E-01
6.69E-01
3.03E-01
ASV
1.38E+00
1.70E-01
7.72E-01
7.72E-01
1.38E+00
1.70E-01
CHROM
4.63E+01
2.04E+00
6.17E+00
5.88E+01
3.06E+00
1.18E+00
2.10E+01
5.30E+00
1.14E+00
8.68E+00
1.84E+00
6.66E+00
7.00E+00
1.38E+02
3.99E+00
5.62E+00
9.21 E+00
1.64E+00
8.59E+00
4.14E+00
1.27E+01
3.04E+01
4.25E+00
3.77E+00
1.36E+01
6.61 E-01
1.99E+01
3.30E+00
1.86E+00
9.75E+00
1.70E+00
4.11 E-01
1.11E+01
3.75E+01
2.08E+01
2.50E+01
1.30E+01
2.74E+00
3.48E+00
3.00E+00
2.10E+00
7.27E+00
7.93E+00
ND
1.10E+01
1.34E+01
6.42E+00
1.38E+02
4.11 E-01
CHROM6
2.37E-02
6.54E-01
8.89E-01
1.89E-01
1.86E-01
6.04E+00
1.04E+00
ND
4.20E-01
3.78E+00
1.67E+00
3.05E+00
1.63E+00
8.89E-01
6.04E+00
2.37E-02
(continued...)
H-74
-------
Table H-2-3. Speciatlon Data from Utility Boiler Tests for Metals (lb/1012 Btu)
rest Site
uub Baldwin
DOE Boswell
DOE Cardinal
DOE Creek
DOE Niles2
DOE NilesNox2 (FF)
DOE NilesNox2 (WSA)
DOE Spring
DOE Yates
NSP-A.S. King
NSP-BD-1-3-4
NSP-BD-2
NSP-HB-3-4-5-6
NSP-Riv-6-7
NSP-Riv-8
NSP-Sher-1-2
NSP-Sher-3
Site 10
Site 102
Site 11
Site 110
Site 11 OA
Site 111
Site 112 (a)
Site 114
Site 115
Site 117 (a)
Site 118
Site 119 (a)
Site 12
Site 120
Site 121
Site 13
Site 14
Site 15
Site 16/OFA
Site16/OFA/LNB(b)
Site 18
Site 19
Site 21
Site 22
EPRI-103/Oil
EPRI-104/Oil
EPRI-105/OH
EPRI-106/Oil
EPRI-107/Oil
EPRI-108/Oil
EPRI-109/Oil
Average
Median
riax
Win
See notes at the end of table H-2-1
NICKEL = Total Nickel
SOL Ni = Soluble Nickel
SULF Ni = Sulfidic Nickel
MET Ni = Metallic Nickel
ux NI = oxidized Nickel
Fuel
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Coal
Coal
Oil
Oil
Oil
Coal
Gas
Gas
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Control
ESP
FF
ESP
ESP/FGD
ESP
FF/SCR
FF/SCR/WSA
SDA/FF
ESP/JBR
ESP
ESP
ESP
ESP
FF
ESP
FGD
SDA/FF
FBC/FF
ESP
ESP/FGD
HESP/CESP
HESP/CESP
SDA/FF
ESP
ESP
FF
SCR
ESP
ESP
ESP/FGD
None
None
PJFF
SDA/PJFF
ESP
ESP
ESP
ESP/COHPAC
ESP
ESP/FGD
ESP
None
None
None
None
None
None
None
NICKEL
2.21 E+01
1 .97E+00
4.72E+00
5.10E+00
5.50E-01
2.20E-01
2.20E+00
4.01 E+01
3.00E-02
1.40E+00
3.29E+00
3.52E+00
3.40E-01
4.53E+00
4.55E+00
7.20E+00
3.40E+02
2.60E+00
7.70E+00
5.00E+00
5.80E+00
3.03E+02
7.80E+01
1.50E+00
8.09E+02
4.60E+01
2.15E+03
4.40E+00
3.60E+00
8.60E-01
1.60E+00
2.30E+00
5.90E+00
2.40E+01
1.70E+01
1.60E+01
7.90E+00
1.68E+00
6.40E-01
3.48E+02
3.64E+02
5.10E+02
3.80E+02
4.20E+02
1.40E+03
2.40E+02
1.65E+02
5.45E+00
2.15E+03
3.00E-02
SOLNi
2.13E+01
1.24E+03
6.28E+02
6.28E+02
1.24E+03
2.13E+01
SULF Ni
2.60E+00
7.30E+01
3.78E+01
3.78E+01
7.30E+01
2.60E+00
METNi
ND
ND
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
OXNi
9.00E+00
8.35E+02
4.22E+02
4.22E+02
8.35E+02
9.00E+00
H-75
-------
Appendix I - SUMMARY OF EPRI'S UTILITY REPORT
-------
This page is intentionally blank.
-------
The industry (Electric Power Research Institute[EPRI])
produced its own exposure and risk assessment. The Executive
Summary, from the report entitled "Electric Utility Trace
Substances Synthesis Report"(November, 1994)i, is included and
quoted below.
BACKGROUND AND OVERVIEW
"This report by the Electric Power Research Institute
(EPRI) is intended to provide information to electric
utilities, and to EPA for its own utility study, as
well as to the broader scientific and regulatory
community. The key goal of the effort is to bring
together the best scientific data and methods currently
available to understand the potential magnitude and
nature of human health risks due to trace emissions
from electric utility steam-generating units in the
United States. As such, the report summarizes the
results of recent trace substance research, conducted
by EPRI and others, addressing a range of critical
issues such as trace substance emission rates from
utility generating units, appropriate sampling and
analytical methods for determining- these rates, and the
toxicity of specific substances found in utility
emissions. Further, the report describes the risk
assessment methodology developed to integrate this
research and understand its implications with respect
to a nationwide evaluation of potential human health
risk.
"The data, methodologies, and analysis results
presented in this report provide an understanding of
utility trace substance emissions and the risks
associated with these emissions, consistent with the
best data and methodologies available at this time.
The results of this research and analysis indicate that
trace substance emissions from fossil-fired electric
utility steam generating units, after compliance with
other provisions of the Clean Air Act Amendments, will
not pose significant long-term risks (either
carcinogenic or noncarcinogenic) to human health.
SCOPE/APPROACH
"Two major projects served as the foundation for EPRI's
trace substance research and provided much of the input
and direction for this analysis. These projects were
the Power Plant Integrated Systems: Chemical Emissions
1-1
-------
Study (PISCES) project and the Comprehensive Risk
Evaluation (CORE) project. PISCES conducted extensive
method development and field measurement programs that
provide a database for predicting airborne trace
substance emissions throughout the utility industry.
CORE developed the methodology to integrate the
information from PISCES and other research results on
emissions, fate, and health effects, into a nationwide
assessment of health risks.
wAs an initial step, these projects identified 16 trace
substances as critical to include in an industry-wide
risk assessment. The 16 were selected based on an
evaluation of those trace substances 1) most likely to
be found in utility stack emissions (based on a
literature review and EPRI measurement data) and 2) for
which health risk analysis could be readily performed
using available toxicity factors. The substances
identified as meeting both of these criteria include:
Arsenic Chlorine Lead PAHs
Benzene Chromium Manganese Radionuclides
Beryllium Dioxins/Furans Mercury Selenium
Cadmium Formaldehyde Nickel Toluene
The scope of the risk assessment presented in this
report encompasses the potential chronic (i.e., long-
term) health risks due to emissions of these 16 trace
substances from approximately 600 power plants,
encompassing roughly 1700 fossil-fired steam electric
generating units. To predict emissions from these
units, several scenarios were produced describing how
the electric utility industry may be configured after
all measures to achieve compliance with the SO2
provisions of the CAAA are implemented (assumed to be
2010). These scenarios were augmented by additional
data on particulate controls developed through an
independent survey of utility operators regarding their
plans to modify or upgrade particulate control
equipment to meet other air pollution control
requirements. These projections of power plant
operations and control configurations were combined
with characterizations of trace element concentrations
in utility fuels to produce estimates of emissions in
the year 2010 from the entire population of United
1-2
-------
States electric utility steam generating units (with
>25 MW nameplate capacity).
"Plant operating and design characteristics together
with regional meteorological data were used to model
the transport and dispersion of trace emissions and the
resulting ground-level concentration within
50 kilometers of each plant. The EPA's Industrial
Source Complex Long Term 2 (ISCLT2) model was selected
for the dispersion modeling. The ISCLT2 model
estimates annual-average concentrations, making it
useful for evaluating chronic effects due to long-term
exposures to chemicals, as well as allowing the
efficient modeling of several hundred separate sources.
In addition to evaluating the concentration levels due
to individual plants, an "overlapping plume' analysis
was performed to account for the total exposure of
populations living within 50 kilometers of more than
one power plant.
"Population data from the 1990 United States census
were used to assess the exposure levels of individuals
living within 50 kilometers of electric utility
generating units, based on electricity.supply scenarios
for the year 2010. Due to the initial atmospheric
transport of these contaminants, the primary pathway
evaluated for human exposure was through inhalation.
The incremental increase in inhalation cancer risk due
to utility emissions was computed for two exposure
scenarios: the 'Maximally Exposed Individual' (or MEI)
and a 'reasonably Exposed Individual' (or REI). The MEI
represents a conservative estimate of possible
exposure, assuming an individual breathes outside air,
in the location with the highest concentrations of
trace substances due to a single power plant's
emissions, 24 hours per day for a 70-year lifetime.
"To provide a more realistic estimate of potential
human exposures, and in keeping with recent
recommendations by EPA and the National Academy of
Sciences, EPRI developed the REI. The REI incorporates
data on the amount of time individuals spend indoors
and outdoors in various activities and on indoor
reductions of outdoor concentration levels. In
addition to the inhalation exposure assessments,
several case studies were performed to evaluate
potential multimedia exposures to utility trace
1-3
-------
emissions through all exposure pathways (i.e.,
inhalation, ingestion, and dermal contact).
"Finally, the exposure assessments were combined with
dose-response relationships to provide an estimate of
potential public health risks. Due to the nature of
the emissions (i.e., very low levels of substances
emitted over a relatively long period of time), the
primary health effects evaluated are potential
increases in chronic cancer or non-cancer risks over a
70-year lifetime. For noncarcinogens, the predicted
concentration levels due to generating unit emissions
are compared to federal reference doses (RfDs) or
reference concentrations (RfCs) for the substances of
concern. These reference levels are defined as being
levels of daily exposure that are likely to be without
appreciable chronic deleterious effects, even for
sensitive individuals in a population.
"For most of the carcinogens, the relevant unit risk
factor tabulated by the U.S. EPA as of mid-1994 was
used to estimate potential incremental cancer risk due
to utility emissions of trace substances. The unit
risk factor represents a plausible upper-bound estimate
of the increased probability of contracting cancer due
to a 70-year lifetime exposure to an inhalation
concentration of 1 ug/m3 of a given substance. In the
case of arsenic, a revised unit risk factor was derived
based on a re-analysis of existing and new occupational
exposure data. The revised unit risk factor of 1.43 x
10"3 per ug/m3 (one-third of that listed in 1994 in the
EPA Integrated Risk Information System (IRIS) database)
was used in this study to estimate arsenic inhalation
cancer risks. In addition, risk estimates were carried
out using the higher EPA IRIS value.
SYNOPSIS OF RESULTS
"The following highlights key research findings that
contributed to the overall assessment of risks due to
trace emissions from electric utility steam generating
units, as well as the results of the risk assessment
itself.
Concentrations of Trace Substances in Coal
"A data set based on U.S. Geological Survey information
and coal cleaning data was developed to characterize
1-4
-------
the concentrations of inorganic substances in coals,
'as fired,' at power plants. This database represents
a significant improvement over previous coal
characterizations in that it incorporates economic
criteria (i.e., seam depth and quality) and the impact
of coal cleaning processes in predicting 'as-fired'
coal properties.
"In addition to this work, a measurement program was
carried out to specifically examine mercury
concentrations in 'as-fired' coal. Approximately 150
samples of delivered coal, representing 20 major seams
and all coal ranks, were analyzed by atomic
fluorescence to determine mercury concentrations. This
analysis showed that concentrations of mercury in 'as-
fired' or 'as-received' coal are about half of earlier
estimates based on 'in-ground' coal samples.
Field Measurements and Data Correlations
"EPRI initiated its Field Chemical Emissions Monitoring
(FCEM) program to rectify the lack of adequate field
measurement data on trace substance emissions from
operating power plants. FCEM provided the first such
data for trace substances in power plant process and
discharge streams. Initiated in May 1990 as part of
the PISCES project, the FCEM program used EPA-
recommended sampling analysis protocols (making
modifications, as necessary), and acquired measurement
data from 35 utility sites representing different
combinations of boiler, fuel type, and environmental
control devices. In addition, the U.S. Department of
Energy (DOE) also conducted sampling and measurement
programs at 8 utility plants, using similar protocols.
The EPRI and DOE data encompass measurements for each
major fuel type and boiler configuration, as well as
all current SO2, NOX, and particulate control
technologies. The resulting database represents the
most up-to-date, complete, and accurate data set
currently available for estimating trace substance
emissions from the national population of steam
electric generating units.
"For coal-fired units, guidelines were developed for
extrapolating the measurement data to predict trace
emissions from similar units. Three major groupings
were defined in order to develop emission factors or
1-5
-------
correlations that serve as the basis for predicting
stack emissions for all coal-fired generating units.
"Particulate-phase inorganic substances. Based on the
field data, these substances (e.g., arsenic, chromium)
are well controlled by a particulate control device.
In general, reductions of greater than 90 percent from
levels in the incoming coal were achieved.
Correlations were thus developed to estimate stack
emissions based on the inlet coal concentration of each
substance and the level of total particulate emissions.
"Volatile inorganic substances. These substances
(including hydrochloric acid, mercury, and selenium)
tend to be more volatile and not efficiently captured
by particulate control devices. Based on the
measurement data, the emissions of these substances
could not be correlated to any specific factors and
were therefore estimated using average removal
efficiencies for each substance and control
configuration.
"Organic compounds. These compounds are formed at
very low levels during combustion and emitted in
concentrations of parts per billion or lower.
Emissions of organic compounds were estimated using the
geometric means of measured emission factors,
calculated from the field data, for each substance.
"For oil- and gas-fired power plants, available data
are not yet adequate to estimate the trace substance
concentrations in fuel burned at individual utility
sites on a nationwide basis. Emissions for these
plants were calculated using average emission factors
(i.e., emissions per Btu of heat input), based on the
field measurements, averaged across all measured units
of the same configuration and fuel type. These data
show that the emission factors for uncontrolled oil-
fired power plants are about the same as for coal-fired
plants with electrostatic precipitat,ors.
Inhalation Exposure Assessment
"As noted above, EPRI developed an alternative measure
of human exposure designed to provide a more realistic
estimate of potential human health effects than the
traditional 'Maximally Exposed Individual,' or MEI,
approach. The 'Reasonably Exposed Individual,' or REI,
1-6
-------
employed in this report still focuses on an individual
living in the area of highest concentrations due to
utility emissions. However, the REI methodology
incorporates data on age-specific and activity-specific
breathing rates and other exposure variables. In
addition, the REI approach assumes that generation
units do not continue to operate for a full 70 years
from their respective start-up dates, but are replaced
after an average operating span of 45 years with units
in the same location that meet the EPA 1994 New Source
Performance Standards (NSPS) for particulates.
wln general, inhalation exposures to carcinogens using
the REI methodology are 2% to 19% of the exposure
levels computed for the MEI. This difference is due
primarily to assumptions regarding the amount of time
individuals spend indoors and the amount of time spent
residing in one location. For the noncarcinogens, REI
exposures are 21% to 70% of those computed for the MEI.
This difference is primarily due to assumptions
regarding the amount of time spent indoors and the
reduction in trace substance concentration levels in
indoor environments.
Health Effects
"Arsenic. Based on new analyses of occupational
exposure data, EPRI computed a revised unit risk factor
for estimating increased cancer risks due to inhalation
exposures to arsenic. The revised unit risk factor
(1.43 x 10"3 per ug/m3) , which is used as the base case
throughout this study, is one-third of the current EPA
value. The revised value reflects a re-analysis of
existing and new occupational exposure data for smelter
workers exposed to arsenic in copper smelter dust.
EPRI used the standard EPA risk assessment methodology
to calculate the revised unit risk.
"Other important issues with respect to the toxicity of
arsenic in power plant fly ash may not be addressed by
the revised unit risk factor and remain the subject of
ongoing research at EPRI and elsewhere. These issues
include: (1) the importance with respect to health
effects of differences in the valence state of arsenic
found in copper smelter dust and that found in fly ash,
(2) the comparative bioavailability of arsenic in fly
ash vs. in other mixtures, and (3) the impact of
metabolic detoxification processes at various arsenic
1-7
-------
exposure levels. Although research is underway on
these topics, they currently remain unresolved.
"Mercury. Recent data also may provide an improved
basis for computing potential neurotoxic effects due to
chronic exposures to mercury. EPA's current reference
dose for methylmercury is based on an incident in Iraq
involving acute exposures to very high methylmercury
concentrations in grain. However, data sets based on
populations exposed to mercury via fish ingestion may
be more appropriate for evaluating health risks from
utility mercury emissions in the United States. EPRI
is currently assessing data on the neurological
responses of children in New Zealand exposed to
methylmercury via maternal fish ingestion to serve as
an alternative basis for evaluating risk.
Inhalation Risk Assessment
"The results of research in a number of areas were
brought together in a nationwide assessment of the
potential health risks associated with trace substance
emissions from electric utility steam generating units.
In general, the analysis indicates that the cancer and
noncancer inhalation risks to the general public due to
trace substance emissions from utility generating units
are small, as described in the following discussion.
"Cancer Risk. For the roughly 600 plants investigated,
the expected increase in individual cancer risk,
incorporating exposure assumptions associated with
maximum exposure over a 70-year life span, did not
exceed 1.7 in one million (1.7 x 10"6) . Out of this
entire group of power plants, only 3 plants, or 0.5
percent, approach exposures leading to a cancer risk
greater than one in one million (1 x 10"6) for a
maximally exposed individual (MEI).
"Incorporating more reasonable assumptions regarding
individual exposure patterns results in the increased
cancer risks for all plants being less than one in one
million, and all but 2 plants (0.3 percent) being less
than one in ten million (1 x 10"7) . Figure ES-1 [not
included in this appendix] shows the distribution of
increased cancer risk for a 'Reasonably Exposed
Individual' (REI) due to utility trace substance
emissions. As shown, the vast majority of plants
(greater than 85 percent) are associated with increased
1-8
-------
individual risk levels that are, at most, below 1 in
100 million (1 x 1CT8) .
"For coal- and oil-fired power plants, across all coal
ranks (i.e., bituminous, subbituminous, and lignite)
and control configurations, arsenic and chromium were
found to be the largest potential contributors to
inhalation cancer risks from utilities.
wFor plants fired only by gas, the median inhalation
cancer risk is about an order of magnitude less than
median risk levels for other fossil-fired plants, and
the primary contributors to risk from gas plant
emissions are chromium and formaldehyde.
"Noncancer Risks. Noncancer risks were evaluated based
on comparisons with EPA-defined reference doses (RfD)
or reference concentrations (RfC). When no EPA-listed
value was available, an RfD or RfC was developed based
on other available health standards. These RfD and RfC
values reflect thresholds below which no adverse health
effects are anticipated, even over continuous long-term
exposures. For all of the plants, inhalation exposures
to all of the noncarcinogens examined (including
mercury) were well below the recommended threshold
levels.
"Sensitivity and Scenario Analyses. Sensitivity
analyses were conducted to clarify the impact of
uncertainty in key parameters or modeling assumptions
on the inhalation risk estimates. Based on extensive
analyses, no single group of plants (as defined by
plant configuration, operating characteristics, stack
height, or fuel type) could be identified as
consistently correlated with relatively high risk
estimates. Rather, it was usually a unique combination
of site- and plant-specific factors that led to higher
relative risks for an individual plant.
"Finally, although variations in assumptions about
future scenarios (e.g., load, fuel type, control
configuration, etc.) can influence risk estimates for
individual plants and the relative risks across
multiple plants, in the aggregate, the alternative
scenarios did not significantly affect the risk
estimates.
1-9
-------
Multimedia Risk Assessment
"EPRI conducted case studies of four power plants with
measured emissions to estimate carcinogenic and
noncarcinogenic multimedia risks from power plant
emissions using the EPRI multimedia risk model, TRUE
(Total Risk of Utility Emissions). Based on the four
case studies, the estimated maximum incremental cancer
risk due to exposures through all pathways (i.e.,
inhalation, ingestion, dermal contact) was below one in
one million (1 x 10"6) for all plants studied. For
noncarcinogens, the multimedia analysis also showed all
exposure levels to be below the relevant threshold
levels (RfDs and RfCs) for adverse effects.
"Mercury. A key focus of the multimedia risk
assessment was the potential health effects due to
mercury emissions. The case study results suggest that
risks due to power plant emissions of mercury are
primarily driven by exposure through ingestion of fish
in which mercury has accumulated as methylmercury. The
current EPA Reference Dose for mercury is 0.3 ug per kg
of body weight per day of methylmercury. Predicted
methylmercury exposure levels due to mercury emissions
from each of the four case study power plants are all
less than 30 percent of federal reference levels.
"Although these results incorporate the best current
understanding of mercury-related risks, the complex
biogeochemistry of mercury, how it is transformed in
the atmosphere and ecosystems, and what exposure and
dose levels ultimately result in health effects are
still not fully understood. As new and ongoing
research contributes to our understanding of this
chemical, potential impacts on the risk assessment
results should be considered.
"Radionuclides. Exposures due to radionuclide
emissions from fossil-fired generating units were also
found to be small. Changes in the risk assessment
methodology for radionuclides used by EPA resulted in
risk levels below previous estimates made by EPA in its
1989 National Emissions Standards for Hazardous Air
Pollutants (NESHAPS) for radionuclides. Specifically,
improvements in estimating power plant radionuclide
emissions (based on the average radioactivity of
emitted particulate matter) resulted in predicted
emissions that are one-third to one-tenth of previous
1-10
-------
estimates. Changes in modeled deposition rates further
reduced predicted exposure levels.
Impact: of Current Control Technologies
"Trace metals in flue gas are normally condensed on fly
ash particles and can be removed effectively by an
efficient particulate collector. Mercury, present
mainly in vapor form in the flue gas, is not collected
effectively by particulate control devices such as
electrostatic precipitators or baghouses. Studies to
date to assess flue gas mercury removal methods, such
as injection of activated carbon, show that the low
mercury levels present in power plant flue gas are much
more difficult to remove than the mercury emitted from
waste incineration plants. Significantly more research
is needed to evaluate these and other removal options
in power plant settings.
SUMMARY
"This report is intended to provide insight into the
best data and methods available for estimating health
risks due to trace emissions from fossil-fuel-fired
steam-electric generating units. To meet this goal,
EPRI conducted extensive research aimed at improving
the state-of-the-art in a number of areas, including:
• More appropriate fuel composition data
• More accurately measuring trace substance
emissions from power plant stacks
• Improved methods for estimating emissions for the
national capacity of power plants
• Development of alternative scenarios of future
industry operations
• Updated health impact data
• Development of reasonable measures of human
exposure and health risks
Although the research presented in this report provides
a considerable improvement over previously available
data and methods, important uncertainties remain. For
example, more complete data are needed on: speciation
1-11
-------
of arsenic, chromium, and mercury in stack emissions;
the atmospheric chemistry of trace substances; and
dose-response information incorporating
bioavailability. However, the results presented herein
suggest that trace substance emissions from electric
utility steam generating units, after compliance with
other provisions of the CAAA, will not pose significant
long-term risks (either carcinogenic or
noncarcinogenic) to human health."
EPA has reviewed the above report and has noted several
differences in the approach and assumptions used by the Agency
and the EPRI. EPRI used different cancer potencies for the three
major trace metals: arsenic, chromium, and nickel. For nickel,
which has a limited number of compounds known to be carcinogenic,
EPRI assumed those nickel compounds emitted from utility power
plants were not carcinogenic, whereas EPA assumed that various
fractions (up to 100 percent) of the nickel was carcinogenic.
For chromium, EPRI assumed that 5 percent of the total chromium
was carcinogenic (the hexavalent form), and EPA assumed that 11
percent of the coal and 18 percent of the oil was carcinogenic.
For arsenic, EPRI reported on some new data that indicated that
arsenic was not as potent as the Agency had estimated; thus, EPRI
used an unit risk estimate of 1.43 x 10~3, which was about a
factor of 3 lower than the 4.3 x 10~3 value used by EPA. (The
Agency is currently reviewing this new data. Some of these
differences are explained by the recent availability of
speciation data that had not been seen by EPRI staff before their
report was finished. In summary, EPRI applied potency estimates,
for all three trace metals, that were lower than those used by
EPA.
There were other significant differences. For instance, the
Agency estimated the effects of long range transport (using the
RELMAP model) for arsenic and projected these results onto other
trace metals. The EPA also conducted long-range transport
modeling for mercury. EPRI did not attempt this analysis. EPA
conducted a radionuclide analysis for each utility plant in the
United States, while EPRI used several model plants.3 Both EPRI
and EPA conducted multimedia exposure analyses for mercury and
The EPRI model plant approach was similar to that used by the Agency in past
analyses. However, choosing model plants tend to underestimate MIR levels,
since the worst exposure scenarios cannot be known until all affected
facilities are modeled. Modeling all the facilities is a large project.
Because the Agency was investing in the resources to model all facilities for
trace metals, it was decided to do a parallel effort for the radionuclide
analysis to overcome the concern of potentially underestimating the MIR.
1-12
-------
radionuclid.es; however, only the industry evaluated multimedia
effects associated with emissions of arsenic, beryllium, cadmium,
chromium, lead, nickel, vanadium, dioxins/furans, chlorine, and
fluorine compounds. When conducting the mercury modeling
analysis to estimate environmental fate and levels in various
media, the Agency used model plants representing a range of sizes
in typical scenarios, where the industry analyzed several
typically sized plant in actual environmental settings. In
addition, the EPRI estimated non-inhalation exposures to mercury.
However, the EPA has not completed a human exposure assessment
for mercury.
1-13
-------
References
1. Letter and enclosure from Peck, Stephen C., Electric Power
Research Institute, to Maxwell, William H., EPA:ESD.
September 15, 1995. Transmittal of unlicensed Electric
Utility Trace Substances Synthesis Report.
1-14
-------
APPENDIX J - PARAMETER JUSTIFICATIONS, SCENARIO INDEPENDENT
PARAMETERS
-------
This page is intentionally blank.
-------
DISTRIBUTION NOTATION
A comprehensive uncertainty analysis was not conducted as
part of this study. Initially, preliminary parameter probability
distributions were developed. These are listed in Appendicies J
and K. These were not utilized in the generation of quantative
exposure estimates. They are provided as a matter of interest
for the reader.
Unless noted otherwise in the text, distribution notations
are presented as follows.
Distribution
Log (A,B)
Log*(A,B)
Norm (A,B)
U (A,B)
T (A,B,C)
Description
Lognormal distribution with mean A and standard deviation B
Lognormal distribution, but A and B are mean and standard deviation of
underlying normal distribution.
Normal distribution with mean A and standard deviation B
Uniform distribution over the range (A,B)
Triangular distribution over the range (A,C) with mode of B
J-l
-------
J. SCENARIO INDEPENDENT PARAMETERS
This appendix describes the scenario-independent parameters
used in the mercury modeling. Scenario independent parameters
are variables whose values are independent of a particular site
and are constant among various site-specific situations.
Examples of scenario independent parameters are air density, the
average height of an adult, or the average crop yield of a
particular food item. These scenario independent parameters may
be either chemical independent or chemical dependent. The
following sections present the chemical independent and chemical
dependent parameters used in this study.
J.I CHEMICAL INDEPENDENT PARAMETERS
Chemical independent parameters are variables that remain
constant despite the specific contaminant being evaluated. The
chemical independent variables used in this study are described
in the following sections.
J.I.I Basic Constants
Table J-l lists the chemical independent constants used in
the study, their definitions, and values.
J.I.2 Agricultural Parameters
J.I.2.1
Parameter:
Definition:
Units:
Interception Fraction.
RPi
The fraction of the total deposition within a unit
area that is initially intercepted by vegetation.
unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Silage
Fruits
Potatoes
Default Value
0.15
0.008
0.05
0
0
0.47
0.44
0.05
0
Distribution
Log (0.16, 0.10)
Log(0.008, 0.004)
Log{0.05, 0.05)
N/A
N/A
Norm{0.47, 0.3)
Log (0.44, 0.3)
Log (0.05, 0.05)
N/A
Range
0.08 - 0.38
0.005 - 0.01
0.004 - 0.08
N/A
N/A
0.02-0.89
0.004 - 0.08
N/A
J-2
-------
Table J-l. Chemical Independent Constants
Parameter
R
pa
ua
Psed
Cdrag
K
A2
Description
ideal gas constant
air density
viscosity of air
solids density
drag coefficient
Von Karman's coefficient
boundary thickness
Value
8.21E-5 m3-atm/mole-K
1.19E-3g/cm3
1 .84E-4 g/cm-second
2.7 kg/L
1.1 E-3
7.40E-1
4.0
Technical Basis:
For leafy vegetables, Baes et al.1 obtained an average
interception fraction of 0.15 where it was emphasized that this
value represents a theoretical average over the United States.
This value was calculated assuming a logistic growth pattern for
leafy vegetables and taking into account a distribution of field
spacings.1 The associated distribution and ranges shown in the
previous table were calculated based on Baes's analyses by
Belcher and Travis.2
For legumes and fruits, Belcher and Travis2 used the exposed
produce equation that relates the interception fraction to the
standing crop biomass (also called productivity) and crop biomass
values from Shor et al. ,3 to obtain the range of values given in
the previous table. The values for fruiting vegetables are
assumed to be the same as for fruits.
The distribution for forage is based on the work of Hoffman
and Baes,4 who determined that the values are normally
distributed with the parameters presented in the previous table.
The value for silage was calculated in Baes et al.1 and is
based essentially on sorghum and corn plantings.5'6
Potatoes, root vegetables and grains are assumed to equal
zero since the edible portion of the plant is protected from
direct deposition (grains have a protective husk).
J.I.2.2
Parameter:
Length of Plant Exposure.
TPi
J-3
-------
Definition.
Units:
The amount of time that the edible part of an
exposed plant is exposed to direct deposition,
years
Plant Type
Leafy vegetables
Legume vegetables
Fruiting vegetables
Forage
Silage
Fruits
Default Value
(years)
0.157
0.123
0.123
0.123
0.123
0.123
Distribution
11(0.082,0.247)
U{0.082,0.247)
U(0.082,0.247)
UfO.082,0.247)
U(0.082,0.247)
U(0.082,0.247)
Range (years)
0.082- 0.247
0.082- 0.247
0.082 - 0.247
0.082 - 0.247
0.082 - 0.247
0.082 - 0.247
Technical Basis:
Bounding estimates were obtained by assuming an average time
between successive harvests of 30 and 90 days. This range is
based on the values in Baes et al.1 of 60 to 90 days and the
reported values by the South Coast Air Quality Management
District (SCAQMD)7 of 45 days for tomatoes and 30-85 days for
lettuce.
The default value for leafy vegetables is the midpoint of
the range for lettuce. The values for legumes, fruits and
fruiting vegetables are based on the value of 45 days for
tomatoes. The value for forage and silage is the average time
between successive hay harvests and successive grazings by
cattle.1
J.I.2.3
Parameter:
Plant Yield.
YPi
Definition: Yield of the ith plant per unit area.
Units: kg (dry weight)/m2
Type of Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Default Value (kg (dry
weight)/m2)
0.177
0.104
0.107
Range (kg (dry
weight)/m2)
0.091 - 0.353
0.077-0.130
0.012-0.253
Distribution
Log (0.177,0.086)
Log (0.104, 0.038)
Log(0.107, 0.093)
J-4
-------
Type of Crop
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage
Default Value (kg (dry
weight)/m2)
0.334
0.3
0.31
0.107
0.48
0.84
Range (kg (dry
weight)/m2)
0.090 - 0.434
0.14-0.45
0.02- 0.75
0.012-0.253
0.405 - 0.555
0.3- 1.34
Distribution
Log(0.334, 0.142)
Log (0.30, 0.09)
0.84482993969
Log(0. 107, 0.093)
Log (0.48,0.106)
Log(0.84,0.26)
Technical Basis:
The distributions and ranges shown for all but the silage
values are those used in Belcher and Travis.2 The distributions
selected were chosen based on a probability plot for leafy
vegetables with data in Shor et al.3 The default values are the
means of the distributions. Silage was not considered in Belcher
and Travis,2 but the same method by which the default values and
distributions were calculated there were replicated using data
from Shor et al.3 for the purpose of this assessment.
J.I.2.4 Plant Ingestion by Animals.
Parameter: QPij
Definition:
Units:
The daily consumption of plants by livestock.
kg dry weight/day
Livestock Consumption
of Plants
Beef/Beef Liver
grain
forage
silage
Dairy
grain
forage
silage
Pork
grain
Default Value
(kg dry weight/day)
0.97
8.80
2.50
2.60
11.0
3.30
3.0
Distribution
U(0. 5,6.5)
U(2.0,9.0)
U(1,5)
11(0.5,6.5)
U(7,15)
U(1,5)
U(2,4)
Range
(kg dry weight/day)
0.5-6.5
2.0-9.0
1.0-5.0
0.5-6.5
7.0-15.0
1.0-5.0
2.0-4.0
J-5
-------
Livestock Consumption
of Plants
silage
Sheep (lamb)
forage
Poultry/Eggs
grain
Default Value
(kg dry weight/day)
1.3
1.1
0.08
Distribution
U(0.5,3)
U(0,2)
11(0.04,0.10)
Range
(kg dry weight/day)
0.5-3.0
0.0-2.0
0.04-0.10
Technical Basis:
With the exception of the beef liver, egg and lamb-forage
values, the default values are from U.S. EPA (1990).8 The value
for beef liver is assumed to be the same as for cattle, and the
value for eggs is assumed to be the same as for poultry. The
value for lamb-forage is from the National Academy of Sciences.9
The ranges shown are based on a combination of the ranges
determined by Belcher and Travis,2 the U.S. EPA8 values, and the
objective of capturing all of the most likely values.
Although lognormal distributions were chosen in Belcher and
Travis,2 this was not based on the actual distribution of the
available data; that is, no probability plots were done. For
that reason, uniform distributions are suggested here.
J.I.2.5 Soil Ingestion by Animals.
Parameter: QSj
Definition:
Quantity of soil ingested daily by the a specific
animal.
Units: kg/day
Technical Basis:
The values for beef cattle and dairy cattle are from McKone
and Ryan.10 The value for beef liver is assumed to be the same
as for beef. The value for pork is the mean of the distributions
used in Belcher and Travis2 and are based on values in Fries.11
The sheep value is from Fries.12 The value for poultry is the
mean of the distribution used in the Hanford Environmental Dose
Reconstruction Project13 (HEDR) and is based on values for
free-ranging chickens. The range is that used in HEDR (1992) .13
J-6
-------
Livestock
Beef/beef liver
Dairy
Pork
Sheep (lamb)
Poultry/eggs
Default Value
(kg/day)
0.39
0.41
0.034
0.05
0.009
Range
(kg/day)
0.1 -0.72
0.1 -0.72
0.0 - 0.0688
0.01 -0.15
0.006-0.012
For beef, dairy and pork, the ranges are from Belcher and
Travis.2
The range for sheep is based on the values reported in
Fries.12 The lower end of the range is for sheep that are fed in
a lot, in which case they eat little soil. The upper end is
based on sheep grazing on poor pasture land.
J.2 CHEMICAL DEPENDENT PARAMETERS
Chemical dependent parameters are variables that change
depending on the specific contaminant being evaluated. The
chemical dependent variables used in this study are described in
the following sections.
J.2.I Basic Chemical Properties
The following sections list the chemical properties used in
the study, their definitions, and values.
J.2.1.1 Molecular Weight.
Parameter: Mw
Definition:
Units:
The mass in grams of one mole of molecules of a
compound.
g/mole
Chemical
Hg°, Hg2+
Methylmercury
Methyl mercuric chloride
Mercuric chloride
Default Value (g/mole)
201
216
251
272
J-7
-------
J. 2 ,1.2
Parameter:
Definition:
Units:
Henry's Law Constant.
H
Provides a measure of the extent of chemical
partitioning between air and water at equilibrium.
atm-m3/mole
Chemical
Hg°
Hg2+ (HgCI2)
Methylmercury
Default Value (atm-m3/mole)
7.1x10'3
7.1X10'10
4.7x1 0'7
Technical Basis:
The higher the Henry's Law Constant, the more likely a
chemical is to volatilize than to remain in the water. The value
for Hg° is from Iverfeldt and Persson,14 while the other values
are from Lindquist and Rodhe.15
J.2.1. Soil-Water Partition Coefficient
Parameter:
Definition:
Units:
Kd
Equilibrium concentration in dry soil divided by
concentration in water.
mL/g
Chemical
Hg*+
Methylmercury
Default Value (mL/g)
53,700
53,700
Technical Basis:
Values in the previous table are the geometric mean of
calibrated values (see Appendix L).
J.2.1.4 Sediment-to-Water Partition Coefficient.
Parame ter: Kdb
Definition:
Units:
Equilibrium concentration in dry sediment divided
by concentration in water.
mL/g
J-8
-------
Chemical
Hg2+
Methylmercury
Default Value (mL/g) 1
1 57,000 I
157,000 1
Technical Basis:
Values in the previous table are the geometric mean of
calibrated values (see Appendix L).
J.2.1.5 Suspended Sediment-Water Partition Coefficient.
Parameter: Kdw
Definition:
Units:
Suspended sediment-water partition coefficient,
L/kg
Chemical
Hg2+
Methylmercury
Default Value (L/kg)
95000
650000
Range
1340-188,000
320,000 - 1 ,000,000
Technical Basis:
For divalent mercury, data were available from three
studies, and are shown in Table J-2. The default value is the
midpoint of the range. For methylmercury, the only data found
that specifically address suspended material are those in Bloom
et al.16 In particular, they report that "Regardless of pH, for
over three orders of magnitude, the log Kd for seston [suspended
matter] was in the range of 5.5 to 6.0." The range listed in the
previous table corresponds to this range. The midpoint of the
observed range is used as the default value.
J.2.1.6
Parameter:
Soil and Water Loss Degradation Constants.
ksg and kwg
J-9
-------
Table J-2. Ranges of Values for Suspended Sediment-to-Water
Partition Coefficient
Range (L/kg)
1380-188,000
118,000
86,800-113,000
Reference
Moore and Ramamodoray17
Glass et al.18
Robinson and Shuman19
Definition.
Units:
Soil and water body loss of the contaminant due to
biotic and abiotic degradation and aqueous
hydrolysis, respectively.
/yr
Chemical
Hg°
Hg*+
Methylmercury
Default Value (year)
0.0
0.0
0.0
Range
N/A
N/A
N/A
Technical Basis:
Data indicate that equilibrium is established between
different species of mercury rather than a degradation/breakdown
process. Parks et al.,20 found that "In water, me thy liner cury and
inorganic appear to be in quasi-equilibrium, as the
methyImercury/total mercury ratio in river water is independent
of contact time with sediments, the atmosphere, and the
theoretical residence time of waters." For this reason, it
appears reasonable simply to assume no net loss with time if any
mercury species occurs in either soil or water.
J.2.1.7
Parameter:
Definition:
Units:
Equilibrium Fraction for Chemical in Soil.
fspecs
For all chemicals tied together in soil
equilibrium, the fraction which is chemical i is
given by fspec.
unitless
J-10
-------
Chemical
Hg°
Hg2+
Methylmercury
Default Value
0
0.98
0.02
Distribution
None
1(0.9,0.98,0.9998)
1 percent Hg2+
Technical Basis:
Akagi et al.21 reported methylmercury fractions of 0.02,
0.072 and 0.089 for sand, silt/woodchips, and woodchip sediments
as compared to total mercury. Wilken and Hintelmann22 reported
that 0.10 of the total mercury in sediments from the River Elbe
in Germany is methylated, although they pointed out that others
had reported maximums of 0.01 and 0.02. Hildebrand et al.23 found
methylmercury fractions of 0.0002 - 0.0005 in sediments from the
Holston River, VA.
The measurements in the previous table did not distinguish
between Hg2* and Hg° in the remaining fractions, leaving the
partitioning of these species in soil uncertain. It is known
that Hg° can be formed from reduction of Hg2+ in the soil
environment, a fraction of which will volatilize and a fraction
of which can be bound to organic matter. Both processes depend
strongly on soil conditions.24 At the redox potential normally
found in soils, however,
predominant than Hg°.
Hg2* complexes are expected to be
Cappon25 found that percent of methylmercury over total
mercury for nonamended soils is 2.6 percent. This is an upper
bound on values from unpublished data reported by several
authors.26
J.2.1.8 Equilibrium Fraction for Chemical in Water.
Parameter: fspecw
Definition:
Units:
For all chemicals tied together in water
equilibrium, the fraction which is chemical i is
given by fspecw.
unitless
Chemical
Hg°
Hg2+
Methylmercury
Default Value
0.02
0.83
0.15
Distribution
NA
1 - (Methyl + Hg° %)'
Log(0.14,1.0)
Range
0.007 - 0.04
0.31 - 0.96
0.03 - 0.65
' The distribution is 1 minus the methylmercury concentration and elemental mercury concentration
dissolved in the water.
J-ll
-------
Technical Basis:
The default value given for methylmercury is that suggested
in U.S. EPA.27 In well oxygenated water, the remaining fraction
(i.e., non-methylated) will be mainly Hg2* complexes-24 There will
be a small fraction of total mercury in water that will be Hg°
due to reduction of Hg2+ by humic acid and microorganisms.24'28
Fitzgerald et al.29 measured the concentration of total
dissolved gaseous mercury in various lake waters and found in all
cases that it consisted mainly of elemental mercury (> 97
percent). Much of these measurements were taken at both basins
of Little Rock Lake, WI, from which total mercury concentrations
for the acid-treatment and reference basins are known from the
work of Watras and Bloom.30 Comparing the concentrations within
each basin gives a possible range for the percent mercury in
water that is Hg° of 0.7 - 4 percent, the midpoint of which (2
percent) we use as the default equilibrium percentage of mercury
in the water column that is elemental mercury.
There are a wealth of data on the Methylmercury/Total
mercury in the water column. Table J-3 lists the values found
reported in the literature. These values were used to determine
the range given previously for methylmercury. The range for Hg2+
is then given by subtracting the contributions from methylmercury
and elemental mercury from the total.
J.2.2 Biotransfer Factors
Biotransfer factors reflect the extent of chemical
partitioning between a biological medium (plants, meats or fish)
and an external medium (air, soil or water). The following
sections describe the BCFs used in this study.
It is necessary to note the uncertainty inherent in
determining BCFs for mercury species with regard to plant uptake.
In general, there seems to be no consensus in the literature on
plant bioconcentration factors for mercury, as values for each
crop vary widely among studies. Further, in many studies the
mercury speciation is not determined. In deriving BCFs for plant
absorption of mercury species fromthe air and soil, it was,
therefore sometimes necessary to make assumptions about certain
behaviors ofmercury based on whatever information was at hand, as
opposed to established scientific knowledg, which was lacking.
These assumptions are described in each Technical Basis section
J-12
-------
Table J-3. Reported Values for Fraction of Total Mercury that is
Methylmercury in Water
Values
0.26,0.11, 0.07,0.07,0.15
0.01, 0.022, 0.019, 0.054,
0.055, 0.052, 0.049, 0.064
0.32, 0.48, 0.57
0.12,0.05
< 0.025
0.04-0.05
0.26-0.46
0.01-0.89
0.036-0.273
0.036-0.053
Reference
Bloom et al.31
Parks et al.20
Akagi et al.21
Watras and Bloom30
Bloom and Watras32
Lee et al.33
Kudo et al.34
Gill and Bruland35
Bloom and Effler36
Lee and Hultberg37
that follows, but it is useful at this time to identify some of
the general uncertainties regarding plant uptake of mercury.
(1) Plants both absorb and release mercury to the
environment. Hanson et al.38 demonstrates clearly that
at ambient air concentrations forest foliage usually
acts as a source of elemental mercury to the
atmosphere; deposition (plant absorption) only occurs
above a "compensation concentration" at air mercury
levels well above background. It is not yet known from
where the mercury released by the plants originates
(air uptake during periods of high mercury air
concentrations, root uptake, Hg(II) absorption, etc.).
Similarly, Mosbaek39 found that for a given period of
time more elemental mercury was released from a
plant-soil system than was absorbed by the plant.
These cases, however, in no way indicate that mercury
is not bioconcentrated in plants; the above behaviors
are consistent with mercury being collected by plants
only to certain levels, after which any mercury
absorbed is simply released.
(2) It is usually not known from where the mercury that is
found in plants originated (air vs. soil). Only one
study determined the fractions of total mercury in
plants which came from air and soil39-' in this study,
J-13
-------
soil was isotopicly labelled with 203Hg. After some
time the specific activity in the plant was compared to
that in the soil to ascertain how much of the mercury
in the plant came from the soil. Although the
experiment worked well, isotopic equilibrium in the
soil was never achieved, and the number of plants
studied was limited.
(3) The speciation of mercury in plants is often not known.
If it is known, it is still very unclear as to how the
speciation occurred. The plant speciation may be
simply a result of direct uptake of different mercury
species from the environment (but from air or soil?) .
It has been shown, however, that a few plants have the
ability to change the species of mercury initially
taken up from the environment.40 Such behavior may
have to be accounted for regarding plant uptake of
mercury.
J.2.2.1
Parameter:
Definition:
Units:
Plant-Soil BCF.
BRi
The ratio of the contaminant concentration in
plants (based on dry weight) .to that in the soil,
Unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage
Hg2+
Default
Value
0
0.015
0.018
0.036
0.0093
0
0.018
0.1
0
Distribution
None
U(0.00026,
0.157)
UIO.007,0.059)
LM0.011, 0.073)
11(0.0024,0.057)
None
11(0.007-0.059)
11(0.05,0.2)
None
Methylmercury
Default
Value
0
0.031
0.024
0.099
0.019"
0
0.024
0.2"
0
Distribution
None
UfO.O,
0.090)
U{0.0,0.11)
U(0.01 3,0.29)
U(0.0048,0.11)'
None
11(0.0,0.11)
U(0.1,0.4)a
None
Hg2+ values multiplied by 2
J-14
-------
Technical Basis:
Mosbaek39 convincingly showed that for leafy, above-ground
parts of plants virtually all of the mercury uptake was from air;
therefore, for leafy vegetables, forage and silage no root uptake
was modeled.
Values in Cappon41-42 were the only data located which
measured methylmercury concentrations in plants, and
methylmercury plant-soil BCF's were determined for rooting
vegetables, fruiting vegetables, and legumes. Values were
determined for crops grown on compost42 and sludge-treated
soils,41 and those values considering edible portions of plants
are shown in Table J-4.
It has been shown, however, that mercury taken up into
plants from the environment can be transformed into other mercury
species, especially to organomercuric forms such as
methylmercury.43 The methylmercury in plants, therefore, may not
have been directly absorbed from the environment. For the
purposes of this study, considering root uptake, methylmercury
concentrations in plants were treated as though they originated
from the soil. It is also important to note that air-to-plant
transfer may have occurred, but the Cappon41'42 study was not
designed to measure air-uptake.
Table J-5 shows additional soil-to-plant transfer
coefficients for Hg2+ species (it was assumed that all the
mercury in the soil is Hg2+, which at worst would result in an
error of a few percent in the Hg2+ soil-to-plant transfer
coefficients) determined from a number of studies. Temple and
Linzon44 sampled garden produce in the vicinity of a chlor-alkali
plant. Lenka et al.45'46'47 also measured mercury concentrations in
soil and plants near a chlor-alkali plant. Somu et al.48
determined mercury uptake in wheat and beans grown on HgCl2
contaminated soil. John49 determined mercury concentrations in
plants grown on soil artificially contaminated with HgCl2.
Wiersma et al.50 measured soil and plant total mercury
concentrations from major growing areas in the Netherlands.
Belcher and Travis2 compiled data from EPA.51 Mosbaek39 studied
plant concentrations from soil and air uptake under background
conditions. For studies reporting wet weight plant
concentrations, wet weight to dry weight conversion factors in
Baes et al.x were used to convert to dry weight based
concentrations.
When possible, default values were chosen based on
experiments under reasonable or background conditions, as opposed
J-15
-------
Table J-4. Soil-to-Plant Transfer Coefficients for Mercury (from
Cappon, 1987 and Cappon, 1981)
Crop
1987 Values
Hg2+
Methylmercury
1981 Values
Hg*+
Methylmercury
Rooting Vegetables
Beet
Carrot
Onion, Yellow
Onion, Spanish
Red Radish
White Radish
Turnip
Cucumber, slicing
Cucumber, pickle
Pepper
Zucchini
Summer Squash
Acorn Squash
Spaghetti Squash
Pumpkin
Tomato
Green Bush Beans
Yellow Bush Beans
Lima Beans
0.055
0.026
0.073
-
0.056
-
0.026
0.227
0.118
0.288
-
0.092
-
0.013
0.017
0.014
0.053
0.047
0.018
0.011
-
0.11
0.048
0.042
0.030
0.066
0.060
-
Fruiting Vegetables
-
0.007
0.019
0.021
-
-
-
-
0.059
-
0
0.022
0
-
-
-
-
0.105
Legumes
0.011
-
-
0
-
-
0.015
0.015
0.016
0.014
0.007
0.016
0.016
0.008
0.020
0
0.006
0.042
0.018
0
0.012
0.024
0.006
0.072
0.014
0.017
0.017
0.020
0.015
0.090
J-16
-------
Table J-5.
for Hg2+
Other Values for Soil-to-Plant Transfer Coefficients
Crop
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Fruits
Potatoes
Values
0.157-1.79, 0.00026-0.0003,
0.0005, 0.003-0.03
0.013-0.33, 0.127-1.36,
0.0078-0.028
0.09-0.33,0.090-0.149,
0.0065-0.013, 0.05-0.2,1.6-1.9
0.0024-0.0093, 0.0033,
0.00038-0.057
0.0078-0.028
0.05-0.2
References
Lenka et al.,46-46-47 Somu et al.,48
John,49 Belcher and Travis2
Temple and Linzon,44 Lenka et
a, (45.46,47 Be|cner and Travis2
Temple and Linzon,44 Lenka et
al.,45.46.47 john/9 Belcher and
Travis,2 Mosbaek39
Somu et al.,48 John,49 Belcher and
Travis,2
Belcher and Travis2
Belcher and Travis2
to experiments where the soil was "spiked" with large amounts of
mercury or measurements were taken from severely polluted areas.
This is actually a conservative approach; although plants from
mercury polluted areas will have greater contaminate levels, the
efficiency of accumulation (quantified in the transfer
coefficients) tends to decrease with increasing contaminate
concentrations. Values from Cappon42'41 were used when possible,
since these experiments were conducted under reasonable garden
conditions, edible portions of plants were analyzed separately,
and different mercury species were measured. Cappon41 analyzed
plants grown in control soil (total mercury soil content of 120
ng/g with 4.2 percent methylmercury) in addition to the sludged
soil (330 ng/g with 5.1 percent methylmercury, which is
comparable to the 1987 soil levels of 430 ng/g with 5.3 percent
methylmercury). The control soil data were not used since the
methylmercury levels were often undetectable. Note that the
compost and sludge-amended soils, although elevated in mercury,
are nonetheless at reasonable concentrations. For fruiting
vegetables, rooting vegetables and legumes values from Cappon42
and values derived from the edible potions of plants grown on
sludged soil from Cappon41 were pooled and averaged; the results
were used as the defaults for these plant types.
Default Hg2* values for grains and cereals are from Somu;48
the methylmercury values were assumed to be twice as great in
accordance with the overall average trend noted in plants from
the pooled Cappon data. The default values for fruits were
assumed to be the same as for fruiting vegetables. The default
Hg2+ value for potatoes was taken from Belcher and Travis;2 the
J-17
-------
methylmercury value for potatoes was assumed to be twice the Hg2+
value.
J.2.2.2 Air-Plant BCF.
Parameter:
Definition:
Units:
BI
The ratio of the contaminant concentration in
plants (based on dry weight) to that in the air.
Unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage
Hg2+1
Default
Value
18000
1050
22000
0
1050
18000
22000
0
18000
Distribution
Ut 12000,24000]
U[700,1400]
U[14000,29000]
NA
U[700,1400]
1)112000,24000]
11(14000,29000]
NA
U[ 12000,24000]
Methylmercury'
Default Value
5000
100
1200
0
100
5000
1200
0
5000
Distribution
U[3300,6800]
U[65,130]
U[780,1600]
NA
U[65,130]
U[3300,6800]
U[780,1600]
NA
U[3300,6800]
* Based on elemental mercury air concentration, and speciation of divalent and methylmercury species based
on Cappon.41-42
Technical Basis:
Mosbaek39 determined that mercury concentration in the
above-ground, leafy parts of plants is almost entirely the result
of air-to-plant transfer of mercury. Cappon,41'42 however, found
only divalent and methylmercury in these types of plants.
Fitzgerald52 noted that up to 99 percent of the total airborne
mercury is Hg° vapor. It was assumed that any atmospheric
elemental mercury taken up by the plant is converted into Hg2*
and methylmercury in the plant tissue. This is not unreasonable:
it has been shown that mercury taken up into plants from the
environment can be transformed into other mercury species.43
A strong correlation between mercury soil concentration and
concentration in rooting vegetables has been established,45"47'49'53
and the Mosbaek study39 demonstrated that much of the mercury in
J-18
-------
rooting vegetables was from the soil. As a result, air-to-plant
uptake of mercury was not modeled for rooting vegetables and
potatoes.
For grains, fruits, legumes and fruiting vegetables, little
correlation between mercury plant concentrations and either air
or soil concentrations has been found; however, non-negligible
concentrations of mercury species in these plants are routinely
observed. For this reason, both air-to-plant and soil-to-plant
uptake was modeled for these plants. Using a conservative
approach, the transfer factors for each accumulation pathway were
calculated as if all of the mercury in the plant came only from
that pathway. This has the effect of possibly double-counting
the amount of mercury in the plant tissue. There is a great deal
of uncertainty due to the lack of applicable data.
The range of air-plant bioconcentration factors based on
Mosbaek et al.39 was found to be 15,000 - 31,000, based on total
mercury concentration in the plant tissue. Mosbeak et al.39
determined average mercury concentrations due to air uptake in
lettuce, radish tops, and grass. Concentrations were converted
to dry weight according to Baes et al.,1 and the overall range of
air-plant bioconcentration factors based on total mercury in the
plant tissue was found to be. 15,000 - 31,000. Air to plant
bioconcentration factors can be derived from other studies only
indirectly (by making a reasonable estimate of the air
concentration and assuming all the mercury in plant tissue comes
from air), and the values arrived at for various plant species
generally fall in'to the previous range. Due to the limited data,
it was decided to use the midpoint of the Mosbeak et al.39
bioconcentration values (23,000) as the starting default for all
plant species assumed to accumulate mercury from the air.
This approach was adjusted for the consideration of portions
of grains and legumes that are not directly exposed to the
atmosphere. Although atmospherically absorbed mercury can
translocate throughout different portions of the plant, data
indicate internal portions of grains and legumes (the edible
portions) do not appear to accumulate mercury to the same degree
as plant leaves or vines. Somu et al.,48 John,49 and Cappon41
determined mercury concentrations from different portions of the
same plants. Table J-6 below shows the relative concentrations
of total mercury found in plant parts from the portions of these
studies representative of noncontaminated conditions.
A clear trend of decreasing mercury concentrations is seen
proceeding from leafy to seed portions of the plants. Based on
these data, it was decided to decrease the default air-to-plant
J-19
-------
Table J-6. Relative Concentration of Mercury in Different Parts
of Edible Plants
Legumes
vines
stalks
pods
seeds
Grains
leaves
stalks
husks
grain
Beans
(Sornu et al.48)
1.0
0.060
Wheat48
1.0
0.14
Peas
(John49)
1.0
0.045
0.0091
Oats49
1.0
0.063
0.61
0.051
Beans
(Cappon41)
1.0
0.028 - 0.089
biconcentration factor of 23,000 by a factor of 20 (to 1200) to
account for the decreasing accumulation of airborne mercury for
the edible portions of these plants as compared to the leafy
portions (for which the biconcentration factor of 23,000 is
applicable). Airborne mercury uptake by fruits may also be
overestimated with the default bioconcentratiori factor. However,
no data are available to explore this possibility.
The product of the bioconcentration factors and the
atmospheric mercury concentration is the total mercury in the
plant tissue resulting from accumulation of airborne elemental
mercury. Plant-specific speciation estimates from Cappon41'42 were
used to partition the total mercury bioconcentration factor (and
corresponding range) in order to model the relative fractions of
methylmercury and Hg2+ found in the plant; these are shown in
Table J-7; note that the rest of the mercury was found to be
divalent mercury.
Thus, for leafy, fruiting and legume vegetables, the
default values for the bioconcentration of methylmercury based
on the elemental mercury concentration in air were assumed to be
23,000 or 1200 multiplied by the average methylmercury
percentages in Table J-6; the Hg2+ values were derived similarly
(Hg2+ fraction x 23,000). The values for fruits were assumed to
be the same as for fruiting vegetables. The values for forage
and silage were assumed the same as for leafy vegetables, and the
J-20
-------
Table J-7. Mercury Speciation in Various Plants
Plant Type
% Methylmercury Cappon41
% Methylmercury Cappon42
Leafy vegetables
Head lettuce
Leaf lettuce
Spinach
Swiss chard, Fordhook
Swiss chard, Ruby Red
Broccoli*
Late Cabbage
Red Cabbage
Savoy King Cabbage
Jersey Wakefield Cabbage"
Cauliflower
Collards
Average
8.8
16.5
19.8
30.2
28.6
33.1
28.8
22.4
25.2
-
21.2
22.8
21.4
18
23.1
14.8
-
17.8
-
-
-
18
-
-
21.8
Legume vegetables
Green Bush Beans
Yellow Bush Beans
Lima Beans
Average
Cucumber, slicing
Cucumber, pickle
Pepper
Zucchini
Summer Squash
Acorn Squash
Spaghetti Squash
Pumpkin
Tomato
Average
0
-
-
7.2
4.3
22.4
8.5
Fruiting vegetables
0
2.1
12.5
6.7
0
4.1
7.4
4.0
16.0
-
0
6.1
0
-
-
-
-
9.1
5.2
• These were classified as "cole" in Cappon
42
J-21
-------
values for grains were assumed to be the same as for legumes
(beans).
J.2.2.3
Parameter:
Definition:
Units:
Animal BTF.
BAj
The equilibrium concentration of a pollutant in an
animal divided by the average daily intake of the
pollutant.
day/kg DW
Livestock
beef
beef liver
dairy
pork
poultry
eggs
lamb
Default Value (day/kg DW)
0.02
0.05
0.02
0.00013
0.11
0.11
0.09
Distribution
INO.0008,0.04)
11(0.02,0.1)
U(0.003,0.09)
11(0.00005,0.00026)
U(0.094,0.13)
U(0.094,0.13)
U(0.009,0.3)
Technical Basis:
Biotransfer factors measure pollutant transfer from the
environment to animal tissues and products. They are defined as
the ratio of pollutant concentration in animal tissue to the
daily pollutant intake of an animal. The biotransfer factors for
mercury to cattle tissues were estimated based on data found in
Vreman et al. ,54 and biotransfer factors for mercury to lamb were
based on data found in van der Veen and Vreman.55
The data collected from Vreman et al.54 and van der Veen and
Vreman55 are not from single pollutant and single route ingestion
studies; rather, the animals in these studies were generally
dosed with elevated levels of several metals in a single wafer.
This is not the ideal set of studies for assessing the transfer
of mercury primarily from ingested grass and soil. These
studies, however are multiple dose and long-term experiments
which should provide data more representative of the desired
equilibrium situation than a single, very large dose experiment.
In two experiments, Vreman et al.54 measured transfer of
mercury from diet to tissues and milk of dairy cattle. In the
first experiment 12 lactating cows/group were placed on pasture
J-22
-------
in 2 groups for 3 months. The control group was fed
uncontaminated wafers and, based on mercury levels in the pasture
grass, were estimated to ingest 0.2 mg mercury/day. The exposed
group received wafers treated with a solution of mercury acetate,
lead, cadmium and arsenic pentoxide; the daily mercury ingestion
rate for the exposed group was 1.7 mg/day. During the experiment
mercury levels in milk were measured. After three months on
test, four cows/group were slaughtered, and mercury levels were
measured in liver, kidney and muscle samples. In the second
study, lactating cows were kept indoors and divided into 4 groups
of 8 for up to 28 months. In addition to the control group, the
diets of 3 other groups were supplemented with the following:
wafers containing the same metals (1.7 mg mercury/day), sludge
delivering dietary levels of 3.1 mg mercury/day, and sludge
delivering dietary levels of 1.2mg mercury/day. Two cows from
each group were slaughtered at study termination (except for the
group receiving 3 .1 mg mercury/day from sludge in which only one
cow was sacrificed) . Mean milk mercury concentrations in the
groups were reported, and mercury levels in the slaughtered cows
were measured in liver, kidney and muscle samples.
Shown in Table J-8 are data from Vreman et al.54 that are
relevant to deriving beef and dairy biotransfer factors. The
tissue mercury concentrations presented are in wet weight.
The data in Table J-8 can be easily converted into milk,
beef and liver biotransfer factors by converting the tissue
concentrations to dry weight and dividing the tissue
concentrations by the daily intake of mercury (after converting
the intake from mg/day to ug/day). The moisture content of the
above tissues are reported in Baes et al. (1984): 0.87 for whole
milk, 0.615 for beef and 0.70 for liver. The biotransfer factors
derived are shown in Table J-9.
Using the number of animals sampled for each value in Table
J-9, weighted averages for the Dairy, Beef and Beef Liver
Biotransfer factors can be derived. These are chosen as the
default values, with the ranges taken from Table J-9.
In a experiment very similar to Vreman et al. ,54 van der
Veen and Vreman55 measured transfer of mercury from diet to
tissues of 10 week old fattening lambs. Two groups of 8 lambs
were placed on pasture for 3 months. The control group was fed
uncontaminated feed concentrate and based on mercury levels in
the pasture grass and uncontaminated feed were estimated to
ingest <0.02 mg mercury/Kg dry feed-day. The exposed group
received feed concentrate treated with a solution of mercury
acetate, lead, cadmium and arsenic pentoxide; the daily mercury
J-23
-------
Table J-8. Mercury Concentrations in Specific Beef Tissue Media
Per Test Group and Dose (from Vreman et al.54)
Test Group
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor
High-level Sludge
Indoor
Low-Level Sludge
Dose
(mg mercury/day)
0.2
1.7
0.2
1.7
3.1
1.2
Mercury in Milk
(ug/Kg WWA)
2.3
0.9
<0.5
0.6
2.4
1.3
Mercury in Muscle
(ug/Kg WWA)
3
4
2
2
1
2
Mercury in Liver
(ug/Kg WWA)
7
10
3
26
14
g
A Wet weight
Table J-9. Animal Biotransfer Factors Derived from Vreman et
al."
Test Group
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor High-Level Sludge
Indoor Low-Level Sludge
Dairy
0.09
0.004
0.02
0.003
0.006
0.008
Biotransfer Factor (day/kg DW)
Beef
0.04
0.006
0.03
0.003
0.0008
0.004
Beef Liver
0.1
0.02
0.05
0.05
0.02
0.03
ingestion rate for the exposed group was 0.08 mg/Kg dry feed.
Another four groups of 8 lambs were kept indoors and were fed hay
and feed concentrate. A control group was fed uncontaminated
feed concentrate, and were estimated to ingest <0.02 mg
mercury/Kg dry feed-day. The 3 other groups were fed feed
concentrate contaminated with, respectively, a soluble solution
of the metals, harbor sludge and sewage sludge. Daily mercury
ingestion rates for these groups ranged from 0.14 - 0.27 mg/Kg
dry feed. After three months all lambs were slaughtered and
mercury levels were measured in liver, kidney, brain and muscle
samples.
Shown in Table J-10 are data from van der Veen and Vreman et
al.55 and the biotransfer factors derived from these data.
J-24
-------
Table J-10. Mercury Concentrations and resulting BTFs in Lamb
Muscle Tissue Per Test Group and Dose55
Test Group
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor High-Level Sludge
Indoor Low-Level Sludge
Dose (mg
mercury/Kg dry
feed-day)
<0.02
0.08
<0.02
0.14
0.27
0.17
Feed Amount
(Kg DW/day)
1.36
1.36
1.3
1.28
1.39
1.38
mercury in
Muscle
(ug/Kg WW)
1
3
2
1
1
1
Muscle Dry
%
32.3
32.8
30.5
29.5
30.5
29.1
BTFA
(day/Kg DW)
0.2
0.08
0.3
0.02
0.009
0.02
A Biotransfer Factor (BTF)
To calculate the biotransfer factors listed from the data in
Table J-10, the daily mercury intake was calculated from the
mercury concentration in dry feed and daily intake of dry feed.
van der Veen and Vreman55 reported the dry weight fractions of
the muscle samples, and the mercury concentration in muscle was
calculated on a dry weight basis. The biotransfer factor for
each group of lambs was then determined. The average over all
groups was chosen as the default value, with the ranges taken
from Table J-10.
In U.S. EPA,56 uptake slopes were developed for a number of
pollutants found in sludge including mercury. For pork and
poultry, U.S. EPA56 reviewed the literature on concentrations of
metals in meat from studies in which livestock were fed known
concentrations of the metals in feed. These values were used to
obtain the default values (after converting wet-weight values to
dry-weight).
J.2.2
Parameter:
Fish Bioaccumulation Factor.
Tier 3 Fish BAF (BAF3)
Tier 4 Fish BAF (BAFJ
Definition:
Units:
The concentration of the methylmercury in fish
divided by the concentration of total dissolved
mercury in water
L/kg
J-25
-------
Fish Type
Trophic Level 3 Fish
Trophic Level 4 Fish
Default Value
(L/kg)
66,200
335,000
Percentiles (L/kg)
5th Percentile
6,400
22,700
Median
662,000
335,000
95th Percentile
684,000
4,700,000
Technical Basis:
The methylmercury value is most important since virtually
100 percent of mercury in fish tissue is methylmercury. The BAFs
for methylmercury is defined as the ratio of the methylmercury
concentration in fish flesh divided by the concentration of total
dissolved mercury (organic plus inorganic forms) in the water
column. As virtually 100 percent of the mercury in fish flesh is
in the methyl form, the definition of the BAF used here is
equivalent to the definition of a total mercury BAF as found in
the literature. The BAF represents the accumulation of mercury
in fish of a specific trophic level from both water intake and
predation on contaminated organisms, the latter being the
dominant pathway. In this report BAFs for methylmercury are
estimated for trophic level 3 (forage fish) and trophic level 4
(piscivorous fish) designated as BAF3 and BAF4, respectively.
The BAFs are intended to be representative of the random
selection of a fish from a random lake in a random geographical
location.
The BAFs were estimated by probabilistic Monte Carlo
simulation methods. Distributions were constructed from a
limited number of available studies for BAF3 and a predator-prey
factor for trophic level 4 (PPFJ . PPF4 represents the
bioaccumulation of mercury for piscivorous trophic level 4 fish
feeding on trophic level 3 fish. BAF4 is the product of BAF3 and
PPF4. Five studies were available for the estimation of BAF3
with values ranging from 10,000 to 350,000. PPF4 is based on 12
studies with values ranging from 1.2 to 15.5. A sensitivity
analysis shows that BAF3 has the greatest effect on the variance
of the BAF4 output, contributing 75 percent of the variance. A
major source of variability in the BAF estimates is the
dependence of PPF4 and to some extent, BAF3, on the age (and
consequently the size) of the fish. Because fish accumulate
mercury throughout their lives, the predator-prey and
bioaccumulation factors increase with age, particularly for
trophic level 4 fish. There is uncertainty as to whether a
single BAF value is appropriate for derivation of water
concentration when the fish-size range of the fish-consuming
J-26
-------
populations is known. For example, kingfishers feed on smaller
fish while human recreational anglers primarily consume large
fish. Because of the large variance in the BAF distributions
and the lack of distinction between uncertainty and variability,
the current recommendation is to apply BAFs derived from valid
data collected at the site of concern. Otherwise, it is
recommended that the mean values of the BAF distributions, rather
than upper or lower percentiles, be used for exposure assessment.
J.2.2.5
Parameter:
Definition:
Units:
Plant Surface Loss Coefficient.
kp
A measure of the loss of contaminants deposited on
plant surfaces over time as a result of
environmental processes.
/yr
Chemical
Hg°
Hg2+
Methylmercury
Default Value (per
year)
40.41
40.41
40.41
Distribution
Log(40.41,17.39)
Log(40.41,17.39)
Log(40.41,17.39)
Range
28.11 - 52.7
28.11-52.7
28.11-52.7
Technical Basis:
The values in the previous table were taken from Belcher and
Travis,2 although no speciation was provided. The values for all
species were assumed to be the same. The default value is the
mean of the lognormal distribution used in Belcher and Travis.2
The choice of a lognormal distribution was based on the work of
Miller and Hoffman.57
J.2.2.6
Parameter:
Definition:
Units:
Fraction of Wet Deposition Adhering.
Fw
Fraction of wet deposition that adheres to plant
(i.e., is not washed off).
unitless
(Default Value
0.6
Distribution
7(0.1,0.6,0.8)
Range I
0.1-0.8 |
J-27
-------
Technical Basis:
The unitless parameter Fw represents the fraction of the
pollutant in wet deposition that adheres to the plant, is not
washed off by precipitation and is used to estimate plant
pollutant levels. A value of 1 is the most conservative; this
implies that all of the pollutant which deposits onto the plant
via wet deposition will adhere to the plant. U.S. EPA8
originally used a value of 0.02, which significantly diminishes
the impact of this pathway. A more recent study by Hoffman et
al.58 suggests an answer between these extremes for both
dissolved pollutants and suspended particulates in simulated rain
drops.
Hoffman et al.58 attempted to quantify the amount of
radiolabeled beryllium (Be) and Iodine (I) as well as particles
of sizes 3, 9, and 25 urn that adhered to three plant types
(fescue, clover, and a typical weeded plot). The radiolabeled
pollutants were dissolved or suspended in water, which was then
showered upon the different types of vegetation to simulate
precipitation. Two precipitation intensities were modeled in the
experiment: moderate (1-4 cm/hour) and high (4-12 cm/hour). Due
to experimental complications, total deposition and pollutant
retention upon the vegetation were estimated by the authors;
these estimates were termed the interception fraction in the
Hoffman report. For example, in the experiment. Beryllium in
the form of BeCl2 was dissolved in the water and then showered
upon the vegetation. For the moderate and high intensity
precipitation events simulated, the mean interception fractions
were estimated to be 0.28 and 0.15, respectively.
The 1993 Addendum to the Indirection Exposure Methodology27
models deposition and retention as the product of the
interception fraction (Rp^) and Fw. In terms of the U.S. EPA
model, the Hoffman report estimates the product Rp^xFw. To
obtain estimates for Fw, the values reported in Hoffman et al.58
were divided by the interception fraction for forage used in this
assessment (0.471). This provides estimates of 0.60 and 0.32 for
Fw for the moderate and high precipitation intensities,
respectively (see Table J-ll).
Table J-ll shows the Hoffman et al.58 estimates for the
interception and adhesion of dissolved pollutants and suspended
particles in simulated moderate and high intensity precipitation.
Based on the Hoffman estimates and the assumption of an
interception fraction for forage of 0.47, the Fw for the two
J-28
-------
Table J-ll. Values From Hoffman et al.58 and the Values of Fw
Estimated Using Those Values
Compound
1
Beryllium
3 fjm
9 fjm
25 fjm
Rp, x Fw for
Moderate
Intensity
0.08
0.28
0.30
0.33
0.37
Rp, x Fw for
High Intensity
0.05
0.15
0.24
0.26
0.31
Fw Estimate for
Moderate
Intensity
0.17
0.60
0.64
0.70
0.79
Fw Estimate
for High
Intensity
0.11
0.32
0.51
0.55
0.66
Fw Mean
0.14
0.46
0.58
0.63
0.72
pollutants and three particle sizes were estimated for the
precipitation intensities studied, and the means were calculated.
No attempt has been made to adjust the final estimate for
frequency of the two precipitation intensities; however, since
moderate precipitation intensities are more common, the
unadjusted means are probably an underestimate.
The Fw estimated for beryllium was used as a surrogate for
mercury. Be2*, as a cation, is assumed to behave in a manner
similar to Hg2+ during deposition. Because the moderate
intensity is expected to be more common than the heavy intensity,
an Fw of 0.60 is assumed to be a reasonable estimate of Fw for
divalent mercury. This value is higher than the range of 0.1-0.3
presented in McKone and Ryan.10 For beryllium, Hoffman noted the
appearance of a strong attraction between the cation and the
plant surface, which was assumed to be negatively charged.
Beryllium is believed to adsorb to cation exchange sites in the
leaf cuticle. Once dried on the plant surface, beryllium was not
easily removed by subsequent precipitation events. Divalent
mercury is assumed to exhibit a similar behavior. The range of
0.1-0.8 was used to estimate the sensitivity of this parameter.
The adjusted Hoffman data indicate that the greater the
intensity of the precipitation, the smaller the Fw estimate for
both dissolved pollutants and suspended particles. This is
intuitively appealing given the understanding of the physical
process. Hoffman et al.58 noted that the intensity and amount of
rainfall had approximately the same impact on the estimated
values. It should also be noted that the data indicate that the
value of Fw for pollutants that deposit as anions (e.g., I) may
be significantly lower than cations.
J-29
-------
J.3 REFERENCES
1. Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. A
Review and Analysis of Parameters for Assessing Transport of
Environmentally Released Radionuclides Through Agriculture.
Prepared under contract No. DE-AC05-84OR21400. U.S.
Department of Energy, Washington, D.C. 1984.
2. Belcher, G.D. and C.C. Travis. Modeling Support for the
Rura and Municipal Waste Combustion Projects: Final Report
on Sensitivity and Uncertainty Analysis for the Terrestrial
Food Chain Model. Prepared for the U.S. EPA. 1989.
3. Shor, R.W. , C.F. Baes , and R.D. Sharp. Agricultural
Production in the United States by County: A Compilation of
Information from the 1974 Census of Agriculture for Use in
Terrestrial Food Chain Transport and Assessment Models. Oak
Ridge National Laboratory, ORNL-5786. 1982.
4. Hoffman, F.O. and D.F. Baes. A Statistical Analysis of
Selected Parameters for Predicting Food Chain Transport and
Internal Dose of Radionucleotides. ORNL/NUREG/TM-882 .
1979.
5. Knott, J.E. Handbook for Vegetable Growers. J. Wiley and
Sons, Inc., New York. 1957.
6. Rutledge, A.D. "Vegetable Garden Guide." Publication 447
(Revised) University of Tennessee Agricultural Extension
Service, The University of Tennessee. 1979.
7. South Coast Air Quality Management District (SCAQMD).
Multi-pathway Health Risk Assessment Input parameters.
Guidance Document. 1988.
8. U.S. EPA. Methodology for Assessing Health Risks Associated
with Indirect Exposure to Combustor Emissions. Office of
Health and Environmental Assessment, Washington, D.C.
EPA/600/6-90/003. 1990.
9. NAS (National Academy of Sciences). Predicting Feed Intake
of Food-Producing Animals. National Research Council,
Committee on Animal Nutrition, Washington, DC. 1987.
10. McKone, T.E. and P.B. Ryan. Human Exposure to Chemicals
Through Food Chains: An Uncertainty Analysis. Environ.
Sci. Technol. 23(9): 1154-1163. 1989.
J-30
-------
11. Fries, G.F. Potential Polychlorinated Biphenyl Residues in
Animal Products from Application of Contaminated Sewage
Sludge to Land. J. Environ. Quality. 11: 14-20. 1982.
12. Fries, G.F. Potential Polychlorinated Biphenyl Residues in
Animal Products from Application of Contaminated Sewage
Sludge to Land. J. Environ. Quality. 11: 14-20. 1982.
13. Hanford Environmental Dose Reconstruction Project.
Parameters Used in Environmental Pathways (DESCARTES) and
Radiological Dose (CIDER) Modules of the Hanford
Environmental Dose Reconstruction Integrated Codes (HEDRICF)
for the Air Pathway, PNWD-2023 HEDR, Pacific Northwest
Laboratories, September 1992.
14. Iverfeldt, A. and J. Persson. The solvation thermodynamics
of methylmercury (II) species derived from measurements of
the heat of solvation and the Henry's Law constant.
Inorganic Chimica Acta, 103: 113-119. 1985.
15. Lindqyist, 0., K. Johansson, M. Aastrup, A. Andersson, L.
Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M.
Meili, and B. Timm. Mercury in the Swedish Environment -
Recent Research on Causes, Consequences and Corrective
Methods. Water, Air and Soil Poll. 55:(all chapters). 1991.
16. Bloom, N.S., C.J. Watras, and J.P. Hurley Impact of
Acidification on the Methylmercury Cycle of Remote Seepage
Lakes. Water, Air, and Soil Poll. 56: 477-491. 1991.
17. Moore, J.W. and S. Ramamoorthy. Heavy Metal in Natural
Waters — Applied Monitoring in Impact Assessment. New York,
Springer-Verlag. 1984.
18. Glass, G.E., J.A. Sorenson, K.W. Schmidt, and G.R. Rapp.
New Source Identification of Mercury Contamination in the
Great Lakes. Environmental Science and Technology
24:1059-1069. 1990.
19. Robinson, K.G. and M.S. Shuman. Determination of mercury in
surface water using an optimized cold vapor
spectrophotometric technique. International Journal of
Environmental Chemistry 36:111-123. 1989.
20. Parks, J.W., A. Luitz, and J.A. Sutton. Water Column
Methylmercury in the Wabigoon/English River-lake System:
Factors Controlling Concentrations, Speciation, and Net
Production. Can. J. Fish. Aquat. Sci. 46: 2184-2202. 1989.
J-31
-------
21. Akagi H., B.C. Mortimer, and D.R. Miller. Mercury
Methylation and Partition in Aquatic Systems. Bull.
Environ. Contain. Toxicol. 23: 372-376. 1979.
22. Wilken, R.D. and H. Hintelmann. Mercury and Methylmercury
in Sediments and Suspended Particles from the River Elbe,
North Germany. Water, Air, and Soil Poll. 56: 427-437.
1991.
23. Hildebrand, S.G., R.H. Strand, and J.W. Huckabee. Mercury
accumulation in fish and invertebrates of the North Fork
Holston river, Virginia and Tennessee. J. Environ. Quan.
9:393-400. 1980.
24. Nriagu, J.O. The Biogeochemistry of Mercury in the
Environment. Elsevier/North Holland. Biomedical Press:
New York. 1979.
25. Cappon, C. Content and Chemical Form of mercury and
selenium in soil, sludge and fertilizer materials. Water,
Air, Soil Pollut 22:95-104. 1984.
26. Lindqvist, 0., K. Johansson, M. Aastrup, A. Andersson, L.
Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M.
Meili, and B.' Timm. Mercury in the Swedish Environment -
Recent Research on Causes, Consequences and Corrective
Methods. Water, Air and Soil Poll. 55:(all chapters).
1991.
27. U.S. EPA. Assessment of Mercury Occurence in Pristine
Freshwater Ecosystems, prepared by ABT Associates, Inc.
Draft as of September 1993.
28. Alberts, J.J., J.E. Schindler, and R.W. Miller. Elemental
Mercury Evolution Mediated by Humic Acid. Science 184: 895-
897. 1974.
29. Fitzgerald, W.F., R.P. Mason, and G.M. Vandal. Atmospheric
Cycling and Air-Water Exchange of Mercury over Mid-
Continental Lacustrine Regions. Water, Air, and Soil
Pollution 56: 745-767. 1991.
30. Watras, C.J. and N.S. Bloom. Mercury and Methylmercury in
Individual Zooplankton: Implications for Bioaccumulation.
Limnol. Oceanogr.31(6): 1313-1318. 1992.
J-32
-------
31. Bloom, N.S. and C.J. Watras. Observations of Methylmercury
in Precipitation. The Sci. Tot. Environ. 87/88: 199-207.
1989.
32.
33. Lee, Y., and H. Hultburg. Methylmercury in some Swedish
Surface Waters. Environ. Tocicol. Chem. 9: 833-841.
34. Kudoet al. (1982). Reference to be added.
35. Gill, G. And K. Bruland. Mercury Speciation in surface
freshwaters systems in California and other areas. Sci.
Total Environ. 24:1392. 1990.
36. Bloom, N. and S.W. Effler. Water, Air, and Soil Poll.
53:251-265. 1990.
37. Lee, Y., and H. Hultburg. Methylmercury in some Swedish
Surface Waters. Environ. Toxicol. Chem. 9: 833-841.
1990.
38. Hanson, P. J., S. E. Lindberg, K. H. Kim, J. G. Owens, and
T. A. Tabberer, Air/surface exchange of mercury vapor in
the forest canopy: I. Laboratory studies of foliar mercury
vapor exchange. International Conference on Mercury as a
Global Pollutant, July 10-14, Whistler, British Columbia,
Canada. 1994.
39. Mosbaek, H., J. C. Tjell, and T. Sevel. Plant Uptake of
Mercury in Background Areas. Chemosphere 17(6):1227-1236 .
1988.
40. Fortmann, L. C., D. D. Gay, and K. 0. Wirtz. Ethylmercury:
Formation in Plant Tissues and Relation to Methylmercury
Formation. U.S. EPA Ecological Research Series,
EPA-600/3-78-037. 1978.
41. Cappon, C.J. Mercury and Selenium Content and Chemical Form
in Vegetable Crops Grown on Sludge-Amended Soil. Arch.
Environm. Contain. Toxicol. 10: 673-689. 1981.
42. Cappon, C.J. Uptake and Speciation of Mercury and Selenium
in Vegetable CropsGrown on Compost-Treated Soil. Water,
Air, Soil Poll. 34: 353-361. 1987.
J-33
-------
43. Fortmann, L. C., D. D. Gay, and K. 0. Wirtz. Ethylmercury:
Formation in Plant Tissues and Relation to Methylmercury
Formation. U.S. EPA Ecological Research Series, EPA-600/3-
78-037. 1978.
44. Temple, P. J. and S. N. Linzon. Contamination of
Vegetation, Soil, Snow and Garden Crops by Atmospheric
Deposition of Mercury from a Chlor-Alkali Plant, in (1977)
D. D. Hemphill [ed] Trace Substances in Environmental
Health - XI, Univ Missouri, Columbia, p. 389-398. 1977.
45. Lenka, M. , K. K. Panda, and B. B. Panda. Monitoring and
Assessment of Mercury Pollution in the Vicinity of a
Chloralkali Plant. IV. Bioconcentration of Mercury in In
Situ Aquatic and Terrestrial Plants at Ganjam, India. Arch.
Environ. Contain. Toxicol. 22:195-202. 1992.
46. Lenka, M., K.K. Panda, and B.B. Panda. Monitoring and
Assessment of Mercury Pollution in the Vicinity of a
Chloralkali Plant. II. Plant Availability, Tissue
Concentration and Genotoxicity of Mercury from Agricultural
Soil Contaminated with Solid Waste Assessed in Barley.
Environ. Poll. 0269-7491/92. pp. 33-42. 1992.
47. Lenka, M., K.K. Panda, and B.B. Panda. Monitoring and
Assessment of Mercury Pollution in the Vicinity of a Chlor-
alkali Plant. IV. Bioconcentration of Mercury in In Situ
Aquatic and Terrestrial Plants at Ganjam, India. Arch.
Environ. Contain. Toxicol. 22:195-202. 1992.
48. Sumo, E., B.R. Singh, A.R. Selmer-Olsen, and K. Steenburg.
Uptake of 203Hg-labeled Mercury compounds by Wheat and Beans
Grown on an oxisol. Plant and Soil 85: 347-355. 1985.
49. John, M.K. Mercury Uptake from Soil by Various Plant
Species. Bull. Environ. Contain. Toxicol. 8(2): 77-80.
1972.
50. Wiersma, D. , B. J. van Goor, and N. G. van der Veen.
Cadmium, Lead, Mercury, and Arsenic Concentrations in Crops
and Corresponding Soils in the Netherlands. J. Agric. Food
Chem., 34:1067-1074. 1986.
51. U.S. EPA. Environmental profiles and hazard indices for
constituents of municipal sludge: Mercury. Washington,
D.C.: Office of Water Regulations and Standards. 1985.
J-34
-------
52. Fitzgerald, W. Cycling of mercury between the atmosphere
and oceans, in: The Role of Air-Sea Exchange in Geochemical
Cycling, NATO Advanced Science Institutes Series, P.
Buat-Menard (Ed.)/ D. Reidel publishers, Dordrecht, pp
363-408. 1986.
53. Lindberg, S. E., D. R. Jackson, J. W. Huckabee, S. A.
Janzen, M. J. Levin, and J. R. Lund. Atmospheric Emission
and Plant Uptake of Mercury from Agricultural Soils near the
Almaden Mercury Mine. J. Environ. Qual. 8(4):572-578. 1979.
54. Vreman, K., N.J. van der Veen, E.J. van der Molen and W.G.
de Ruig. Transfer of cadmium, lead, mercury and arsenic
from feed into milk and various tissues of dairy cows:
chemical and pathological data. Netherlands Journal of
Agricultural Science 34: 129-144. 1986.
55. van der Veen, N.G. and K. Vreman. Transfer of cadmium,
lead, mercury and arsenic from feed into various organs and
tissues of fattening lambs. Netherlands Journal of
Agricultural Science 34: 145-153. 1986.
56. U.S. EPA. Technical Support document or Land Application of
Sewage Sludge. Federal Register. 40 CFR Part 257 et al.
Standards for the Use or Disposal of Sewage sludge; Final
Rules. February 19, 1993.
57. Miller, C. And F. Hoffman. An examination of the
environmental half-time for radionuclides deposited on
vegetation. Health Physics 45:731. 1993.
58. Hoffman, F.O., K.M. Thiessen, M.L. Frank and E.G. Blaylock.
Quantification of the interception and initial retention of
radioactive contaminants deposited on pasture grass by
simulated rain. Atmospheric Environment, 26A (18): 3313-
3321. 1992.
J-35
-------
This page is intentionally blank.
-------
APPENDIX K - PARAMETER JUSTIFICATIONS SCENARIO-DEPENDENT
PARAMETERS
-------
["his page is intentionally blank.
-------
DISTRIBUTION NOTATION
A comprehensive uncertainty analysis was not conducted as
part of this study. Initially, preliminary parameter probability
distributions were developed. These are listed in Appendices J
and K. These parameter probability distributions were not
utilized to generate quantitative exposure estimates. They are
provided as a matter of interest for the reader.
Unless noted otherwise in the text, distribution notations
are presented as follows.
Distribution
Log (A,B)
Log* (A,B)
Norm (A,B)
U (A,B)
T (A,B,C)
Description
Lognormal distribution with mean A and standard deviation B
Lognormal distribution, but A and B are mean and standard deviation of
underlying normal distribution.
Normal distribution with mean A and standard deviation B
Uniform distribution over the range (A,B)
Triangular distribution over the range (A,C) with mode of B
K-l
-------
K. SCENARIO DEPENDENT PARAMETERS
This appendix describes the scenario dependent parameters
used in the exposure modeling for the Mercury Study Report to
Congress. Scenario dependent parameters are variables whose
values are dependent on a particular site and may differ among
various site-specific situations. For this assessment, three
settings are being evaluated: (1) rural, (2) lacustrine, and (3)
urban. The receptors differ for each of these scenarios, as do
the parameters. These scenario dependent parameters may be
either chemical independent or chemical dependent. The following
sections present the chemical independent and chemical dependent
parameters used in this assessment.
Chemical independent parameters are variables that remain
constant despite the specific contaminant being evaluated. The
chemical independent variables used in this assessment are
described in the following sections.
Site physical data include information such as the
environmental setting, vegetative cover, presence of surface
water or groundwater, area of source and meteorological and
climatological data. These parameters are described in the
following sections.
K.I Time of Concentration
Parameter: Tc
Definition: Number of years that the air concentration at the
above level persists; equal to the facility
lifetime for calculations from anthropogenic
sources
Units: yrs
1 Scenario
All
Default Vatue(s) (years)
30
Distribution
None
Technical Basis:
The time of concentration is the same as the assumed
facility lifetime. The generic value is 30 years. It is noted
that this assumption is made only for estimation of soil
concentrations. The water concentrations are calculated assuming
K-2
-------
steady-state has been attained, with the flux due to
runoff/erosion based on the 30-year soil concentrations.
K.2 Average Air Temperature
Parameter: Ta
Definition: Average air temperature of microscale area
Units: °C
Location
Eastern Location
Western Location
Default Value (Years value is based upon) (°C)
11.9 (25)
13.4(47)
Distribution
U (8,16)
U(9,17)
Technical Basis:
The values for local airports are reported in the section
"U.S. Local Climatological Data Summaries for 288 Primary
Stations throughout the U.S." on CDROM by WeatherDisc
Associates.1 The distributions are arbitrary to explore the
sensitivity of this parameter.
K.3 Watershed Area
Parameter:
Definition:
Units:
WAI
Area of contamination which drains into a water
body
Km2
Location
Eastern Location
Western Location
Default Value (Km2)
37.3
37.3
Technical Basis:
The values for the fish ingestion pathways are based on
hypothetical watershed/waterbody surface area ratio of 15 and a
lake diameter of 1.78 km. This parameter was used only to
calculate the erosion and runoff load to the water body.
K-3
-------
K.4 Average Annual Precipitation
Parameter: p
Definition: Average annual precipitation
Units: cm/yr
Location
Eastern Location
Western Location
Default Value (cm/yr)
102
21
Distribution
1(82,102,122)
1(1,21,41)
Technical Basis:
All values are for local airports as reported in the section
"U.S. Local Climatological Data Summaries for 288 Primary-
Stations throughout the U.S." on CDRom by WeatherDisc
Associates.1 These were considered the "best estimates" of a
triangular distribution, with a range of 20 in/yr above and below
the mode.
K.5 Average Annual Irrigation
Parameter: I
Definition:
Units:
Average annual irrigation of plants
cm/yr
Location
Eastern Location
Western Location
Default Value (cm/yr)
12.5
57.5
Distribution
U(0,25)
U(50,65)
Technical Basis:
The ranges were approximated from Figure 4.25 in Baes et
al.2 The tentative default values are the midpoint of this
range. It was assumed that both the farmer and home gardener
will irrigate the same amount if they are in the same area of the
country (i.e., irrigation rate does not depend on size of plot).
K-4
-------
K.6 Average Annual Runoff
Parameter: Ro
Definition: Average annual runoff
Units: cm/yr
1 Location
Eastern Location
Western Location
Default Value (cm/yr)
18
1
Distribution
U{9,27)
U(0,2)
Technical Basis:
The default values for the eastern location are from
Geraghty et al.3 The total runoff values given in that report
include groundwater recharge, direct runoff, and shallow
interflow. Following U.S. EPA4, this number was reduced by
one-half to represent surface runoff. Because of the difficulty
of hydrologic modeling in the western location, the PRZM-2 model5
was used to estimate the runoff for this area. The estimated
value was 1 cm/yr. The distributions are arbitrary to determine
the sensitivity of this parameter.
K.7 Average Annual Evapotranspiration
Parameter:
Definition:
Units:
Ev
Average annual loss of water due to evaporation
cm/yr
Location
Eastern Location
Western Location
Default Value (cm/yr)
65
13
Distribution j
U(60,70) I
U(8,18) ~|!
Technical Basis:
For the eastern location, the ranges are based on estimates
from isopleths given in Figure 4.24 in Baes et al.2 The values
presented there were estimated based on local data (average
temperature and precipitation) as well as the maximum possible
sunshine for the area. The default value is the midpoint of this
K-5
-------
range. For the western location, the model PRZM-2 was used to
estimate the values given previously.
K.8 Wind Speed
Parameter: W
Definition: Wind speed
Units: m/s
Location
Eastern Location
Western Location
Default Value (m/s)
4.3
4.0
Distribution
U(1,7)
U(1,7)
Technical Basis:
All values were collected for local airports and reported in
the section "U.S. Local Climatological Data Summaries for 288
Primary Stations throughout the U.S." on CDROM by WeatherDisc
Associates.1 The primary use of this parameter is for estimating
volatilization from soil and water bodies. The distributions are
arbitrary to explore the sensitivity of this parameter.
K.9 Soil Density
Parameter: BD
Definition: Soil density
Units: g/cm3
Location
All Sites
Default Value (g/cm3)
1.4
Distribution
Log(1.4,0.15)
Range I
0.93-1.84 |
Technical Basis:
The distribution is from Belcher and Travis6 and is based on
a probability plot using data from Hoffman and Baes.7 There is
little variation in the parameter, despite the fact that more
than 200 data points were used. The default value is the mean of
the distribution.
K.10 Mixing Depth in Watershed Area
K-6
-------
Parameter:
Definition:
Units:
Zd
The depth that contaminants are incorporated into
soil (no tillage)
cm
Location
All Sites
Default Value (cm)
1.0
Distribution
U(0.5,5)
Technical Basis:
The default value is based on U.S. EPA.8 The distribution
is arbitrary to determine the relative sensitivity of the
parameter.
K.ll Mixing Depth for Soil Tillage
Parameter: Ztill
Definition: The depth that contaminants are incorporated into
tilled soil
Units:
cm
1 Location
All Sites
Default Value (cm)
20
Distribution
U(10,30)
Technical Basis:
The default value is based on U.S. EPA.8 The distribution
is arbitrary to determine the sensitivity of this parameter.
K.12 Soil Volumetric Water Content
Parame ter: Theta,0
Definition: Amount of water that a given volume of soil can
hold
Units:
ml / cm3
K-7
-------
Location
Eastern Location
Western Location
Default Value (ml/cm3)
0.30
0.36
Distribution
11(0.1 5,0.42)
UI0.1 5,0.42)
Technical Basis:
Values for water content can range from 0.003 to 0.40 ml/cm3
depending on the type of soil.7 Table K-l demonstrates the
dependency of values on the hydrologic soil type. These values
were derived from the PATRIOT software system9, which can be
obtained from the Center for Exposure Assessment Modeling at the
U.S. Environmental Protection Agency, Athens, Georgia.
Representative soil types for both sites are shown in Table
K-2 and were determined from Carsel.5 The soil types were used
in conjunction with the previous table to determine the default
value for the soil water content, with the value for the western
location being the average of the values for types C and D. The
distribution for all sites is a uniform distribution over the
range over all soil types.
K.13 Soil Erosivity Factor
Parameter:
Definition:
Units:
R
Quantifies local rainfall's ability to cause
erosion
kg/km2-yr
Location
Eastern Location
Western Location
Default Value (kg/km2-yr)
200
53
Distribution
U(1 00,300)
U(30,75)
Technical Basis:
The ranges were determined based on an isopleth map for the
region in USDA.10 The upper and lower bounds were determined
from this map by finding extremes within a 300-mile radius.
K.14 Soil Erodability Factor
Parameter:
K
K-8
-------
Table K-l. Water Content Per Soil Type
Soil Type
A
B
C
D
Water Content
0.15
0.22
0.30
0.42
Table K-2. Representative Soil Types For Each Site
Location
Eastern Location
Western Location
Soil Type
C
C/D
Definition: Quantifies soil's susceptibility to erosion
Units: tons/acre
(Location
Eastern Location
Western Location
Default Value (tons/acre)
0.30
0.28
Distribution
11(0.12,0.48)
U(0.08,0.48)
Technical Basis:
Based on similar soil near the eastern location (loamy sand,
loam, and silt loam) and using Table A2-2 in U.S. EPA11, a range
of 0.12 to 0.48 was obtained. A similar analyses has not been
performed for the other sites, but the ranges listed in the
previous table are apparently the maximum range possible based on
Table A2-2 in U.S. EPA8; therefore, these ranges encompass all
likely values and can be used for sensitivity analyses. The
default values are the midpoint of these ranges.
K-9
-------
K.15 Topographic Factor
Parameter: LS
Definition:
Units:
Provides a measure of the length and steepness of
the land slope
unitless
Location
Eastern Location
Western Location
Default Value
2.5
0.4
Distribution
U(0.25,5)
11(0.1,1.2)
Technical Basis:
The length and steepness of the land slope substantially
affect the rate of soil erosion. Table A2-3 in U.S. EPA11
contains LS values for various slopes and slope lengths and was
used in conjunction with United States Geological Survey (USGS)
maps to obtain the ranges given in the previous table. A 1:24000
map was available for the humid/east/complex I site while only a
1:250000 USGS map was available for all other sites. The default
value was chosen as representative of the most common slope and
length in the area.
K.16 Cover Management Factor
Parameter:
Definition:
Units:
The ratio of soil loss from land cropped under
local conditions to the corresponding loss from
clean tilled fallow
unitless
Location
Eastern Location
Western Location
Default Value
0.006
0.1
Technical Basis:
The lower end of the range for areas having forests (0.001)
is the lower of two values suggested for woodlands in U.S. EPA.12
K-10
-------
For those areas lacking forests (i.e., western site), the value
of 0.1 given for grass in U.S. EPA4 was used.
For the watershed, it was decided to use a cover fraction
representative of undisturbed grass or forested areas, although
high-end values were used. It was noted that the cover fraction
can vary by several orders of magnitude, depending on the land
use type and soil type. Table K-3 shows estimates of cover
factor values for undisturbed forest land.13 Bassd on the above
values and the objectives of this exposure assessment, it was
decided that the high-end values (of those above) would be
appropriate; a nominal value of 0.006 (the midpoint of the high-
end range) was chosen.
B.17 Sediment Delivery Ratio to Water Body
Parameter: Sdel
Definition: Sediment delivery ratio to water body
Units: unitless
Location
Both Locations
Default Value
0.2
Distribution
U(0.14,0.23)
Technical Basis:
The sediment delivery ratio is the fraction of soil eroded
from the watershed that reaches the water body. It can be
calculated based on the watershed surface area using an approach
proposed by Vanoni14:
Sdel = a WA
-b
where WAL is watershed area in m2, b is an empirical slope
coefficient (-0.125) and a is an empirical intercept coefficient
that varies with watershed area. A graph of the sediment
delivery ratio as a function of watershed area is given in the
Water Quality Assessment Manual.15
K-ll
-------
Table K-3. Cover Factor Values of Undisturbed Forest Land (from
WQAM, 1985; original citation Wischmeier and Smith12)
Percent of Area Covered by Canopy
of Trees and Undergrowth
75-100
45-70
20-40
Percent of Area Covered by Duff
(litter) at least 5 cm deep
90-100
75-85
40-70
Cover Management Factor
Value
0.0001-0.001
0.002-0.004
0.003-0.009
K.18 Pollutant Enrichment Factor
Parameter:
Definition:
Uni ts:
EF
The pollutant enrichment factor accounts for the
fact that the lighter particles susceptible to
erosion tend to have a greater concentration of
pollutants attached per mass than what the average
soil concentration may suggest.
unitless
Location
Default Value
Distribution
Both Locations
U{1.5,2.6)
Technical Basis:
Enrichment refers to the fact that erosion favors the lighter
soil particles, which have higher surface area to volume ratios and
are higher in organic matter content. Concentrations of
hydrophobic pollutants would be expected to be higher in eroded
soil as compared to in-situ soil. While enrichment is best
ascertained with sampling or site-specific expertise, generally it
has been assigned values in the range of 1 to 5 for organic matter,
phosphorus, and other soil-bound constituents of concern.
Mullins et al.16 describe the following equation for calculating
enrichment ratio for storm events:
EF = 2 + 0.2 In (Xe/Aj
where Xe is the mass of soil eroded, in metric tons (1 metric ton
= 1000 kg) , and Aw is watershed area, in hectares (1 hectare =
10,000 m2) . Experience suggests that typical values range from 1.5
to 2.0, reflecting erosion events from 0.08 to 1.0 tonnes per
hectare. A very large erosion event of 20 tonnes per hectare would
K-12
-------
have a predicted enrichment ratio of 2.6.
assumed here is 2.
K.19 Water Body Surface Area
Parameter: Waw
Definition: Water body surface area
Uni ts: km2
The default value
Location
Both Locations
Default Value 1
2.49 |
Distribution
11(1.5,3)
Technical Basis:
For the purpose of this assessment, it was assumed that the
hypothetical water body has a diameter of 1.78 km, from which the
default surface area is calculated.
K.20 Water Body Volume
Parameter: Vw
Definition: Water body volume
Uni ts: m3
Location
Both Locations
Default Value 1
1.24x107 |
Distribution
Constant
Technical Basis:
For the purpose of this assessment, it was assumed that the
hypothetical water body has a diameter of 1.78 km and mean depth of
5 m. The corresponding volume assuming a disk of height 5 m and
radius 0.89 km is then given by 1.24xl07 m3 (using the formula
volume=n r2 h) .
K.21 Long-Term Dilution Flow
Parameter: Q
Definition: Long term dilution flow
K-13
-------
Units: rnVyr
Location
Eastern Location
Western Location
Default Value (m3/yr)
1.44x107
1.44x105
Technical Basis:
The long-term dilution flow can be estimated from Tables in
U.S. EPA.17 The values in in/yr are given in Table K-4. These
were multiplied by the watershed area of 3.3xl07 m2 to obtain the
default values.
K.22 Suspended Solids Deposition Rate
Parameter: Ssdep
Definition: Suspended solids deposition rate
Units: m/day
Scenario Default Value (m/day)
Both Locations 0.5
Technical Basis:
Stokes equation can be used to calculate the terminal velocity
of a sediment particle settling through the water column, as
described in Ambrose et al. (1988):
Vs = 8'64g (FEp - FEw) dp2
18 E6
where:
Vs is Stokes velocity for a particle with diameter dp and
density =FEp, m/day, g is acceleration of gravity =3D 981
cm/sec2, = E6 is absolute viscosity of water =3D 0.01 poise
(g/cm3-sec) at 20 =F8C, = FEp is density of the solid, g/cm3,
= FEw is density of water, 1.0 g/cm3, and dp is particle
diameter, mm.
K-14
-------
Table K-4. Long-Term Dilution Flow In In/Yr
Location
Eastern Location
Western Location
Value (in/yr)
15
0.15
Values of Vs for a range of particle sizes and densities are
provided in Table 3.1. Deposition velocities should be set at or
below the Stoke's velocity corresponding to the median suspended
particle size, keeping in mind that pollutants tend to sorb more to
the smaller silts and clays than to large silt and sand particles
= 20. The deposition velocity here represents net deposition over
time and so will be smaller for systems experiencing periodic scour
= 20. The value chosen here is an order of magnitude below the
Stoke's velocity calculated for medium silt particles.
K.23 Benthic Sediment Concentration
Parameter.-
Definition:
Units:
BS
Benthic sediment concentration
kg/L
1 Scenario
Both Locations
Default Value (kg/L) I
1 kg/L |
Technical Basis:
Benthic sediment concentration is related to the densities of
sediment particles, water, and the bulk sediment:
Cs =
FEp (FEb - FEw}
(FEp - FEw)
where
FEp is the particle density in g/cm3, FEw is the water
density in g/cm3, and =FEB is the sediment bulk density in
g/cm3.
K-15
-------
Typical particle densities in sediments range between 2.6 and 2.7
k/cm3, and at 20 degrees Celsius water density is close to 1.0
g/cm3. For these properties, a bulk density value of 1.6 g/cm3
corresponds to a sediment concentration of 1.0 g/cm3 (or kg/L) and
a porosity of 0.65, which represents consolidated benthic sediment.
An analysis of 1680 measured bulk densities in marine sediments
exhibited a range from 1.25 to 1.8 g/cm3 and an average particle
density of 2.7.18 Some waterbodies contain an upper unconsolidated
layer of sediment with bulk densities of 1.1 to 1.3, which
correspond to porosities of 0.94 to 0.82 and sediment
concentrations of 0.16 to 0.48 g/cm3. In this study, we represent
pollutant storage in consolidated beds.
K.24 Upper Benthic Sediment Depth
Parameter: Db
Definition: Benthic sediment concentration
Units: m
1 Scenario
Both Locations
Default Value (m)
0.02
Distribution 1
U(0.01,0.03) I
Technical Basis:
The total benthic sediment depth can vary from essentially
zero in rocky streams to hundreds of meters in oceans. In the lake
environments being modeled here, the total benthic sediment depth
usually exceeds a few centimeters. Here we are modeling only the
upper layer that is in partial contact with the water column
through physical mixing and bioturbation. Although bioturbation
can descend to tens or even hundreds of centimeters, only the top
few centimeters would be in significant contact with the water
column. Because this model assumes chemical equilibrium between
the upper sediment layer and the water column, a shallow depth of
2 cm was chosen.
K.25 Aquatic Plant Biomass
2 mg/L
Technical Basis:
Aquatic biomass can include phytoplankton and, in shallow
areas, benthic algae and rooted aquatic plants. Phytoplankton
biomass, as measured by chlorophyll a, can range from less than 1
E 6g/L in oligotrophic lakes to higher than 200 E 6g/L during
K-16
-------
blooms = in eutrophic lakes. Given a typical carbon to chlorophyll
ratio of 30 (Ambrose et al., 1988), and carbon to biomass ratio of
approximately 0.4 19, the range of aquatic phytoplankton biomass is
from 0.08 to 15 mg/L. A yearly average chlorophyll a value of 25
E6g/L gives an estimated biomass of about 2 mg/L, which was used in
this study.
K.26 Total Fish Biomass
Bioenergetics typically dictate that biomass declines each
trophic level by a factor of 10. Trophic level 3 fish supported by
2 mg/L aquatic biomass, then, would be supported at about 0.02
mg/L. Additionally, trophic level 4 fish and fish supported by
external energy sources (such as insects) can be present. A total
fish biomass is estimated to be 0.05 mg/L.
K-17
-------
K.27 References
1. WeatherDisc Associates. U.S. Local Climatological Data
Summaries for 288 Primary Stations throughout the U.S., on
CDROM. 1992.
2. Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. A
Review and Analysis of Parameters for Assessing Transport of
Environmentally Released Radionuclides through Agriculture.
Oak Ridge National Laboratory, ORNL-5786. 1984.
3. Geraghty, J. J., D. W. Miller, F. V. Der Leenden, and F. L.
Troise, Water Atlas of the United States. A Water
Information Center Publication, Port Washington, N.Y. 1973.
4. U.S. EPA. Indirect Exposure Assessment Working Group
Recommendations, DRAFT pending review. 1993 .
5. Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean, and P.
Jowise. User's Manual for the Pesticide Root Zone Model
(PRZM) Release 1. U.S. EPA, Athens, GA. EPA-600/3-84-109.
1984.
6. Belcher, G.D. and C.C. Travis. Modeling Support for the
Rural and Municipal Waste Combustion Projects: Final Report
on Sensitivity and Uncertainty Analysis for the Terrestrial
Food Chain Model. Prepared for the U.S. EPA. 1989.
7. Hoffman, F.O. and D.F. Baes. A Statistical Analysis of
Selected Parameters for Predicting Food Chain Transport and
Internal Dose of Radionucleotides. ORNL/NUREG/TM-882 .
1979.
8. U..S. EPA. Methodology for Estimating Health Risks from
Indirect Exposure to Combustor Emissions. Office of Health
and Environmental Assessment, Washington, D.C. EPA/600/6-
690/003. 1990.
9. Imhoff, J.C., P.R. Hummel, J.L. Kittle, and R.F. Carsel.
PATRIOT - A Methodology and Decision Support System for
Evaluating the Leaching Potential of Pesticides.
EPA/600/S-93/010. U.S. Environmental Protection Agency,
Athens, Georgia. 1994.
10. U.S. Department of Agriculture. Handbook No. 537:
Predicting Rainfall Erosion Losses. U.S. Government
Printing Office, Washington, D.C. 1978.
K-18
-------
11. U.S.. EPA. Development of Risk Assessment Methodology for
Land Application and Distribution and Marketing of Municipal
Sludge. Office of Health and Environmental Assessment,
Washington, D.C. EPA/600/6-89/001. 1989.
12. U.S. EPA. Superfund Exposure Assessment Manual. Office of
Remedial and Emergency Response, Washington, D.C.
EPA/540/1086/060. 1988.
13. Wischmeier, W. and D. Smith. Predicting Rainfall and
Erosion Losses: A Guide to Conservation Planning. U.S.
Department of Agriculture, Agriculture Handbook No. 537.
1978.
14. Vanoni, V.A., Sedimentation Engineering. American Society
of Civil Engineers, New York, NY. pp. 460-463. 1975.
15. Mills, W.B., et al. Water Quality Assessment: A Screening
Procedure for Toxic and Conventional Pollutants in Surface
and Ground Water. Part 1. EPA/600/6-85/002a. U.S.
Environmental Protection Agency, Athens, Georgia. pp.
177,178. 1985.
16. Mullins, J.A., R.F. Carsel, J.E. Scarbrough, and A.M. Ivery.
PRZM-2, A Model for Predicting Pesticide Fate in the Crop
Root and Unsaturated Soil Zones: Users Manual for Release
2.0. EPA/600/R-93/046. U.S. Environmental Protection
Agency, Athens, Georgia, p.6-22. 1993.
17. U.S. EPA. Water Quality Assessment: A Screening Procedure
for Toxic and Conventional Pollutants in Surface and Ground
Water (Part 1). Washington, D.C. EPA/600/6-85/002-A.
1985.
18. Richards, A.F., T.J. Hirst, and J.M. Parks. Bulk
Density-Water Content Relationship in Marine Silts and
Clays. Journal of Sedimentary Petrology, Vol.44, No.4, p
1004-1009. 1974.
19. Bowie, G.L. et al. Rates, Constants, and Kinetics
Formulations in Surface Water Quality Modeling (Second
Edition). EPA/600/3-85/040. U.S. EPA, Athens, GA. 1985.
K-19
-------
APPENDIX L - MERCURY PARTITION COEFFICIENT CALIBRATIONS
-------
This page is intentionally blank.
-------
L. 1 INTRODUCTION
For an assessment of mercury exposure, an accurate modelling
of watershed chemistry is critical. One of the important
parameters in this watershed chemistry model is the soil-water
partition coefficient (Kd) and the benthic sediment-water
partition coefficient (see Appendix M for a more complete
description of the model). The method by which literature values
were determined did not account completely for the watershed
transport of mercury. As a consequence, a calibration effort was
undertaken in which mercury watershed transport was assessed at
specific sites and modeled in Addendum to the Methodology for
Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions (IEM2). To estimate a general effective Kd
value for mercury, the model was calibrated at three sites. The
geometric mean of the generated estimates was selected as the
final value.
As noted by Dooley,1 there is a difference of about three
orders of magnitude between the reported Kd values for mercury in
soil-water systems and those in water-suspended solid systems.
Dooley indicated that this difference is not as large as other
values in the literature suggest. In this appendix evidence is
presented in support of this hypothesis by means of a series of
calibrations of a watershed model. The calibrated partition
coefficients are about one order of magnitude lower than the
reported partition coefficients in water suspended solid systems.
L.2 BACKGROUND
L.2.1 Parameters and Coefficients
The parameters used to address mobility properties are among
the most important in multimedia chemical fate and transport
modeling. In many models, it is assumed that the total chemical
mass is partitioned among several different compartments. A
common assumption is that partitioning is linear; that is, the
fraction in one compartment is directly proportional to the
fraction in another compartment.
For soil-water systems, the constant of proportionality is
called the partition or distribution coefficient and is usually
denoted by Kd, with units of (mg/kg) / (mg/L) or L/kg. The
partition coefficient is the ratio of the concentration sorbed
onto soil particles to that dissolved in soil water at
equilibrium; that is no net changes of amount of chemical in soil
and water components. The adsorptive properties of a chemical
can depend on a variety of environmental factors; e.g., pH of
soil, amount of organic matter in the soil or water, percent of
L-l
-------
sand, silt or clay in soil, other chemicals present and even the
magnitude of the chemical concentration in the water itself.
Because of the complicated nature of the sorptive process, it is
not surprising that reported values for the linear partition
coefficient can vary over many orders of magnitude for a given
chemical.
More complicated methods than linear partitioning exist for
addressing sorption. The nonlinear Freundlich equation is an
example of a more complex model in which the soil concentration
is assumed to be proportional to some power of the water
concentration. The particular power, usually denoted n and
called the Freundlich exponent, affords a wider range of data
fitting capabilities, but as with the simpler approach and as
noted by Buchter et al.2, it does not provide much information
about the actual processes involved.
For long-term (years) estimates, the simple linear approach
is perhaps most applicable. The equilibrium assumptions
necessary are more likely to be achieved over a long period of
time, and the variation that could be observed and expected for
short-term simulations are more likely to be adequately
characterized by a single representative value. It is more
appropriate to call such a partition coefficient an "effective"
partition coefficient to reflect its strong empirical nature.
It is this kind of estimate that is appropriate for the
methodology described in the Draft Addendum to the Methodology
for Assessing Health Risks from Combustor Emissions.3 The IEM2
model also requires partition coefficients for the suspended
sediment-water and benthic sediment-water. The soil-water Kd is
critical in determining the movement of mercury from land to
water bodies, while the other coefficients partition the mercury
once it arrives in the water body.
L.2.2 Mercury
Mercury (Hg) can exist in three oxidation states: Hg°
(metallic or elemental) , Hg22+ (mercurous) , and Hg2+ (mercuric) .
The properties and behavior of mercury depend strongly on the
oxidation state. Mercurous and mercuric mercury can form
numerous inorganic and organic compounds; however, mercurous
mercury is rarely stable under ordinary environmental conditions.
Most of the mercury encountered in all environmental media except
the atmosphere is in the form of inorganic mercuric salts and
organomercurics. Organomercurics are defined by the presence of
a covalent C-Hg bond. The compounds most likely to be found
under environmental conditions are these: the mercuric salts
HgCl2, Hg(OH)2 and HgS; the methylmercury (MHg) compounds CH3HgCl
L-2
-------
and CH3HgOH; and, in small fractions, other organomercurics
(e.g., dimethyImercury, phenylmercury and ethylmercury).
A number of methods can be used to determine mercury
concentrations in environmental media. The concentrations of
total mercury, elemental mercury, organic mercury compounds
(especially methyImercury) and information on various Hg2*
compounds can be measured, although speciation among Hg2+
compounds is not usually attempted. Recently, significant
improvements and standardizations in analytical methodologies
enable reliable data on the concentration of methyImercury,
elemental mercury and the Hg2+ fraction to be readily separated
from the total mercury in environmental media. It is possible to
further speciate the Hg2* fraction into reactive, non-reactive
and particle-bound compounds, but it is not generally possible to
determine which Hg2+ species is present (e.g., HgS or HgCl2) .
Most of the mercury in soil is thought to be in the form of
Hg2* species. Soil conditions are typically favorable for the
formation of inorganic Hg2* compounds such as HgCl2, Hg(OH)2 and
inorganic Hg2+ compounds complexed with organic anions).4
Although inorganic Hg2+ compounds are quite soluble and thus
theoretically mobile, they form complexes with soil organic
matter (mainly fulvic and humic acids) and mineral colloids, with
the former being the dominating process. This is due largely to
the affinity of Hg2+ and its inorganic compounds for sulfur
containing functional groups. This complexing behavior greatly
limits the mobility of mercury in soil. Much of the mercury in
soil is bound to bulk organic matter and is susceptible to
elution in runoff only by being attached to suspended soil or
humus. However, some Hg2* will be absorbed onto dissolvable
organic ligands and other forms of dissolved organic carbon (DOC)
,and may then partition to runoff in the dissolved phase. Hg° can
be formed in soil by reduction of Hg2* compounds/complexes
mediated by humic substances .5 This Hg° will eventually
vaporize and re-enter the atmosphere, methyImercury can be
formed by various microbial processes acting on Hg2" substances.
Generally, approximately 1 to 3 percent of the total mercury in
surface soil is methyImercury, and as is the case for Hg2+
species, it will be largely bound to organic matter. The other
97-99 percent of total soil mercury can be considered largely
Hg2* complexes, although a small fraction of mercury in typical
soil will be Hg0.6 The methyImercury percentage can exceed
3 percent7 in garden soil with high organic content under
slightly acidic conditions. Contaminated sediments may also
contain higher methylmercury percentages compared to soils.8-9
L-3
-------
Values for soil-water partition coefficients for mercury are
rarely reported in the literature, regardless of species.
Reported values for mercury range from 10 ml/g 10 to 408 ml/g 1X.
For a Freundlich model, the partition coefficients range from 19
to 299 ml/g, with the Freundlich exponent ranging from 0.5 to
2.2.2 Although there is considerable variability in these
results, they suggest that typical values in soil-water systems
are between 10 and 500 ml/g and are certainly less than 1000
ml/g. These values are based on laboratory experiments under
conditions typically not representative of ambient mercury
concentrations.
Values derived from measurement under real-world conditions
are naturally most appropriate. A determination of the
soil-water partition coefficient requires a measurement of
speciated soil mercury concentration and the speciated soil water
dissolved phase mercury concentration. Measurements of the
speciated soil concentrations are typically reported in the
literature, but speciated soil water dissolved phase mercury
concentration are considerably harder to find.
Data on the benthic sediment-water Kd that are based on
measurements under realistic conditions are scarce as well.
Wiener et al.12 studied mercury partitioning .at Little Rock Lake,
a clear water seepage lake in north-central Wisconsin. The
mercury concentrations in the surficial sediment ranged from
10 to about 170 ng/g (dry weight). Assuming that the reactive
mercury values reported represent dissolved Hg2*, the dissolved
water concentrations range from 0.29 to 0.59 ng/L. Using these
values results in a range for the benthic sediment partition
coefficients for this site from 16950 ml/g to 586200 ml/g. There
appears to be at least as much uncertainty in the benthic Kd as
the soil-water Kd.
In contrast, a number of values for the suspended-sediment
Kd have been determined. These are for the most part based on
measured data under realistic conditions, unlike the values for
the soil-water and benthic-sediment partition coefficients
(Appendix J) . These values range from 103 ml/g13 to 106 ml/g.14
Because of the need for realistic partition coefficients in
the exposure assessment, several calibrations were performed and
are described here. Studies were found that include data on the
movement and partitioning of mercury in and around watersheds.
The type of information available varies among these studies and
can include soil, sediment, surface water, soil water, and runoff
water mercury concentrations, as well as lake outflow, lake
inflow (runoff and erosion) and sedimentation rates for mercury.
L-4
-------
The main purposes of these calibrations are twofold: 1} to
determine values for the soil-water partition coefficients and
the benthic sediment-water partition coefficients that result in
mercury to water transport and partitioning behavior that are in
reasonable agreement with available mercury transport data; and
2) to confirm that the IEM2 model is capable of correctly
predicting the complex process of mercury movement and
partitioning in the soil and water environments with the use of
realistic parameters. This is one of the most critical aspects
of mercury behavior addressed in the exposure assessment.
The modelling results were not as sensitive to the
suspended-sediment partition coefficients as the benthic
sediment-water coefficients in predicting mercury behavior in the
lakes considered in the calibrations, due to the clarity of the
water bodies considered. There is also enough reliable
information on the suspended-sediment partition coefficients to
believe that the mid-point from measured values should reasonably
predict mercury partitioning in this study (see Appendix J).
Thus, the suspended-sediment partition coefficients were not used
in the calibration process.
It is stressed that these calibrations are intended to be
only semi-quantitative, with the degree of accuracy of the
calibration determined qualitatively; the calibrated results are
only to be "consistent with" available data. There are doubtless
other possible calibrations. This problem is an intractable
aspect of almost any calibration and is discussed in section L.4.
L.3 CALIBRATIONS
L.3.1 Description of Calibration Approach
For the model application described in chapter 7, the models
were run in a "forward" fashion: the input parameters were
specified, and the output values (e.g., media concentrations)
were obtained. In the calibration effort described here, this
process was reversed; the input parameters were modified so that
certain output values were within specific ranges. The values
for the partition coefficients that yielded acceptable output
values were then used as representative values in the main report
when the models were run in the "forward" fashion.
The watershed model used, IEM2, uses atmospheric chemical
loads and perform to mass balances on a watershed soil element
and a surface water element. This mass balance tracks all
mercury specified in the background soil concentrations and the
mercury deposition rates. The mass balances are performed for
total mercury, which is assumed to speciate into three
L-5
-------
components: Hg°, Hg2+, and methylmercury. The fraction of
mercury in each of these components is specified for the soil and
the surface water elements. Loadings and chemical properties are
given for the individual mercury components, and the overall
mercury transport and loss rates are calculated by the model.
IEM2 first performs a terrestrial mass balance to obtain mercury
concentrations in watershed soils. IEM2 next performs an aquatic
mass balance driven by direct atmospheric deposition along with
runoff and erosion fluxes (i.e., amount of the chemical
transported from soil element to surface water element per unit
time) from watershed soils. The water body output values of the
IEM2 model are calculated based on the assumption that
steady-state conditions (i.e., fluxes out of surface water
element are equal to fluxes into element so that concentrations
are independent of time) have been achieved.
There are two main assumptions made in these calibrations.
The first is that the measured surface water concentrations are
due to the estimated (or reported) fluxes to the water body.
Other processes of mercury influx and outflux not considered here
are assumed negligible. If these are significant, it could
significantly modify the necessary calibrated values. Second, it
is assumed that conditions are approximately at steady-state.
The calibrations were generally performed in three steps,
depending on the particular data available. First the soil-water
partition coefficients were adjusted until the soil-water
concentration was within the target soil-water concentration
range. Then the runoff/erosion parameters were adjusted until
the fluxes to the water body were within the target range of
values. Finally, the benthic sediment partition coefficients
were adjusted until the water concentrations and benthic sediment
concentrations were both within acceptable range (increasing the
benthic sediment partition coefficient reduces the water
concentration and increases the benthic sediment concentration) .
L.3.2 Parameters Constant for All Calibrations
Table L-l shows the values for parameters that were the same
for all calibrations.
L.3.3 Calibration Results
L.3.3.1 Swedish Composite Lake. In a series of papers,
Meili investigated the mercury cycle through Swedish boreal
forest watersheds and lakes.15-16'17 The data in these papers,
which consist of a combination of summary values for Swedish
lakes and predicted values, were used to construct a model
L-6
-------
Table L-l. Parameters Constant for All Calibrations
Chemical-Dependent Parameters
Molecular weight (g/mole)
Henry's Constant (atm-m3/mole)
Soil-water partition coefficient (ml/g)
Benthic-sediment partition coefficient
Suspended-sediment partition coefficient
Equilibrium fraction in soil
Equilibrium fraction in water
Constants
Ideal gas constant (m-atm/mole-K)
Air density (g/cm3)
Solids density (kg/L = g/cm3)
Drag coefficient
Von Karman's Coefficient
Dimensionless boundary thickness
Run Options
Water body type
Suspended solids options switch
Equilibrium speciation option
Hg°
201
7.3x1 0'3
130'
130'
r
0
0.02
Value
8.21x10'6
1.19x10'3
2.70
1.10x10'3
7.40x10'1
4.00
Value
1
0
1
Hg'+
201
7.3x1 0'10
Calibrated
Calibrated
9.50x1 O4"
0.98
0.83
Methylmercury
216
4.7x1 0'7
Calibrated
Calibrated
6.50x105b
0.02
0.15
Comment
Used for volatilization from soil and
surface water
Used for water body calculations
Used to estimate speciation in waterbody
and concentration in benthos
Used for water body volatilization
calculations
Used for water body volatilization
calculations
Used for water body volatilization
calculations
Comment
Stagnant ponds, lakes
Use given sediment deposition rate to
calculate suspended solids concentration
Species are tied together in equilibrium
Because it is assumed that the equilibrium fraction of elemental mercury in soil is 0 (see Appendix J),
the soil-water partition coefficient does not affect the calculations. Similarly, due to the low assumed
equilibrium fraction in surface water the other partition coefficients for elemental mercury does not
significantly affect calculations and so it is not varied from the value shown here.
The suspended sediment partition coefficients were not as influential on the results used in the
calibrations here, based on the initial sensitivity analyses. For this reason, they were assigned the
values given in Appendix J. These values are based on these studies: Moore and Ramamodoray,13
and Robinson and Shuman.18
L-7
-------
Swedish lake, from which many of the necessary IEM2 parameters
could be approximated. The output values in Meili15'17 that were
used as target values in the calibrations are shown in Table L-2.
Table L-3 shows the input values, excluding the partition
coefficients, used in the IEM2 model for the calibrations.
Because there is considerable uncertainty about the degree
of volatilization from the surface water body, two separate
calibrations were performed. In the first, volatilization from
the surface water body was considered as a loss process. In the
second, no volatilization was assumed. The latter is consistent
with assumptions in Meili et al.17 where volatilization was not
considered due to uncertainty. The calibration of the soil-water
partition coefficients is the same in both calibrations because
it is not affected byloss processes from the surface water body.
The first step was to calibrate the soil-water partition
coefficients so that the predicted total mercury soil-water
concentration was within range of the target values (3.75 to
5 ng/1) . Then the erosion parameters were modified so that the
fluxes to the water body from runoff and erosion agreed. In the
IEM2 model, the various erosion parameters (sediment delivery
ratio, pollutant enrichment factor, erosivity factor, erodability
factor, topographic factor, cover management factor) are
multiplied together to obtain an estimate of the annual amount of
soil erosion; thus, there are many different possible
combinations of various values for these parameters that can
yield the same general erosion rate. Finally, the benthic
sediment partition coefficients were modified so that the
predicted surface water and benthic sediment concentrations were
consistent with the values reported in Meili et al.16 The
results of both calibrations are shown in Table L-4.
Although there is general agreement with the target
outfluxes, the high benthic sediment concentration suggests that
the assumption of no significant volatilization from the surface
water may not be true, unless there are processes not addressed
here that serve to prevent the predicted high benthic sediment
concentrations from occurring.
In summary, using the available data on 88 lakes in Sweden,
the IEM2 watershed model was calibrated using these available
data. The calibrated values of the benthic-sediment partition
coefficients depend on the significance of the volatilization
pathway from the water body. The calibrated benthic-sediment
L-8
-------
Table L-2. Target Values for Swedish Lake Calibration
Output Parameter
Mercury Concentration in lake
(ng/L)
Runoff load of mercury to lake,
ug/m2/yr or g/yr"
Mercury Concentration in
Runoff (ng/L)
Outflow of mercury from lake,
ug/mVyr or g/yr"
Sedimentation of mercury in
lake, ug/m2/yr or g/yr*
Ratio of runoff load and direct
deposition
Surface sediment
concentration, ng/g
Mass balance of Loss Processes
Central Sweden
Southern Sweden
Value
2-3
4-8 Central Sweden,
6-1 1 Southern Sweden
3.7
2-5 Central Sweden,
3-7 Southern Sweden
7-20 Central Sweden, 10-30
Southern Sweden
0.6
1 50-460
% Sedimentation
80
79
Comment
For clearwater lake; Table I of
Meili19
Based on mercury/Carbon ratio
of 0.25 ug/g in runoff water
and organic carbon
concentration of 1 5 g/m3.20
For clearwater lakes Meili21
Range from Table 1 in Meili et
al.22 and p.441 for
characteristics of 25 study
lakes in 1 986
% Outflow
20
21
• Fluxes are given per unit lake surface area.23 Using the assumed surface area of 1 km2 for a typical lake
gives an estimate of the flux in grams per year.
partition coefficients were found to agree with the overall range
reported in Wiener et al..12
L.3.3.2 Fen at Tivedan National Park, Sweden. Aastrup et
al.26, describe mercury transport within a small segment
(6 percent) of a larger watershed area. The results presented in
this paper were used for calibrating the parameters involved in
estimating the runoff of mercury from the watershed.
The study area was described as a minicatchment watershed
consisting of a small forested catchment in the Tivedan National
L-9
-------
I
o
0
1
Parameter
—
CM
t~-
&
5
ec
c
CD
T5
al and Southern Swei
Average of values for Centr
ir>
CM
deposition rate (ug/m2/yr
|
o
CD
Assumed negligible
o
(^
CD
CO
CC
C
o
'+3
O)
'fc
.2
CO
CD
.O
.>
CD
CD
^
m
al. are presented on i
Fluxes estimated in Meili et
-
Concentration (years)
"o
O
p
S
!>•
CD
3
a
1
CD
'S
c
CO
CO
c
o
•o
CO
£
m
*
air temperature (C)
i
<
£
3
; average air temperat
Assumed to be the same as
•*
water temperature
o
2
CO
<
:hern Sweden21
3
O
to
•i
eg
•3
c
CD
U
"o
S
S
§
<
to
r^
>
annual precipitation (cm/
CD
2
CD
<
^
*^.
(D
^
$
(/3
=
»
f
1
OT
•o
c
CO
I
c
CD
water discharge for c
Annual area-specific runoff
o
*»•
annual runoff (cm/yr)
CD
S
CD
_
CD
2
CO
CO
_0
O)
c
1
Q)
I
o
CO
CO
_o
•c
CO
to
3
>.
~c
o
(A
CO
o
CD
1
.C
O)
3
O
CC
in
p^
?
E
,0
c
annual evapotranspiratio
CD
a
2
a
c
_o
ID
_N
O
O
>
w
,O
"S
CO
3
>•
C
O
m
CO
€
1
(A
CO
£
a>
3
O
CC
in
CO
'co
E
i
CD
&
1
i
o
E
CD
ion Parameters
CO
S
UJ
"5
(A
CO
Ic
; used to incorporate
2
u
CD
4^
C
CD
i
CO
c
o
I
U
-
0
u
1?
L^
f\
Practice Suppi
CD
3
CD
>
"5
u
J
CM
d
1
*•*
'c
_3
Sediment delivery ratio
co
3
CO
>
"5
u
'5.
>•
CM
CO
1
'c
_3
llutant enrichment factor
S.
—
o
o
CO
O
o
o
E
S
0)
5>
.S. values (values rar
Rough estimate based on U
in
rv
£
k.
1
Erosivity fi
CO
6
o
CO
CM
0
S
CD
?
CO
i
"5
>
aii
ij
CD
3
CO
>
"5
o
J
00
CM
d
I
1
Erodability factor (t
CO
£
CO
£
^
(A
CD
*•>
o
+*
CO
J=
1
in
>.
2
O)
fc»
m
%
£
1
i
CO
-C
ipper northeast, whici
3
|
,c
c
.0
CD
o
3.
CO
£
3
CO
ir>
CM
CO
CO
£
*-
"c
2
Topographic factor
CD
3
1C
>
IA
CO
o
u.
co
o
o
6
CO
CA
_CD
"E
^
over management factor
u
sd Parameters
.c
CO
0)
i
te
"co
S
"5
c
1
_CD
'o
C
CO
CD
*t
N
.*
03
£
Watershed E
CD
3
§
CD
."
O.
Tt
S
^J
S
>•
1
CD
•D
"5
CO
.^
CO
p>
8
•o
CD
Value suggested for non-till
*—
^j
1
Watershed soil mixing d
CO
3
CO
>
"5
u
'5.
>-
CO
d
"cA
1
'c
2
Soil moisture content
CO
1
S
1
o
*^
CD
J?
1
CO
•o
o
.c
_D)
1
•D
C
CO
•?
0)
c
Cfi
t^
"o
jn
'5
CO
S
!
CO
w
o
10
c
CO
-5
•o
c
CD
1
CO
= s
•= OJ
"S »
l«
|S
i °
go
b to
0^
CD =6
O) m
2 5
g U)
< .£
8
5
O)
^c
c
o
ckground soil concentrat
03
CD
L-10
-------
•o
I
•H
I
CO
o
H
I
o
o
j
5
w
•
ID
Q.
I
JO
CD
«D
CO
CO
a
T3
o
CD
Q
5
values"
CD
O
'5.
"5
c
CO
CD
O>
0
EC
in
d
rate (m/day)
c
_g
*^
"w
o
Q.
S
c
T3
CD
v>
to
0
"5
"5
o
'o.
"o
c
CD
CD
.C
O
O
c
1
c
o
1
o
y
o
u
c
'•5
o
en
o
'£
I
CD
to
"5
"5
u
'5.
H
CM
0
d
j=
a
o
•o
o
J"
CD
a
Q.
§
2
.0
"5
U
«
n
•D
c p
e CD
c E
o io
o t
•?j o
ca a
M Jr
:= f
**
to
"5
2
•
ibration Paramet
O
Z
§
in
CN
CM
o
0
in
CN
•?
1
^
o
CD
Hg2* and methylrm
o
en
i
1
»#-
O
y
l-Water partition
'5
z
00
d
CO
d
en
en
'c
_3
O
4-^
Topographic fac
.
Z
o
q
en
en
4-»
'E
_3
O
4-1
ant enrichment fac
J3
"5
Q.
z
o
d
0
d
en
CA
_£
'£
_3
g
*^
ediment delivery ra
z
in
CN
in
CN
b.
J
^
~
h»
Soil erosivity facto
Z
350000
in
tn
^
•a
CO
CN
a
i
*n
O
O
§
•S _
CO D9
ithic Sediment F
thylmercury (ml/
11
:put Parameters
3
O
o
o
r-
o
o
CD
CD
CO
O
CD
+J
to
ration
CD
U
O
U
al mercury Soil
r-
1 i
<§ H
^ C/5
• in
co
;
^
i
.0
to
k.
CD
U
C
o
O
£
<
al mercury Soil-
r-
C
CO CD
jl
O tyj
CO T-
co
U)
00
m
in
CO
ff/erosion (g/yr)
£
3
O
^
•D
O
^
2
IS
o
4-*
T3
CO
O
£
1-
L-ll
-------
8
1
2
>
let Calibration
S?
ee
•a
c a>
o E
»•? 3
atilization 1
Water Ass
•5 «
> u
•^ n
o T
Z 3
(A
(B
U
•c
V) "§
E |
o 5
•S in
c <
o >-
'i *
«
o
ameters
(0
£
|
o
z
CO
00
o
'55
E
UJ
sO
Z
00
CM
00
»*-
"5
c
cr
c?*
CO
CM
§
CO
q
CO
=
c
o
i
o
o
o
U
c
1
~5
O
h>
O
I
z
1-
V
2
CO
f
s
(A
-c
S
•o
o
•°
0)
a
E
o
utflows
o
o
2
M
!
}
z
0
o
?
o
CM
r^1
CO
.0
*4^
(0
.N
'^
CO
o
>
c
11
O OT
0 O
CM CO
rv o
If)
^
?
CM
CO
en
^*
CO
.g
ID
a>
c
^:
$
2-5 Central
3-7 Southern
O)
(O
3
ON
00
CM
CM
r™
co
o
c
ti
^
Q
i
6
in
O
CM
O
O
co
5
o>
M
o
JC
*•«
9
to
c
c
o
s
k.
oncen
o
L-12
-------
Park located in southern Sweden. The mercury budget was
estimated for a till formation on a slope, making up a
funnel-shaped minicatchment of 0.014 km2. 'This area drains into
a fen (low land covered wholly or partly with water unless
artificially drained). Elevation in the minicatchment ranges
from approximately. 1.5 m at the point farthest from the fen to
0 m at the fen itself. The catchment was divided into three
areas: an upper level with shallow soils, an intermediate area,
and a waterlogged area.
Mercury concentrations were reported for different soil
layers. It was noted that 41 percent of the total mercury
estimated (8.8 kg/km2) was found in the highest humic layer.
Table L-5 shows the total mercury found in each soil layer
analyzed.
Also reported were estimates of mercury concentrations in
soilwater and groundwater. There was a large amount of variation
in the measured values. The values included in Table L-6
generally have a standard deviation as large as the mean, even
for those with as many as 30 samples.
The soilwater mercury concentration calculated in the IEM2
model is assumed to be the dissolved mercury, and hence does not
consist of any particulate-bound mercury. For the purpose of the
calibration effort, it was decided that this calculated quantity
would be bounded above by the values for Hg-II (sum of reactive
and unreactive mercury) reported in Aastrup et al. and shown in
Table L-6. These values, estimated in accordance with the
standard Swedish sampling program (Chapter 2 in Lindquist et
al.29), are the sum of the dissolved Hg2+ plus some reactive
particle associations and some humic matter associations which
may fall under particulate mercury (Hg-IIa and Hg-IIb). The
difference between total mercury concentration (Hg-tot) and Hg-II
was assumed to be Hg2+ strongly bound to particulates (i.e., not
dissolved).
The fluxes out of the minicatchment area were estimated from
Figure 4 (page 165) in Aastrup et al.26 (as well as Figure 2 in
Johansson et al.30) . The flux to the fen from the top 20 cm of
soil is 2.6 g/km2/yr. Because the fluxes are normalized to the
minicatchment area, multiplying by the area (0.014 km2) yields a
total flux of 0.0364 g/yr. Similarly, for the top 8 cm (called
the mor layer, which consists of humic matter distinct from
mineral soil), the flux is 1.5 g/km2/yr, corresponding to a total
flux of 0.02 g/yr.
L-13
-------
Table L-5. Soil Layers and mercury Concentrations Reported in
Aastrup et al.10
Soil Layer
Mor
E-Horizon
upper B-horizon
lower B-horizon
C-horizon
Total
Assumed Thickness (cm)
8
6
6
NA
NA
Mercury Content
(ng/g)
250
27
58
23
6
NA
Total Mercury
(g)
50
9
34
23
8
124
Two separate calibrations were performed. In the first, the
top 20 cm was treated as a single layer, while in the second only
the mor layer was used. The parameters, their values, and the
rationale for their selection are shown in Table L-7. Table L-8
shows the results for the both calibrations.
Despite the low values used for the default soil erosion
parameters, the loss due to erosion becomes significant when the
effective partition coefficient is increased. Whether this was
actually true at the Tividen site is not certain. Nevertheless,
the calibration for the mor layer alone provides an upper bound
for the partition coefficient, as the predicted soil-water
concentration lies at the lower end of the observed range.
L.3.3.3 Composite Minnesota Lake. These calibrations are
based on the Minnesota lake characterization presented in
Sorensen et al.31 based on 80 lake watersheds. Mercury
concentrations in precipitation, lake water and sediment were
measured and analyzed along with watershed data for 80 lake
watersheds in the study region of northeastern Minnesota. The
summary values are shown in Table L-9. Median values were used
when possible because a number of large watersheds/ waterbodies
biased the mean values. The values used in the IEM2 model are
shown in Table L-10.
The median value for evaporation reported in Sorensen et
al.31 was 47.6; however, this results in a negative net leach
rate since leach rate is proportional to Precipitation +
Irrigation - Runoff - Evapotranspiration. For this reason, the
evapotranspiration was set so that leach rate is 0. This has
L-14
-------
4J
0
?
4J
CO
ff>
ti
•H
•0
O
V
to
C
0
•H
(D
I
U
i
0
4J
I
•H
O
W
o
H
I
_
O)
=
O)
c
i.
o>
_J
c
~
3
S
1
E
3
E
X
<8
S
E
'E
S
c
Q
•$
CO
c
E
E
3
'x
(D
S
E
3
'c
S
^
Q
•o
M
C
(0
s
E
3
._
a
E
g
c
S
S
•d
CO
i
o
1
•5
CO
Tt
CO
in
d
_
*-
^.
"~
CO
d
O5
d
r-
CO
T—
"*
CO
O5
CO
CO
"-
^
^
en
-
o
CO
o
CM
^
O
^
*-
in
*~
CM
d
CM
in
d
CO
^~
co
"*
•*
00
O5
*~
^
co
(O
CO
u
O
CSI
CO
00
co
O
CO
0
CO
•"
in
O5
d
(O
"*
^
*
<-
CM
in
(O
o
O)
CM
,_
in
r-
O
0
in
o
CM
00
CO
0
en
o
^
*~
CM
0
en
*t
co
CO
CM
en
CO
r*-
o
CO
CO
en
en
u
o
o
o
in
*t
r-
O
^
o
m
o
^
CO
d
in
o
05
o
m
CM
in
*-
CO
CO
(0
in
CO
Q g
«- u
E S
S §
o e
c *^
•So
a c
2 .2
> O
§ «
•o
C
11
I '
(0
L-15
-------
o
H
•8
*•*
§
£
u
CD
"5
S
CO
i
D
"co
03
Q.
3
Value assumed in Aas
o
CM
^^
•^
EM
"S
03
| Mercury Deposition Rat
,
CQ
CD
Q.
Q.
1
CD
C
O
N
O
o
o
**"
_o
'•i^
s
o
u
Weighted average soil
horizon soil layers.
LU
^
k.
O 03
*Q CO ^
C 03 Jp
!M
i- CD O
° S
LO
CM
-D 03
C >-
3 CD
2 =
CO O
JC CO
.O '~
1 Approximate Estimated
soil concentration (ng/g
of consideration
to
CN
"co
lo
a.
E
to
CO
c
*™
•g
1
•o
1
(t)
£
"o
£
1
£
CO
1 6 months, which wa
co
—
_>
c
.2
ro
CD
'55
§
"o
1
D.
03
CO
CN
CO
03
O.
CO
CO
c
1
g
03
co
f
03
O
II 1
^r
O g.
E c
® CQ "J5
.£ *.
^ r^
o r^
*• co
1^
00
LO
.X
"o
1
•o
03
4-*
CD
CO
LU
a
k_ ^^
M « 0-
c | S
.2 c E
*3 •- O
i. co **~
t£^
lit i
o — oo
ro "§ CM
a> 03
sli
•- 0 O
t CD a
° eo" S
1 = E
« 5 o
§ co i:
lit
.22°
.i 2 ®
Q. •§ ^
'o co <«
03 03
Q. ~ 2
5 C O ~
ol 5 §.
" 03 J -2
J 1 TB ~
* a 4- E
> 03 03 O
CQ
k.
03
a g
"ro
1 1
LU E
k.
w O
o •»-
E LO
*. °°
£ co
^.
CO
>
1 Evapotranspiration (cm/
ID
"co
CD
^
CD
03
a
03
4>*
co
'o
03
5
Estimate of average S
*
.M.
O
"o
Average Air Temperatui
Rough estimate
in
CO
"co
(Average Wind speed (rr
en
S
(/}
. «.
CO
I)
CD
5
CO
CO
O
0
CD
rameter
CO
Q.
CO
Ic
g
'•£5 w
CO JD
CD f;
« 1
|j
£ !
CD —
f S
tt
__
2
>•
'33
O
'6
CO
CQ
CO
3
co
co
2
u
CO
.g
03
'i
CD
ns
4=
^.
"co
CO
.2
£
03
CO
03
"5
*>
"co
o
Q.
"o
'5
o.
1
1 1
o
CO
d
Soil Moisture Content
CO
03
3
CD
>
CO
u
'5.
4-1
0
CD
CO
03
CD
CO
§
a:
LO
o
4-*
O
CO
•*-
'to
o
LU
f
o
CO
C3
CO
CM
0
CO
03
CO
CO
c
o
.5
CD
_CD
4V
CO
CD
£
03
1-
00
CM
6
*tn
U
CD
| Erodability factor (tons/
(D
£
CD
H-
o
0.
~z
CO
3
«
m
"co
^j
CO
T3
0
k.
"o
Q
Computed using meth
O)
T—
6
^5
CO
_CD
H Topographic factor (uni
03
"(0
1
»*-
"CD
u
'5.
co
d
CO
CO
£
'E
3
o
|| Cover management fad
L-16
-------
O
0
U
§
4J
M
id
o
4J
I
0
to
n
o
§
_o
^
>•
'6
CO
^
0
5
^^
r B Horizon
o
a
a
•a
c
(0
LU
0
T3
»
s
o
O
§
(D
^
~
.2
13
£
10
U
4J
CD
E?
(D
CD
To
ation Value
.B
(0
U
4J
CD
O>
CO
CD
"5
Parameter
Z
O
in
CM
Z
O
o
o
00
CO
3
c |j5
]c -^
o ®
U E
||
1+*
£ 5
'5 ^
0
in
CM
o
in
CM
in
CM
in
CM
C
g
E
c
CD
o
o
U
'6
W)
1
s>
5 _
CD -^
K S
^^
^
c
ID
0
CO
d
O)
d
-
(0
eg
CM
d
CM
in
d
o>
CM
tal Mercury Soil-Wate
ncentration (ng/L)
0 0
H 0
CM
O
d
CM
0
d
(O
CO
o
d
(O
CO
o
d
I
<•«••
c __
£ CO
11
£ 0
fe °
S c
« o
?'§"
gee
•o c
CO o
5|
(0 U
4-* 4^
O (0
H (J
z
I*.
r*.
Z
t'
CM
Erosion
#
|
CO
CM
z
cq
in
Runoff
£
L-17
-------
Table L-9. Parameter Values for 80 Minnesota Lakes Reported in
Sorensen et al.31
Parameter
Lake concentration (ng/L)
Total organic carbon as C in surface water
(mg/L)
Annual Direct deposition onto lake (ug/m2/yr)
Deposition immediate ug/m2 (calculated
deposition falling directly on lake surface plus
calculated runoff from immediate watershed
(assuming 100% mercury transport to lake)
Indirect/Direct Deposition to Water Body
Median surface sediment concentration (ng/g)
Lake surface area (Ha)
Immediate watershed area (Ha)
Site elevation (m)
Topographic high immediate (m)
Annual precipitation (m/yr)
Annual evaporation, land (m/yr)
Annual runoff (m/yr)
Lake renewal time (yr)
Total renewal time (yr)
Mean depth (m)
Lake volume (m3)
% Forest
% Water
Median
2.30
6.76
12.7
24.8
0.95 (calculated
using median
values, but not
necessarily the
median)
154
328
650
432
476
0.665
0.476
0.196
49.1
2.18
5.70
1.5x107
83
16.2
Range/Comment
0.90-7.00
3.53-14.3
10.4-15.4
14.8-58.4
This is a value calculated here by
assuming that the difference between
the "deposition immediate" and the
direct deposition onto the lake is the
flux from the immediate watershed
(which turns out to be 12.1 ug/m2/yr).
Dividing this by the direct deposition
gives this ratio.
34-753
24-89400
55-168000
388-587
378-664
0.560-0.762
0.446-0.506
0.103-0.315
5.85-202
0.01-45.4
1.08-29.0
4.35x1 05- 5.47x1 09
46.2-94.7
4.10-38.5
L-18
-------
a
a
a>
•H
u
o
u
I 0
J -rl
0 -rl
H a
Q Lj
* S
EH Pi
Comment
CD
>
CO
4-1
CD
ID
£
w
••
ID
4^
CD
C
in
c
£
o
C/)
"o
CO
J3
ID
1-
.c
Median of values reported
CN
--.
>•
N
P
;ury deposition rate (ug/i
CO
Assume it is negligible
o
.>•
2
CO
OC
g
(0
O)
t
(0
Q
m
)il concentri
Used only in calculating sc
"T?
j of Concentration (yean
w
Mean for Minnesota
in
•age air temperature (C)
CO
£
3
S
CO
a
E
CD
'm
•is average <
Assumed to be the same ;
in
rage water temperature
CD
m
75
4-*
c
in
c
£
o
CO
"o
CO
JD
co
Median of values reported
in
co
>•
E
•"•
•age annual precipitation
CD
S.
"co
®
c
CD
in
c
£
o
w
"o
CD
JD
Median of values reported
CO
o>
"^
1
"o
1
"Jo
ID
>
"o
Set to precipitation minus
O)
CO
1
c
g
To
•age annual evapotranspi
>
c
.2
ID
N
JS
1C
O
£
T3
CD
in
3
Rough estimate; this is on
in
00
"w
E
!
i
0
0!
CO
1
Erosion Parameters
"6
CO
.2
4-»
CD
«J
w
Q
Q.
O
U
O
4-»
•o
CD
in
3
cn
Cover management factor
tice Support Factor
§
a.
Typical value
CN
o
•jj
in
'c
g
*4^
CO
b.
CD
13
•o
CD
•5
CD
(/)
CD
"co
"5
u
'a
1-
CN
"in
in
0)
'c
3
itant enrichment factor (
j
I
8
m
o
4*
8
E
0
CD
CD
£
03
CD
3
1C
in
"io
C/J
Z)
Rough estimate based on
in
.>.
2
o
CO
>.
'»
s
UJ
m
6
00
CN
d
2
CD
0)
§
in
o>
"co
cn
D
CD
"co
U
'5.
1-
00
CN
d
"£
u
-2
o
4-*
1
CO
4-*
15
CD
•o
S
UJ
1
c
o
u
CD
"5
>
CO
T3
CO
U
4-<
1C
E
g
*4-"
to
4^
U
2
-»^
O
2
«
.£ "•
+* *(D
5s
d value so t
i Sorensen
gj .=
11
H
CO «-
•^. cn
= CD
||
CO CD
\\
in
CN
"in
in
^
'c
2
u
CO
a
CO
!
Forest value
o
6
~tn
in
_CD
.ti
c
sr management factor (u
o
o
ershed Parameters
1
rt
"(5
^M
-------
•a
I
o
H
H
•a
ommem
o
3
co
CD
O
2
. c
£•2
1 .1
CD CO
U O
<= 4-
3 c
2 £
5 3
O CD
£ **
"S
•2 CD
£ 2
3 CD
5 e
£ CD
§>
"^^
U CO
_ .*;
'> »
> 3
S5
CD 4J
CD CD
C >
'x °5)
CD .S
£ o.
c 2
— Q.
CO Q.
C CO
o ._
S »
"w C
tO Q)
>•§
CD .2
3 CD
CO 3
* CO
"5 *
•§ J: w
! 5^
to .Q "CD
,-
— *
c
^
•w
f
CD
•D
CD
'x
'E
'6
CO
T3
CD
CD
1
g
CO
43
4-*
"5
'"M
CO
£
^^
To
CO
CD
"co
U
'5.
H
co
d
«•»
CO
CO
CD
.t;
oisture content (un
E
'o
V)
•?
c*
0
CM
CM
O
*^
CN
E
o
*•
13
CD
CO
C
CO
CO
CD
3
CO
to
m
"5
CD
CO
CD
w
O
in
c
CD
t^
O
Q.
CD
CO
CD
3
CO
"o
CD
CO
2
CD
<
00
"CD
_c
c
o
'IS
round soil concentr
CD
U
CO
CD
body parameters
|
a
N
CO
CD
13
To
U
'a
*o
§
f
o
cc
in
d
"^
CO
5
ent deposition rate
E
1
V)
f*
CM
CO
0
To
To
u
'5.
4^
"o
CO
CD
E
CD
O
tr
^
"S
^•*
c
o
1
c sediment concen
I
CD
CO
(N
CD
To
To
u
'a
H
CN
O
d
1
£
•D
0
£
1
CD
a
a
n
*
CO
+J
CD
C
CD
CO
S
0
c/>
_c
_
—
_CD
o
CO
t-
•—
1
6
!
£
CO
CD
r~
00
CN
«*~
O
o
3
CO
c
CO
1
o
1
CO
m
00
CM
to
CM
I
body surface area
CD
1
f
m_*
CO
CD
ensen
o
c
^
_®
CO
^_
_c
•o
CD
4-*
O
a.
£
CD
To
CO
CD
^
b
r-
X
in
_ .
column volume (m
CD
1
**-
Q
—
^™
i
CO
^^
CO
ro
CD
CO
CN
.t
O
CD
*w
To
CD
1
ID
O
CO
^
CD
C
(0
CD
10
^~
"o .
CD Z~
El:
3 CO
o *-
> CD
ased on
orensen
CO (/)
b
x
00
00
co
_
w
™p
erm dilution flow (i
CD
j
3
O
U
O
o
4^
CO
N
_co
o
o
o
z
c
c
o
*
2
.D
o
CD
N
CD
CA
O
in
en
co
"8
'i
CD
L-20
-------
little practical affect on predicted values because the
background soil concentration is not subject to these loss
processes.
Although no values are reported for the soil-water
concentrations in Sorensen et al.31 the values reported in
Aastrup et al.26 for Swedish soil indicate that they may be
approximated by the surface water concentration. The target
value for this effort was the median of the surface water
concentrations reported in Sorensen et al.31 of 2.3 ng/1.
After the soilwater concentrations were calibrated, the
topographic factor, used in estimating soil erosion, was set so
that the indirect/direct ratio was consistent with that estimated
from Sorensen et al. .31
As in the calibrations performed for the composite Swedish
lake, two separate calibrations were performed, one with and one
without volatilization from the surface water body. The results
of the calibrations are shown in Table L-ll. These two
calibrations provide a range for the benthic sediment
concentrations.
Despite the uncertainties introduced by using a composite
lake, the results are in general agreement with previous
calibrations. As for the composite Swedish lake, the
benthic-sediment partition coefficients had to be substantially
increased if volatilization from the surface water body was not
considered as a loss process.
L.4 LIMITATIONS AND UNCERTAINTIES
The calibrated partition coefficients derived here are
intended to represent long-term retention properties of the
watershed systems for which they were derived. An obvious
limitation with any calibration effort is that there may be other
calibrations that also give the same qualitative agreement but
have significantly different values for the calibrated
parameters. The likelihood of such alternative calibrations is
increased when large data gaps exist. Because the calibrations
were performed in a sequence of steps, these possibilities are
discussed in turn for each step in the calibration process used
here.
The soil-to-water partition coefficient calibrations seem
the most defensible because there is relatively little involved
in calculating soilwater concentrations. Furthermore, only a
relatively simple argument is required in order to suggest that
L-21
-------
Table L-ll. Results for Composite Minnesota Lake Calibrations
Parameter
Effective Soil-Water Partition
Coefficient (ml/g) for Hg2* and
methylmercury
Effective Benthic Sediment Partition
Coefficient (ml/g) for Hg2+ and
Methylmercury
Total Mercury Soil Concentration
after one year (ng/g)
Total Mercury Soilwater
Concentration (ng/L)
Indirect/Direct Ratio
Total Load to Water body from
Catchment Region Considered (g/yr)
% Erosion
% Runoff
Total Water column concentration
(ng/L)
Steady-state outflows (g/yr)
Volatilization
Sedimentation
Dilution
Total Mercury Concentration in
Benthos (ng/g)
Volatilization
from Surface
Water Assumed
38200
100500
87.8
2.3
0.95
39.7
92.36
7.64
2.3
21.4(26%)
44.0 (54%)
15.7 (20%)
214
No Volatilization
from Surface
Water Assumed
38200
149600
87.8
2.3
0.95
39.7
92.36
7.64
2.3
0
65.4 (80%)
15.7 (20%)
318
Target Calibration
Value
NA
NA
87
2.30
0.95
NA
NA
NA
2.3
154 (Range 34-753)
the typically reported values for the mercury partition
coefficients in soil-water systems are questionable. In the IEM2
model, the total concentration of each mercury component in soil
is assumed to reach equilibrium between its particulate and
aqueous phases. The concentration of species i dissolved in pore
water is given by the following equation:
Sci BD
'ds,i
e
BD
(1)
L-22
-------
The concentration in particulate phase is defined in equation
(2).
Sc. Kd . BD
^ '2 (2)
+ Kd . BD
£ f J.
where:
SCi = total soil concentration of component "i" (ug/g)
Qs = volumetric soil water content (L^ter/^)
KdSii = soil/water partition coefficient for component "i"
(L/kg = ml/g)
BD = soil bulk density (g/cm3)
Cst,i = total soil concentration of component "i" (mg/L)
Cds i = concentration of "i" dissolved in pore water
Cps,i = concentration of "i" in particulate phase (mg/kg)
The total soil concentration in ug/g is given by this
equation.
(3)
The value for the partition coefficient to achieve a given soil-
water concentration is , thus,
SC. 6C
Kd-< = ^-^D (4)
If the mercury soil-water concentrations reported in Meili et
al.16 are accurate, then these values indicate that the mercury
in the soil-water represents only a small fraction of the total
mercury per volume. For example, if a typical total soil
concentration of 100 ng/g (0.10 ug/g) were completely dissolved
in a liter of water, and assuming a typical soil density of 1.4
g/cm3, the resulting water concentration would be 100000 ng/L
(100 ug/L). This is to be compared to the reported soil-water
concentrations in the range of 1-10 ng/L. Thus, at most about
0.01 percent can be dissolved to achieve the values observed, and
the rest must be bound to particulates,in the soil matrix. Even
assuming a volumetric soil water content of 1, using equation (4)
above the partition coefficient must be about 104 ml/g in order
to have a dissolved water concentration of 10 ng/L. Achieving
soil-water concentrations of 2 ng/L requires a partition
coefficient of slightly less than 5xl04 ml/g.
L-23
-------
Because adequate speciation estimates were not available,
there is uncertainty in the values for the partition coefficients
for methylmercury. For the purpose of this effort they were
assumed to be the same as for Hg2+. In sediment, values between
about 2 percent and 9 percent methylmercury have been reported36
for sand, silt/woodchips and woodchip sediments. Cappon37 found
that percent methylmercury for nonamended soils was about 2.6
percent (this is an upper bound on values from unpublished data
reported by several authors as cited in Water, Air and Soil
Pollution 199138) . If the speciation in soil-water is similar to
that sorbed onto soil particles, then the partition coefficients
for methylmercury would be similar to those for Hg2*. Although
there is considerable variability in the percent of total mercury
that is methylmercury in surface waters, the latest estimates
(Bloom et al.14 ; Watras and Bloom39 range from 5 to 25 percent
methylmercury. If the fraction in soil water is slightly larger
than that sorbed onto soil particles, as the data would indicate,
then the required calibrated partition coefficient for
methylmercury would be correspondingly lower than that for Hg2*
derived above. This is because the fraction dissolved for
methylmercury would be higher than that for Hg2+. However, for
the purpose of this calibration effort, it was felt that the data
were not adequate to justify separate calibrations for both Hg2+
and methylmercury. The result of this assumption is that the
amount of methylmercury in the flux to the water body from runoff
may be underestimated and amount in soil erosion overestimated.
Calculation of the flux to the water body boils down to
determining the a set of adequate erosion/runoff parameters. The
total load due to runoff and erosion, denoted here by LE/Rii
(g/yr), is given by equation (5).
ER C 10-3 (5)
where :
LE/R,I = load to water body from surface runoff and soil
erosion for component i (g/yr)
WAL = watershed surface area (m2)
R = average annual runoff (cm/yr)
cds,i = concentration of "i" dissolved in pore water
Xe = unit soil loss (kg/m2/yr)
SD = sediment delivery ratio (unitless)
ER = contaminant enrichment ratio (unitless)
Cps,i = concentration of "i" in particulate phase (mg/kg)
The unit soil loss rate is given by equation (6) .
L-24
-------
X. - R_ K LS C PS
where
R_ = soil erosivity factor (kg/km2/yr)
K = soil erodability factor (tons/acre)
LS = topographic factor (unitless)
C = cover management factor (unitless)
PS = support practice factor (unitless)
Values for the site-specific average annual runoff were
available for the sites considered. Values for the soil erosion
parameters (R_, K, LS, C, PS, SD, ER) were not available. This
was further complicated by the composite nature of the sites
considered for the calibration efforts. For this reason, the
erosion parameters were calibrated so that the ultimate total
fluxes to the water body were consistent with measured data.
The calibrated benthic sediment partition coefficients are
consistent with values reported elsewhere (e.g., Wiener et
al.12). Because there is apparently considerable uncertainty as
to the degree of volatilization from surface water bodies, two
different calibrations were performed, when possible. In the
first the volatilization was assumed to contribute to the total
loss rate from the surface water using a generic set of the
relevant parameters (e.g., equilibrium speciation of the mercury
species in water,wind speed). Another calibration was also
performed assuming that volatilization was negligible. The
differences in the calibrated partition coefficients are well
within the range of the usual variation of the partition
coefficients themselves. Calibration without volatilization
requires that sedimentation play a larger role as a loss process.
The higher benthic sediment partition coefficients needed to
achieve this effect, while resulting in high benthic sediment
concentrations, provide upper bounds on the partition
coefficients for the site under consideration.
L.5 CONCLUSIONS
Calibrations of the IEM2 model were performed using three
data sets, with the partition coefficients serving as the primary
calibration parameters. Due to uncertainty as to the exact
degree of volatilization from the surface water body, two
separate calibrations were performed with and without
volatilization. The calibrated partition coefficients are shown
in Table L-12.
L-25
-------
u
V
1-1
2
n
M
a
H
(0
M
i-l
c
CD
'in
O
CL
O
O
»
k.
CO
Q.
C c
CD m
0 •"=
||
.C
C
•£
o
CD
— 1
.C
en
CD
C/}
O
O
Q.
E
o
CJ
C TJ
.2 ®
"•*•• "O
~ "o
•^ _c
"5 o
> c
c
o
^n CD
N "D
•— 3
'•*"* "o
O ™~
CD
> CD
_eo c
a-jg
•2
'
•§ i
C >.
s <3
E =!
31
C -D
O CD
11
KJ •—
= u
'•^ V
"5 o
> c
c
o
N ^?
'^ *o
"o —
b
X
CO
in
O
x
00
CO
«»
o
X
CO
CO
in
^
x
in
CS
b
x
00
CO
b
X
CO
CN
b
^
co
CN
Soil-Water Partition
Coefficients for Hg2*
and Methylmercury
(ml/g)
fc
x
r>
in
in
O
x
in
^
vn
O
X
q
^
^
z
-*.
z
in
o
x
CN
b
^
in
"
Benthic Sediment
Partition Coefficients
for Hg2* and
Methylmercury
(ml/g)
L-26
-------
The significance of volatilization as a mercury loss
process from water bodies is currently unclear. The results
derived here show that the assumption that volatilization is
negligible, while it can be modelled by increasing the benthic
sediment partition coefficients, results in benthic sediment
concentrations that are near or above the upper end of the
measured values. This suggests that volatilization may in fact
be nonnegligible.
Despite acknowledged uncertainties and limitations, the
results derived here support the use of soil-to-water partition
coefficients that are higher than previously published values,
when the equilibrium assumption is considered appropriate. The
effective partition coefficients (soil and benthic) determined
from these calibrations are similar to values found for
suspended-sediment partition coefficients, for which much
reliable data are available. Additionally, it was confirmed that
the IEM2 model could predict mercury movement and partitioning in
the soil and water environments with the use of realistic
modeling parameters. Thus, we have established that applying
this part of the IEM2 model using partition coefficients
representative of those in Table L-12 can be expected to result
in reasonable predictions of mercury movement and behavior in and
out of watersheds.
That soil-water partition coefficients larger than
previously published values would be necessary is consistent with
the growing concern that watershed soils may be serving as a
significant repository for mercury. This repository can
potentially act as a source of mercury to water bodiesvlong after
enhanced mercury deposition has occurred.
L-27
-------
L.6 REFERENCES
1. Dooley, J. H. Natural Sources of Mercury in the Kirkwood-
Cohansey Aquifer System of the New Jersey Coastal Plain.
New Jersey Geological Survey, Report 27. 1992.
2. Buchter, B., B. Davidoff, M.C. Amacher, C. Hinz, I.K.
Iskandar, and H.M. Selim. Correlation of Freundlich Kd and
retention parameters with soils and elements. Soil Science
148:370-379. 1989.
3. U.S. EPA. External Review Draft addendum to Methodology for
Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions. EPA/600-93/003 November 1993.
4. Schuster, E. The Behavior of Mercury in the Soil with
Special Emphasis on Complexations and Adsorption Processes —
a Review of the Literature. Water Air Soil Pollution. Vol.
56: (667-680) 1991.
5. Nriagu, J.O. The Biogeochemistry of Mercury in the
Environment. Elsevier/North Holland. Biomedical Press:
New York. 1979.
6. Revis, N.W., T.R. Osborne, G. Holdsworth, and C. Hadden.
Mercury in Soil: A Method for Assessing Acceptable Limits.
Arch. Environ. Contain. Toxicol. 19:221-226. 1990.
7. Cappon, C.J. Uptake and Speciation of Mercury and Selenium
in Vegetable CropsGrown on Compost-Treated Soil. Water,
Air, Soil Poll. 34:353-361. 1987.
8. Wilken, R.D. and H. Hintelmann. Mercury and Methylmercury
in Sediments and Suspended particles from the River Elbe,
North Germany. Water, Air and Soil Poll. 56:427-437.
1991.
9. Parks, J.W., A. Lutz, and J.A. Sutton. Water Column
Methylmercury in the Wabigoon/ English River-Lake System:
Factors Controlling Concentrations, Speciation, and Net
Production. Can. J. Fisher. Aq. Sci. 46:2184-2202. 1989.
10. Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor. A
Review and Analysis of Parameters for Assessing Transport of
Environmentally Released Radionuclides Through Agriculture.
Prepared under contract No. DE-AC05-840R21400. U.S.
Department of Energy, Washington, D.C. 1984.
L-28
-------
11. Rai, D. and J.M. Zachara. Chemical attenuations,
coefficients and constants in leachate migration, v.l, A
Critical Review. EA-3356, v.l, Research Pro. 1984.
12. Wiener, J.G., W.F. Fitzgerald, C.J. Watras, and R.G. Rada.
Partitioning and Bioavailibility of Mercury in an
Experimentally Acidified Wisconsin Lake, Environmental
Toxicology and Chemistry, Vol.9, pp. 909-918. 1990.
13. Moore, J.W. and S. Ramomodoray. Heavy Metal in Natural
Waters - Applied Monitoring in Impact Assessment. New York,
Springer-Varlag. 1984.
14. Bloom, N.S., C.J. Watras, and J.P. Hurley. Impact of
Acidification on the Methylmercury Cycle of Remote Seepage
Lakes. Water, Air, and Soil Poll. 56:477-491. 1991.
15. Meili, M. The Coupling of Mercury and Organic Matter in the
Biogeochemical Cycle - Towards a Mechanistic Model for the
Boreal Forest Zone, Water, Air, and Soil Pollution 56:333-
347. 1991.
16. Meili, M., A. Iverfeldt and L. Hakanson. Mercury in the
Surface Water of Swedish Forest Lakes - Concentrations,
Speciation and Controlling Factors, Water, Air, azzd Soil
Pollution 56:439-453. 1991.
17. Meili, M. Fluxes, Pools, and Turnover of Mercury in Swedish
Forest Lakes, Water, Air, and Soil Pollution 56:719-717.
1991.
18. Robinson, K.G. and M.S. Shuman. Determination of mercury in
surface waters using an optimized cold vapor
spectrophotometric technique. International Journal of
Environmental Chemistry 36:111-123. 1989.
19. Ref. 17, p. 723.
20. Ref. 17, Table I.
21. Ref. 17, p. 724
22. Ref. 16, Table I and p. 441.
23. Ref. 17, p. 722.
24. Ref. 6, p. 337.
L-29
-------
25. Jensen, A. and Jensen, A. Historical deposition rates of
mercury in Scandavia estimated by dating and measurement of
mercury in cores of peat bogs, IVater, Air, and Soil
Pollution 56:759-777.
26. Aastrup, M., J. Johnson, E. Bringmark, I. Bringmark, and A.
Iverfeldt. Occurrence and Transport of Mercury within a
Small Catchment Area, Water, Air, and Soil Pollution 56:
155-167. 1991.
27. U.S. EPA. Personal communication with R.A. Ambrose, U.S.
EPA, Athens, GA. 1994.
28. Ref. 17, Table II, p. 723.
29. Lindqvist, 0., K. Johansson, M. Aastrup, A. Andersson, L.
Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M.
Meili, and B. Timm. Mercury in the Swedish Environment -
Recent Research on Causes, Consequences and Corrective
Methods. Water, Air and Soil Poll. 55:(all chapters). 1991.
30. Johansson, K;, M. Aastrup, A. Andersson, L. Bringmark, and
A. Iverfeldt. Mercury in Swedish Forest Soils and Waters -
Assessment of Critical Load, Water, Air, and Soil Pollution
56:267-281. 1991.
31. Sorensen, J., G.E. Glass, K.W. Schmidt, J.K Huber, and G.R.
Rapp, Jr. Airborne Mercury Deposition and Watershed
Characteristics in Relation to Mercury Concentrations in
Water, Sediment, Plankton, and Fish of Eighty Northern
Minnesota Lakes, Environ. Sci. Technol. 24:1716-1727. 1990.
32. Ref. 26, Figure 3, p.163.
33. Arnold, J.G., J. Williams, A. Nicks, and N. Sammons. SWRBB:
A Basin-Scale Simulation Model for Soil and Water Resources
Management, Texas A & M University Press, College Station,
Texas. 1990.
34. Ref. 31, Table III.
35. Ref. 31, p.1724.
36. Akagi H., B.C. Mortimer, and D.R. Miller. Mercury
Methylation and Partition in Aquatic Systems. Bull.
Environ. Contam. Toxicol. 23:372-376. 1979.
L-30
-------
37. Cappon, C. Content and Chemical Form of mercury and
selenium in soil, sludge and fertilizer materials. Water,
Air, Soil Pollut 22:95-104. 1984.
38. Water, Air and Soil Pollution, 56, 1991.
39. Watras, C.J. and N.S. Bloom. Mercury and Methylmercury in
Individual Zooplankton: Implications for Bioaccumulation.
Lixnnol. Oceanogr. 37 (6) : 1313-1318 . 1992.
L-31
-------
This page is intentionally blank.
-------
Appendix M: Description of Exposure Models
-------
This page is intentionally blank.
-------
M.I DESCRIPTION OF RELMAP MERCURY MODELING
M.I.I History and Background Information
During the mid-1970's, SRI International developed a
Lagrangian puff air pollution model called the European Regional
Model of Air Pollution (EURMAP) for the Federal Environment
Office of the Federal Republic of Germany.1 This regional model
simulated monthly sulfur dioxide (S02) and sulfate (SO2')
concentrations, wet and dry deposition patterns, and generated
matrices of international exchanges of sulfur for 13 countries of
western and central Europe. In the late-1970's, the U.S. EPA
sponsored SRI International to adapt and apply EURMAP to eastern
North America. The adapted version of this model, called Eastern
North American Model of Air Pollution (ENAMAP), also calculated
monthly S02 and SO^" concentrations, wet and dry deposition
patterns, and generated matrices of interregional exchanges of
sulfur for a user-defined configuration of regions.2'3 In the
early-1980's, U.S. EPA modified and improved the ENAMAP model to
increase its flexibility and scientific credibility.
By 1985, simple parameterizations of processes involving
fine (diameters < 2.5 um) and coarse (2.5 urn < diameters < 10.0
um) particulate matter were incorporated into the model. This
version of the model, renamed the Regional Lagrangian Model of
Air Pollution (RELMAP), is capable of simulating concentrations
and wet and dry deposition patterns of S02, SO^" and fine and
coarse particulate matter and can also generate source-receptor
matrices for user defined regions. In addition to the main model
program, the complete RELMAP modeling system includes 19
preprocessing programs that prepare gridded meteorological and
emissions data for use in the main program. The RELMAP code was
developed using FORTRAN on a Sperry-UNIVAC 1100/82 computing
system. It has since been migrated and adapted to operate on
other computing systems. Currently, the RELMAP is operated by
U.S. EPA's Atmospheric Research and Exposure Assessment
Laboratory on DEC VAX and CRAY computing systems, and a test
version has recently been installed on a DEC 3000 AXP (Alpha)
workstation. The simulations for the Mercury Study Report to
Congress were performed on a CRAY Y-MP supercomputer at the
National Environmental Supercomputing Center. A complete
scientific specification of the RELMAP as used at U.S. EPA for
atmospheric sulfur modeling is provided in RELMAP: A Regional
Lagrangian Model of Air Pollution - User's Guide.* Section
M.I.2.1 discusses the modifications made to the original sulfur
version of RELMAP to enable the simulation of atmospheric
mercury.
M-l
-------
M.I.2 RELMAP Modeling Strategy for Atmospheric Mercury
M.I.2.1 Introduction. Previous versions of RELMAP have
been described by Eder et al.4 and Clark et al.5 The goal of the
current effort was to model the emission, transport, and fate of
airborne mercury over the continental U.S. for the year of 1989.
Modifications to the RELMAP for atmospheric mercury simulation
were heavily based on recent Lagrangian model developments in
Europe.6 The mercury version of RELMAP was developed to handle
three species of mercury: elemental (Hg°) , divalent (the mercuric
ion, Hg2+) and particulate mercury (Hgpart) , and also carbon soot.
Recent experimental work indicates that ozone7 and carbon
soot8'9'10 are both important in determining the wet deposition of
Hg°. Carbon soot, or total carbon aerosol, was included as a
modeled pollutant in the mercury version of RELMAP to provide
necessary information for the Hg° wet deposition
parameterization. Observed 03 air concentration data were
obtained from the Agency's Aerometric Information Retrieval
System (AIRS) data base, and it was not necessary to include O3
as an explicitly modeled pollutant. Observed O3 air
concentration data were objectively interpolated in time and
space for each 3-hour timestep of the model simulation to produce
grids of 03 air concentration. A minimum 03 air concentration
value of 20 ppb was imposed. Methylmercury was not included in
the mercury version of RELMAP because it is not yet known if it
has a primary natural or anthropogenic source, or if it is
produced in the atmosphere.
RELMAP may be run in either of two modes. In the field
mode, wet deposition, dry deposition, and air concentrations are
computed at user-defined time intervals. In the source-receptor
mode, RELMAP also computes the contribution of each source cell
to the deposition and concentration at each receptor cell. For
mercury, only the field mode of RELMAP operation was used. With
over 10,000 model cells in the high-resolution receptor grid and
a significant fraction of these cells also emitting mercury, the
data accounting task of a source-receptor run for all mercury
sources could not be performed with the computing resources and
time available.
Unless specified otherwise in the following sections, the
modeling concepts and parameterizations described by Eder et al.4
were preserved for the RELMAP mercury modeling study.
M.I.2.2 Physical Model Structure. Because of the long
atmospheric residence time of mercury, long range transport of
the majority of mercury emitted was expected. RELMAP simulations
were originally limited to the area bounded by 25 and 55 degrees
M-2
-------
north latitude and 60 and 105 degrees west longitude and had a
minimum spatial resolution of 1 degree in both latitude and
longitude. For this study, the western limit of the RELMAP
modeling domain was moved out to 130 degrees west longitude, and
the modeling grid resolution was reduced to ^ degree longitude by
Va degree latitude . (approximately 40 km square) to provide high-
resolution coverage over the entire continental U.S.
Since the descriptive document by Eder et al.4 was produced,
the original 3-layer puff structure of the RELMAP has been
replaced by a 4-layer structure. The following model layer
definitions were used for the RELMAP mercury simulations:
Layer 1 top - 30 to 50 meters above the surface
(season-dependent)
Layer 2 top - 200 meters above the surface
Layer 3 top - 700 meters above the surface
Layer 4 top - 700 to 1500 meters above the surface
(month-dependent)
M.I.2.3 Mercury Emissions. Point source emissions were
introduced into model layer 2 to account for the effective stack
height of the point source type in question. Effective stack
height is the actual stack height plus the estimated plume rise.
The layer of emission is inconsequential during the daytime when
complete vertical mixing is imposed throughout the 4 layers. At
night, since there is no vertical mixing, area source emissions
to layer 1 are subject to dry deposition while point source
emissions to layer 2 are not. Large industrial emission sources
and sources with very hot stack emissions tend to have a larger
plume rise, and their effective stack heights might actually be
larger than the top of layer 2. Since, however the layers of the
pollutant puffs remain vertically aligned during advection, the
only significant process effected by the layer of emission is
nighttime dry deposition.
For the RELMAP mercury modeling study, the point sources
were assigned particular mercury speciation profiles. These
speciation profiles defined the estimated fraction of mercury
emitted as Hg°, Hg2+, or Hgpart. Since there remains considerable
uncertainty as to the actual speciation factors for each point
source type, an alternate emission speciation was simulated in
addition to the base speciation in order to test the sensitivity
of the RELMAP results to the speciation profiles used. The base-
case and alternate speciation profiles used for this study are
shown in Table M-l. Gridded fields of total Hg°, Hg2+ and Hgpart
point source emission rates and Hg° area source emission rates
were produced and used as input to the RELMAP model simulation.
M-3
-------
Table M-l. Emission Speciation Profiles for the Point Source
Types Defined
Point Source Type
Electric Utility Boilers
Base-Case Speciation (%)
Hg°«
50
Hg2+b
30
Hgpc
20
Alternate Speciation (%)
Hg°«
50
Hg2+b
0
HgPc
50
• Hg° symbolizes elemental mercury
" Hg2+ symbolizes divalent mercury
c Hgp symbolizes particle bound mercury
Global-scale natural and recycled anthropogenic emissions
were accounted for by assuming an ambient atmospheric
concentration of Hg° gas of 1.6 ng/m3. This use of a constant
background concentration to account for global-scale natural and
anthropogenic emissions is the same technique used by Petersen et
al.6 The deposition parameterizations described in section
M.I.3.1 were used to simulate the scavenging of Hg° from this
constant ambient concentration throughout the entire
3-dimensional model domain. The result was used as an estimate
of the deposition of mercury from all natural sources and
anthropogenic sources outside the model domain.
M.I.2.4 Carbon Aerosol Emissions. Penner et al.
11
concluded that total carbon air concentrations are highly
correlated with sulfur dioxide (S02) air concentrations from
minor sources. They concluded that the emissions of total carbon
and S02 from minor point sources are correlated as well, since
both pollutants result from the combustion of fossil fuel. Their
data indicate a 35 percent proportionality constant for total
carbon air concentrations versus S02 air concentrations. The
RELMAP mercury model estimated total carbon aerosol emissions
using this 35 percent proportionality constant and S02 emissions
data for minor sources obtained by the National Acidic
Precipitation Assessment Program (NAPAP) for the year 1988. Much
of these S02 emissions data had been previously analyzed for use
by the Regional Acid Deposition Model (RADM). For the portion of
the RELMAP mercury model domain not covered by the RADM domain,
state by state totals of S02 emissions .were apportioned to the
county level on the basis of weekday vehicle-miles-traveled data
since recent air measurement studies have indicated that aerosol
elemental carbon can be attributed mainly to transportation
source types.12 The county level data were then apportioned by
M-4
-------
area to the individual RELMAP grid cells. Total carbon soot was
assumed to be emitted into the lowest layer of the model.
M.I.2.5 Ozone Concentration. Ozone concentration data were
obtained from U.S. EPA's Aerometric Information Retrieval System
(AIRS) and the Acidmodes experimental air sampling network. AIRS
and Acidmodes data were available hourly. Any observations of
ozone concentration below 20 ppb were treated as missing. For
each RELMAP grid cell, the ozone concentrations were computed for
the two mid-day time steps by using the mean concentration value
during two corresponding time periods (1000-1300 and 1400-1600
local time). The mean of these two mid-day values was used to
estimate the ozone concentration for the time steps after 1600
local time and before 1000 local time the next morning. This
previous-day average was used at night since ground-level ozone
data are not valid for the levels aloft, where the wet removal of
elemental mercury was assumed to be occurring. Finally, an
objective interpolation scheme was used to produce complete ozone
concentration grids for each time step, with a minimum value of
20 ppb imposed.
M.I.2.6 Lagrangian Transport and Deposition. In the model,
each pollutant puff begins with an initial mass equal to the
total emission rate of all sources in the source cell multiplied
by the model time-step length. For mercury, as for most other
pollutants, emission rates for each source cell were defined from
input data, and a time step of three hours was used. The initial
horizontal area of each puff was set to 1200 km2, instead of the
standard initial size of 2500 km2, in order to accommodate the
finer grid resolution used for the mercury modeling study;
however, the standard horizontal expansion rate of 339 km2 per
hour was not changed. Although each puff was defined with four
separate vertical layers, each layer of an individual puff was
advected through the model cell array by the same wind velocity
field. Thus, the layers of each puff always remained vertically
stacked. Wind field initialization data for a National Weather
Service prognostic model, the Nested Grid Model (NGM), were
obtained from the NOAA Atmospheric Research Laboratory for the
entire year of 1989. Wind analyses for the op=0.897 vertical
level of the NGM were used to define the translation of the puffs
across the model grid, except during the months of January,
February, and December, when the ap=0.943 vertical level was used
to reflect a more shallow mixed layer. ap is a pressure-based
vertical coordinate equal to (p-ptop) / (PSurface-Ptop) •
Pollutant mass was removed from each puff by the processes
of wet deposition, dry deposition, diffusive air exchange between
the surface-based mixed layer and the free atmosphere and, in the
M-5
-------
case of reactive species, chemical transformation. The model
parameterizations for these processes are discussed in Section
M.I.3. Precipitation data for the entire year of 1989, obtained
from the National Climatic Data Center, were used to estimate the
wet removal of all pollutant species modeled. Wet and dry
deposition mass totals were accumulated and average surface-level
concentrations were calculated on a monthly basis for each model
cell designated as a receptor. Except for cells in the far
southwest and eastern corners of the model domain where there
were no wind data, all cells were designated as receptors for the
mercury simulation. When the mass of pollutant on a puff
declines through deposition, vertical diffusion or transformation
to a user-defined minimum value, or when a puff moves out of the
model grid, the puff and its pollutant load is no longer tracked.
The amount of pollutant in the terminated puff is taken into
account in monthly mass balance calculations so that the
integrity of the model simulation is assured. Output data from
the model includes monthly wet and dry deposition totals and
monthly average air concentration for each modeled pollutant, in
every receptor cell.
M.I.3 Model Parameterizations
M.1.3.1 Chemical Transformation and Wet Deposition. The
simplest type of pollutant to model with RELMAP is the inert
type. To model inert pollutants, one can simply omit chemical
transformation calculations for them, and not be concerned with
chemical interactions with the other chemical species in the
model. In the mercury version of RELMAP, particulate mercury and
total carbon were each modeled explicitly as inert pollutant
species. Reactive pollutants are normally handled by a chemical
transformation algorithm. RELMAP was originally developed to
simulate sulfur deposition, and the algorithm for transformation
of sulfur dioxide to sulfate was independent of wet deposition.
For gaseous mercury, however, the situation is more complex.
Since there are no gaseous chemical reactions of mercury in the
atmosphere which appear to be significant6, for this modeling
study mercury was assumed to be reactive only in the aqueous
medium. Elemental mercury has a very low solubility in water,
while oxidized forms of mercury and particle bound mercury
readily find their way into the aqueous medium through
dissolution and particle scavenging, respectively. Worldwide
observations of atmospheric mercury, however, indicate that
particulate mercury is generally a minor constituent of the total
mercury loading9 and that gaseous elemental mercury (Hg°) is, by
far, the major component. Swedish measurements of large north-
to-south gradients of mercury concentration in rainwater without
corresponding gradients of atmospheric mercury concentration
M-6
-------
suggest the presence of physical and chemical interactions with
other pollutants in the precipitation scavenging process.9
Aqueous chemical reactions incorporated into the mercury version
of RELMAP were based on research efforts in Sweden 7-10'13-17
Unlike other pollutants that have been modeled with RELMAP,
mercury has wet deposition and chemical transformation processes
that are interdependent. A combined transformation/wet-removal
scheme proposed by Petersen et al.6 was used. In this scheme,
the following aqueous chemical processes were modeled when and
where precipitation is present.
1) oxidation of dissolved Hg° by ozone yielding Hg2+
2) catalytic reduction of this Hg2* by sulfite ions
3) adsorption of Hg2+ onto carbon soot particles suspended
in the aqueous medium
Petersen et al.6 shows that these three simultaneous reactions
can be considered in the formulation of a scavenging ratio for
elemental mercury gas as follows:
where,
k-L is the second order rate constant for the aqueous
oxidation of Hg° by 03 equal to 4.7 x 107 M^s"1,
kz is the first order rate constant for the aqueous
reduction of Hg2+ by sulfite ions equal to 4.0 x 10"4 s"1,
HHg is the dimensionless Henry's Law coefficient for Hg°
(0.18 in winter, 0.22 in spring and autumn, and 0.25 in
summer as calculated from Sanemasa),18
[O3]aq is the aqueous concentration of ozone,
K3 is a model specific adsorption equilibrium constant
(5.0 x lO'6 mV1) ,
csoot is the total carbon soot aqueous concentration, and r
is the assumed mean radius of soot particles (5.0 x 10~7 m)
[°j]a<7 is obtained from this equation.
M-7
-------
where H03 is the dimensionless Henry's Law coefficient for ozone
(0.448 in winter, 0.382 in spring and autumn, and 0.317 in summer
as calculated from Seinfeld) ._19 csoot is obtained from the
simulated atmospheric concentration of total carbon aerosol using
a scavenging ratio of 5.0 x 105.
The model used by Petersen et al.6 defined one-layer
cylindrical puffs, and the Hg° scavenging layer was defined as
the entire vertical extent of the model. The RELMAP defines
4-layer puffs to allow special treatment of surface-layer and
nocturnal inversion-layer processes. It was believed that, due
to the low solubility of Hg° in water, the scavenging process
outlined above would only take place effectively in the cloud
regime, where the water droplet surface-area to volume ratio is
high, and not in falling raindrops. Thus the Hg° wet scavenging
process was applied only in the top two layers on RELMAP, which
extends from 200 meters above the surface to the model top.
For the modeling study described in Petersen et al.6, the
wet deposition of Hg2* was treated separately from that of Hg°.
Obviously, any Hg2+ dissolved into the water droplet directly
from the air could affect the reduction-oxidation balance between
the total concentration of Hg° and Hg2+ in the droplet. Since the
solubility and scavenging ratio for Hg2+ is much larger than that
for Hg°, and since air concentrations of Hg° are typically larger
than those of Hg2+, separate treatment of Hg2+ wet deposition was
deemed acceptable. Thus, process 2 above was only considered as
a moderating factor for the oxidation of dissolved Hg°.
There was no attempt to develop a new interacting chemical
mechanism for simultaneous Hg° and Hg2" wet deposition. Although
Hg2+ was recognized as a reactive species in aqueous phase redox
reactions, it was, in essence, modeled as an inert species just
like particulate mercury and total carbon soot. With the rapid
rate at which the aqueous Hg2* reduction reaction is believed to
occur in the presence of sulfite, it is possible that an
interactive cloud-water chemical mechanism might produce
significant conversion of scavenged Hg2+ to Hg°, with possible
release of that Hg° into the gaseous medium.
Wet deposition of Hg2*, particulate mercury, and total
carbon soot in the mercury version of RELMAP were modeled with
the same scavenging ratios used by Petersen et al.6 The gaseous
M-8
-------
nitric acid scavenging ratio of 1.6 x 10~6 has been applied for
Hg2+ since the water solubilities of these two pollutant species
are similar. For particulate mercury, a scavenging ratio of 5.0
x 10"5 was used, based on experiences in long-range modeling of
lead in northern Europe. As previously mentioned, a scavenging
ratio of 5.0 x 10"5 was also used for total carbon soot. These
scavenging ratios for Hg2+, particulate mercury, and total carbon
soot were applied to all four layers of the RELMAP in the
calculation of pollutant mass scavenging by precipitation.
M.I.3.2 Dry Deposition. Recent experimental data indicate
that elemental mercury vapor does not exhibit a net dry
depositional flux to vegetation until the atmospheric
concentration exceeds a rather high compensation point of around
10 ng/m3.20 This compensation point is apparently dependent on
the surface or vegetation type and represents a balance between
emission from humic soils and dry deposition to leaf surfaces.21
Since the emission of mercury from soils was accounted for with a
global-scale ambient concentration and not an actual emission of
Hg°, for consistency, there was no explicit simulation of the dry
deposition of Hg°.
For Hg2+ during daylight hours, a dry deposition velocity
table previously developed based on HN03 data was used.22-23 The
dry deposition characteristics of HN03 and Hg2* should be similar
since their water solubilities are similar. This dry deposition
velocity data, shown in Table M-2, provided season-dependent
values for 11 land-use types under six different Pasquill
stability categories. Based on the predominant land-use type and
climatological Pasquill stability estimate of each RELMAP grid
cell, and the season for the month being modeled, the dry
deposition velocity values shown in Table M-2 were used for the
daytime only. For nighttime, a value of 0.3 cm/s was used for
all grid cells since the RELMAP does not have the capability of
applying land-use dependent dry deposition at night. Since the
nighttime dry deposition was applied only to the lowest layer of
the model and no vertical mixing is assumed for nighttime hours,
all Hg2+ modeled to be quickly depleted from the lowest model
layer by larger dry deposition velocities.
For Hgpart, Petersen et al.6 used a dry deposition velocity of
0.2 cm/s at all times and locations. Lindberg et al.24 (1991)
suggests that the dry deposition of Hgpart seems to be dependent
on foliar activity. During the daylight hours of spring, summer,
and autumn, a dry deposition velocity of 0.11 cm/s was used for
H9Part/ except for model cells with predominant surface
characteristics of water, barren, and rocky terrain where
0.02 cm/s was used. At night and at all hours during the winter,
M-9
-------
Table M-2. Dry Deposition Velocity (cm/8) for Divalent Mercury
Season
Winter
Spring
Summer
Autumn
Land-Use Category
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Pasquill Stability Category
A
4.83
1.32
1.89
3.61
3.61
3.49
1.09
1.16
2.02
1.62
1.98
4.59
1.60
1.49
3.42
3.42
3.28
0.98
1.05
1.85
1.60
1.84
4.47
2.29
1.67
3.32
3.32
3.17
0.92
0.98
1.91
1.90
1.95
4.64
2.02
1.78
3.46
3.46
3.32
1.00
1.07
1.88
1.93
1.97
B
4.80
1.30
1.86
3.57
3.57
3.46
1.07
1.14
2.00
1.60
1.95
4.54
1.56
1.46
3.36
3.36
3.23
0.96
1.04
1.82
1.56
1.81
4.41
2.25
1.64
3.26
3.26
3.12
0.90
0.98
1.88
1.87
1.91
4.59
1.98
1.74
3.40
3.40
3.27
0.98
1.06
1.86
1.90
1.94
C
4.61
1.20
1.73
3.34
3.34
3.27
0.98
1.06
1.89
1.48
1.81
4.35
1.46
1.36
3.13
3.13
3.05
0.89
0.97
1.73
1.46
1.67
4.12
2.04
1.48
2.95
2.95
2.86
0.81
0.89
1.73
1.69
1.71
4.35
1.81
1.59
3.13
3.13
3.05
0.89
0.97
1.73
1.74
1.76
D
4.30
1.05
1.52
3.02
3.02
2.99
0.85
0.92
1.70
1.30
1.58
4.05
1.28
1.19
2.81
2.81
2.78
0.77
0.85
1.56
1.28
1.46
3.73
1.76
1.26
2.57
2.57
2.53
0.69
0.76
1.52
1.44
1.46
4.05
1.60
1.40
2.81
2.81
2.78
0.77
0.85
1.56
1.53
1.54
E
2.79
0.46
0.73
1.68
1.68
1.77
0.38
0.39
0.96
0.60
0.73
2.49
0.53
0.48
1.42
1.42
1.55
0.31
0.30
0.84
0.53
0.58
2.07
0.72
0.41
1.04
1.04
1.27
0.22
0.23
0.77
0.52
0.42
2.49
0.73
0.60
1.42
1.42
1.55
0.31
0.30
0.84
0.68
0.63
F
0.36
0.15
0.19
0.29
0.29
0.29
0.13
0.31
0.21
0.17
0.20
0.36
0.18
0.17
0.29
0.29
0.29
0.13
0.13
0.21
0.18
0.20
0.36
0.24
0.19
0.29
0.29
0.29
0.13
0.13
0.22
0.21
0.21
0.36
0.21
0.19
0.29
0.29
0.29
0.13
0.13
0.21
0.20
0.20
M-10
-------
all cells used 0.02 cm/s as the dry deposition velocity for
Hgpart • Lindberg et al.24 suggested a value of 0.003 cm/s for
non-vegetated land, but since the RELMAP can not model land-use
dependent dry deposition at night, the value of 0.02 cm/s was
used for these cells by necessity.
For total carbon soot, daytime dry deposition velocities
were calculated using a FORTRAN subroutine developed by the
California Air Resources Board.25 A particle density of 1.0 g/cm3
and radius of 0.5 urn was assumed. Table M-3 shows the wind speed
(u) used for each Pasquill stability category in the calculation
of deposition velocity from the CARE subroutine, while Table M-4
shows the roughness length (z0) used for each land-use category.
For nighttime, a dry deposition velocity of 0.07 cm/s was used
for all seasons and land-use types.
The RELMAP assumes instantaneous vertical mixing of all
pollutants through the entire depth of the model. For grid cells
with significant emission rates, this results in an
underestimation of the ground-level concentration and therefore
an underestimation of the dry deposition rate for mercury species
emitted near the ground. To remedy this, the model used a local
dry deposition factor for Hgpart in a similar manner as Petersen
et al.6 This local deposition factor was 0.5, meaning that
one-half of the Hgpart emissions from a grid cell were assumed to
dry deposit within that grid cell by processes not otherwise
simulated by the dry deposition parameterization. There was no
application of a local deposition factor for Hg2+ since the
majority of its emission was assumed to be from elevated point
dry deposition factor for Hgpart in a similar manner as Petersen
et al.6 This local deposition factor was 0.5, meaning that
one-half of the Hgpart emissions from a grid cell were assumed to
dry deposit within that grid cell by processes not otherwise
simulated by the dry deposition parameterization. There was no
application of a local deposition factor for Hg2+ since the
majority of its emission was assumed to be from elevated point
sources.
M.I.3.3 Vertical Exchange of Mass with the Free Atmosphere.
Due to the long atmospheric lifetime of mercury, the RELMAP was
adapted to allow a treatment of the exchange of mass between the
surface-based mixed layer and the free atmosphere above. As a
first approximation, a pollutant depletion rate of 5 percent per
3-hour timestep was chosen to represent this diffusive mass
exchange. When compounded over a 24-hour period, this depletion
rate removes 33.6 percent of an inert, non-depositing pollutant.
M-ll
-------
Table M-3. Wind Speeds Used for Each Pasquill Stability Category
in the CARB Subroutine Calculations
Stability Category
A
B
C
D
E
F
Wind Speed (m/s)
10.0
5.0
5.0
2.5
2.5
1.0
Table M-4. Roughness Length Used for Each Land-Use Category in
the CARB Subroutine Calculations
Land-Use Category
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Roughness Length (meters)
autumn-winter
0.5
0.15
0.12
0.5
0.5
0.4
10'6
0.1
0.2
0.135
0.1
spring-summer
0.5
0.05
0.1
0.5
0.5
0.4
10-6
0.1
0.2
0.075
0.1
Since all three forms of the modeling mercury deposit to the
surface to some degree, their effective diffusion rate out of the
top of the model is less than 33.6 percent per day.
M.I.4 Discussion of RELMAP Modeling Uncertainties
M.I.4.1 Vertical Model Domain. The RELMAP model top is
defined to be the maximum vertical extent of the convectively
M-12
-------
driven mixed layer. Monthly values defined from
mixed-layer-height climatology are rough estimates of a
meteorological phenomenon that may not exist in many situations.
Although a surface-based mixed layer may be well defined,
pollutants that persist in the atmosphere for long periods of
time are certain to mix to some degree into the free atmosphere
above the mixed-layer top. Chlorofluorocarbon (CFC) compounds
are an extreme example of this possibility. Elemental mercury
deposits relatively slowly through precipitation processes due to
its low water solubility, and its dry deposition appears to be
minimal since it is in vapor form under normal atmospheric
conditions. In fact, pollutant mass balance accounting
information from the RELMAP mercury simulation indicated that
approximately 75 percent of all elemental mercury emitted was
transported out of the model domain before it was wet or dry
deposited. Elemental mercury appears to be quite persistent in
the atmosphere.
Since the RELMAP does not simulate the flux of air or
pollutant through the height of the mixed layer, which is fixed
for each monthly simulation, the use of horizontally
divergent/convergent wind fields to define the motion of the
pollutant puffs can sometimes result in unrealistic instantaneous
concentration fields. Horizontally convergent winds will tend to
concentrate puffs at the point of convergence, resulting in high
modeled concentrations when the effects of the puffs are summed
together. Ordinarily, horizontal convergence in the
surface-based mixed layer would push the mixed-layer top higher
into the atmosphere as a result of the incompressible nature of
air in general atmospheric motion. This higher mixed-layer top
would compensate for the greater pollutant mass loading per unit
area from the converging puffs, keeping the resulting pollutant
concentration more constant. The RELMAP was not designed to
provide instantaneous realizations of pollutant concentration
fields. Rather, it was designed for seasonal and annual
simulations where the total effects of convergent and divergent
wind fields can balance one another. There does exist some
uncertainty, however, as to whether this balance actually occurs
in all situations.
M.I.4.2 Aqueous Chemistry. The aqueous reduction-oxidation
chemistry mechanism in the mercury version of RELMAP was applied
only to the Hg° dissolved from the ambient air into the water
droplet. Where significant concentrations of Hg2* from emissions
exist in the ambient air, this Hg2+ could be dissolved into the
water droplet along with the Hg° and inhibit the scavenging of
Hg°. The RELMAP results described above indicate that Hg2+ air
concentrations are certainly lower than those for Hg° at the
M-13
-------
length scales of the RELMAP grid cells; however, the magnitude of
the effect of ambient Hg2+ on the Hg° oxidation scavenging is not
yet well understood.
Another source of modeling uncertainty in aqueous chemistry
relates to the fact that the aqueous chemical mechanisms were
invoked only when and where precipitation was known to occur, and
precipitation fields were only defined over land areas where
precipitation observations were available. Significant wet
transformation and removal of mercury may occur over oceanic
areas were precipitation observations are not available, and it
is possible that significant aqueous chemistry is occurring in
non-precipitating clouds.
M.I.4.3 Transport and Diffusion. Since the RELMAP
simulates transport and diffusion only in the surface-based mixed
layer and vertical wind shear is small when the mixed-layer is
well defined, under ideal conditions transport and diffusion are
handled adequately. When the surface-based mixed layer is not
well defined, vertical gradients in the speed and/or direction of
the wind may be present which cannot be represented by the motion
of individual Lagrangian puffs whose layers remain vertically
stacked. There are two techniques that might be used to
represent vertical wind shear in the RELMAP: puff splitting and
wind-shear-dependent puff expansion. Due to computational limits
and scheduling constraints, these were not attempted. The most
complete solution to the problem of vertical wind shear is the
use of a Eulerian reference frame for numerical modeling. The
Atmospheric Characterization and Modeling Division of U.S. EPA's
Atmospheric Research and Exposure Assessment Laboratory has
proposed the development of a Toxics Linear Chemistry Model
(TLCM) using the Eulerian reference frame of the Regional Acid
Deposition Model (RADM). The TLCM could be operational within
two years.
M.I.4.4 Boundary Fluxes of Pollutants. Due to the fact
that RELMAP simulates atmospheric pollutant loading as the
combined effect of a population of discrete Lagrangian puffs, and
the fact that elemental mercury gas has a long residence time in
the atmosphere, natural mercury emissions from the oceans and
land surfaces could not be explicitly modeled. Given the general
west-to-east wind flow at the latitudes of the continental U.S.,
simulated puffs of natural mercury emissions could be emitted
from all grid cells, but their effects would be artificially
concentrated in the eastern sections of the model domain. The
only puffs that could impact the western areas would be those
originating from the far western grid cells, while the eastern
areas could be impacted by puffs from all parts of the model
M-14
-------
domain. The use of a Eulerian-type model would allow the
definition of boundary fluxes of pollutant based on larger-scale
model results or assumed background concentration levels.
M.I.5 Verification of Mercury RELMAP Results
In general, the EPA modelers believed that the RELMAP would
tend: 1) to overestimate Hg values in urban areas; and 2) to
underestimate Hg values in rural areas and in the urban center of
larger cities. There was limited data available to check the
model and confirm those beliefs. The discussion below summarizes
the the limited comparison between model predictedions and
measured data. [Note that measured Hg values will have some
uncertainty associated with each reported value.] Overall, the
RELMAP seems to over- and underestimate Hg values within a factor
of 2 and was relatively unbiased in its predictions..
M.I.5.1 Verification of Mercury Concentration Estimates.
The RELMAP-simulated HgO and Hg2+ air concentrations taken
together with the assumed background HgO concentration of 1.6
ng/m3 agree well with observations of vapor-phase Hg air
concentration in Minnesota26, in Vermont27 and in Wisconsin28.
These observations showed that annual average vapor-phase Hg
concentrations were near the levels found over other remote
locations in the northern hemisphere, from 1.6 to 2.0 ng/m3. The
RELMAP simulation indicated 1.64 ng/m3 in Minnesota, 1.67 ng/m3
in Wisconsin and 1.70 ng/m3 in Vermont. Measurements taken for a
two-week period at three sites in Broward County, Florida29, show
slightly elevated vapor-phase Hg air concentrations for two of
those sites downwind of industrial activities. These two sites
had average vapor-phase Hg air concentrations of 3.3 and 2.8
ng/m3. The RELMAP simulation results for the Fort Lauderdale
area show only about a 0.2 ng/m3 elevation of the annual average
vapor-phase Hg (HgO plus Hg2 + ) concentration over the 1.6 ng/m3
background value assumed. However, the measurements in Broward
County did not extend for a significant portion of the year and
there was no discrimination between HgO and Hg2+ forms. The
third site for their observations had an average vapor-phase air
concentration of 1.8 ng/m3, exactly what the RELMAP simulation
suggests. Obviously, a more comprehensive air monitoring program
is required before an evaluation of the RELMAP results for
Florida can be performed.
The maximum annual average HgP (particulate mercury)
concentrations from the RELMAP simulation are around 50-100 pg/m3
(0.05-0.1 ng/m3) in the urban centers of the Northeast. A study
in urban Detroit during March of 1992 found an average HgP
concentrations over an 18-day period of 94 pg/m3.19 The RELMAP
M-15
-------
simulation suggests an annual average HgP concentration in the
Detroit area of about 50 pg/m3. Average HgP concentrations of
between 34 and 51 pg/m3 were measured in Broward County, Florida,
at three sites from 25 August to 7 September of 1993.29 The
RELMAP simulation showed an average annual concentration of about
50 pg/m3 for HgP around the city of Fort Lauderdale.
Researchers have found annual average HgP air concentrations of
10.5 pg/m3 in Pellston, Michigan, 22.4 pg/m3 in South Haven,
Michigan, and 21.9 pg/m3 in Ann Arbor, Michigan, from April 1993
to April 1994, and 11.2 pg/m3 in Underbill, Vermont, for the year
of 1993.8 The RELMAP simulation results agree quite well with
these observations also, with 10 to 20 pg/m3 indicated for
Pellston and Underbill, and 20 to 50 pg/m3 indicated for South
Haven and Ann Arbor.
M.I.5.2 Verification of Mercury Wet Deposition Results.
Measurements of mercury wet deposition at three locations in
northeastern Minnesota during 1989 indicated annual wet
deposition rates of 6.5 ug/m2 at Duluth, 13.5 ug/m2 at Marcell
and 41.9 ug/m2 at Ely.30 A later study measuring annual wet
deposition of Hg during 1990, 1991 and 1992 at Ely, Duluth and
seven other sites in Minnesota, upper Michigan and northeastern
North Dakota found all annual wet deposition totals to be within
the range of 3.8 to 9.7 ug/m2.31 The RELMAP simulation indicated
a range of 1.7 to 10.9 ug/m2. Measurements at Little Rock Lake,
in northern Wisconsin, of Hg in snow during February and March,
1989, and in rain from May to August, 1989, have been used to
estimate annual Hg depositions in rain and snow of
4.5 and 2.3 ug/m2, respectively.26 This suggests a total annual
Hg wet deposition of 6.8 ug/m2 at Little Rock Lake, while the
RELMAP simulation showed 5.8 ug/m2. Measurements at Presque
Isle, also in northern Wisconsin, from 1993 to 1994 indicated a
wet deposition rate for total Hg of 5.2 ug/m2/yr,28 only slightly
less than the RELMAP simulation of 5.7 ug/m2/yr.
There were also some Hg wet deposition measurement programs
conducted during the early 1990's in somewhat less remote sites
in Michigan and Vermont. Observations during two years of event
precipitation sampling at three sites in Michigan show evidence
for a north-to-south gradient in Hg wet deposition.32 The total
Hg wet deposition observed for the two years at South Haven, in
the southwest part of the state, were 9.45 and 12.67 ug/m2, while
the RELMAP simulation showed 13.98 ug/m2. At Pellston, in the
northern part of the lower peninsula of Michigan, the wet
deposition was 5.79 and 5.54 ug/m2, while the RELMAP showed 8.12
ug/m2. At Dexter, in southeast Michigan just west of Detroit,
the observed wet deposition was 8.66 and 9.11 ug/m2. The RELMAP
simulation showed 24.74 ug/m2 for the grid cell containing
M-16
-------
Dexter, but this grid cell also contains the city of Detroit.
The RELMAP 40-km horizontal grid is unable to resolve urban-scale
gradients. The higher second-year wet deposition at South Haven
has been attributed to increased precipitation rate, and
measurements at Underhill, Vermont27, are cited as further
evidence of the importance of precipitation amount.32 From
December 1992 to December 1993, the average volume-weighted Hg
concentration at Underhill (8.3 ng/L) was similar to that
observed at Pellston (7.9 ng/L). However, with more
precipitation during that period, the total Hg wet deposition at
Underhill was 9.26 ug/m2, significantly higher than at Pellston.
The RELMAP simulation for Underhill shows 17.41 ug/m2, again due
to influences from a nearby city, Burlington, VT.
The very large total Hg wet deposition values (>50 ug/m2)
from the RELMAP simulation for some of the larger urban centers
in the Ohio Valley and Northeast regions can not be evaluated
thoroughly due to a lack of long-term precipitation event
sampling at those locations. RELMAP results of around 50 ug/m2
along the east coast of south Florida can be compared to observed
data. Precipitation event sampling was performed from 19 August
to 7 September of 1993 at 4 sites in Broward County, Florida, in
and around the city of Fort Lauderdale.29 During the 20-day
sampling period, total Hg mean concentrations in precipitation
were 35, 57, 40 and 46 ng/L at the 4 sites. Given the average
annual precipitation of 150 cm per year typical of that area, the
resulting annual wet deposition estimates at these 4 sites are
52.5, 85.5, 60 and 69 ug/m2. Since most of the annual rainfall
in Broward County occurs in warm tropical conditions of the March
to October wet season, this extrapolation from 20 days during the
wet season to an annual estimate may be considered reasonable.
However, additional urban measurement studies will certainly be
required to allow any credible evaluation of RELMAP wet
deposition results in heavily populated, industrialized areas.
M.2 Description of COMPDEP Air Dispersion Model
M.2.1 Description of the COMPDEP Air Quality Model
General references for this section are Overcamp33, Rao,34
and U.S. EPA.35
The COMPDEP model uses hourly meteorological data to
estimate air concentrations and deposition fluxes from a point
source. In this section a summary description of the model is
presented. In Section M.2.2.1, specific modifications made for
this assessment are discussed.
M-17
-------
M.2.1.1 Atmospheric Stability and System Used in COMPDEP.
After pollutants are emitted from a source, they are diluted with
ambient air. The degree of dispersion is a function of wind
speed and the level of turbulence. In general, higher wind
speeds or turbulence result in lower air concentrations. The
amount of turbulence is quantified in terms of the atmospheric
stability. A stable atmosphere is one that suppresses vertical
motions, hence mitigating turbulence, while an unstable
atmosphere is one that enhances turbulence. Atmospheric
turbulence per se is difficult and expensive to measure36, and it
is usually estimated from other more easily measurable
quantities. In particular, the stability of the atmosphere is
typically characterized by the vertical temperature profile of
the atmosphere.
For an isolated parcel of air in which no heat is
transferred in or out (adiabatic), one can show using the first
law of thermodynamic and the hydrostatic equation37 that there is
a 1 degree (C) decrease for every 100 m increase in altitude, and
a 1 degree increase for every 100 m decrease. This is called the
adiabatic lapse rate.
The atmosphere is not adiabatic as it is both heated and
cooled. This results in temperature profiles that differ from
the adiabatic profile, and it is this difference that is
ultimately responsible for a given atmospheric stability. The
three broad classes of stability and their associated temperature
profiles are summarized in Table M-5.
It should be noted that any time the temperature increases
with altitude, the atmospheric condition is termed an inversion.
Because of the associated stability, inversions tend to
decrease.38
The most widely used scheme of atmospheric stability
classification, and that used in COMPDEP, was developed by
Pasquill39 and modified by Gifford40. There are six stability
classes, denoted with the letters A through F. In general,
classes A through C indicate unstable conditions, D is roughly
neutral, and classes E and F represent stable conditions. Table
M-6, from Hanna et al.41, originally from Gifford42, shows the
criteria for the different classes.
The meteorological conditions that are used to determine the
stability class are shown in Table M-7. From this table, it can
be seen that extremely unstable conditions (class A) occur during
the day with light winds and moderate to strong solar radiation
M-18
-------
Table M-5. Classes of Atmospheric Stability and Associated
Vertical Temperature Distribution
Vertical Temperature Profile
Increases with height, or decreases
less rapidly than adiabatic lapse rate
Nearly identical with adiabatic rate
Decreases with height faster than
adiabatic lapse rate
General Result
Vertical motions inhibited
No significant buoyant forces
Vertical motions enhanced
Class of Stability
Stable
Neutral
Unstable
Table M-6 Pasguill Turbulence Types and Corresponding
Atmospheric Conditions42
Pasquill
Turbulence Type
A
B
C
D
E
F
Atmospheric Stability
Conditions
Extremely unstable
Moderately unstable
Slightly unstable
Neutral'
Slightly stable
Moderately stable
1 Applicable to heavy overcast day or night.
(necessary conditions for the formation of an unstable
temperature profile.33 Conversely, extremely stable conditions
can occur only at night with clear skies and light winds. Hanna
et al. note that some have filled in the blank in Table M-7 with
a "G" class, but this has not received wide acceptance.41
Other stability classification schemes exist. For example,
M.E. Smith43 proposed a classification scheme that is based on
wind direction, and Cramer44 advocated a method based on observed
wind fluctuations at a height of 10m (often referred to the
Brookhaven National Laboratory, or BNL, stability classes). In
addition, Irwin45 proposed a method of allowing for a continuum
of stability, as opposed to a discrete approach such as the
Pasquill method. Use of the Pasquill letter classes is common
due to their ease of use, and because they have produced
satisfactory results.
M-19
-------
Table M-7. Meteorological Conditions Defining Pasquill
Turbulence Types42
Surface wind
speed (m/sec)
<2
2-3
3-4
4-6
>6
Daytime Solar Radiation
Strong
A
A-B
B
C
C
Moderate
A-B
B
B-C
C-D
D
Slight
B
C
C
D
D
Nighttime conditions
Mostly Overcast
E
D
D
D
Mostly Clear
F
E
D
D
For this assessment, the stability classes for each hour
were estimated with the RAMMET program46, using hourly surface
meteorological data.
Estimation of wind speed is important because higher wind
speeds result in greater dispersion and hence reduced
concentrations of pollutants. Frictional forces cause the
surface wind speed, which is usually the value available, to be
lower than the speed at the stack top. A power law wind speed
profile is typically used to calculate the change of wind speed
with height, and takes the following form:
u.
u
ref
-refj
where, uref = wind speed at the reference height
zref = reference height
us = wind speed at the release height
zs = release height
p = wind speed profile exponent (dependent on
atmospheric stability and is between 0 and 1). In
general, a reference anemometer height of 10 m,
the standard height for measurement of wind speed
and direction by the National Weather Service, is
used.33
M-20
-------
The wind profile exponents used in COMPDEP are given in
Table M-8. These are the default values for rural settings in
U.S. EPA35 and are based on Irwin45. Although default values for
urban settings are available in U.S. EPA35 as well, due to
limitations of the COMPDEP model it was decided that their use
was not warranted for this assessment. The default values for
urban settings were about twice as high as the rural ones for
classes A, B, and C, which resulted in higher wind speeds at the
stack top.
Directional shear with height is not included, which means
that the direction of flow is assumed to be the same at all
heights over the region. The taller the effective height of a
source, the larger the expected error in direction of plume
transport.47
M.2.1.2 Plume Rise. A general principle, borne out by the
analytic solutions of the diffusion algorithms and known since at
least 191748, is that the maximum ground level concentration is
inversely proportional to the height of release.36 Due to the
buoyant properties of the stack gases and the velocity of the
stack gases emitted, the height of release from a modeling
perspective is usually higher than the actual physical height of
the stack. This height is called the effective stack height and
is the sum of the physical stack height and the rise of the
plume.
Due to the sensitivity of the maximum concentrations to the
effective stack height, and because the maximum downwind
concentration has been historically the output of interest for
regulatory agencies, numerous methods exist for estimating plume
rise in a variety of conditions. Overcamp33 noted that over 50
different plume rise formulas had been published by 1977, and
Pasquill49 observed that there are many rival formulae from which
to choose.
The method used in COMPDEP is based on Briggs50-51-52 and
Bowers et al.53 With this approach, it must be determined
whether thermal buoyancy or vertical momentum is dominating the
plume's motion. Estimates of the buoyancy flux (Fb, units of
m4/s3) and momentum flux (Fm, units of mVs2) are based on
Briggs54:
M-21
-------
Table M-8. Wind Profile Exponents Used In The Assessment
Stability
Category
A
B
C
D
E
F
Wind Profile
Exponent
0.07
0.07
0.10
0.15
0.35
0.55
= 9
4T
' -
where, g = acceleration due to gravity (9.80616 m/s2)
vs = stack gas exit velocity (m/s)
ds = stack diameter (m)
Ts = stack gas temperature (K)
Ta = ambient air temperature (K).
If the stack gas temperature is less than or equal to the
ambient air temperature, it is assumed that plume rise is
dominated by momentum, in which case the effective stack height
is given by this formula:
™*T
s
Unstable or neutral
1.5
1/3
, 3d —} Stable
u.
where,
actual physical stack height (m)
stability parameter41-51 and is only used in
calculations for stable conditions (classes E
and F):
M-22
-------
s =
0.020 StabilityClassE
0.035 Stabili tyClassF
where the constants 0.02 and 0.035 are default approximations of
the derivative of the ambient potential temperature with respect
to height.
If the stack gas temperature is greater than the ambient air
temperature, then the determination of which force is dominating
is made by calculating a critical crossover temperature
difference DTC above which it is assumed that buoyancy dominates.
This critical value depends on the stack gas temperature,
atmospheric stability, and the magnitude of the buoyant flux
itself in a chain of empirical formulas50"54 as follows:
ATc =
0.0297T
0.00575T
v,
1/3
V,
2/3
Unstable or neutral, Fb<55
Unstable or neutral,F^.55
0.019582T v Js Stable
= h.
21.425-
38.71
2.6
,3/4
U
,3/5
Unstable or neutral,Ffc<55
Unstable or neutral,F^55
Stable
If the difference between the stack gas temperature and
the ambient air is less than the critical temperature, then it is
assumed that momentum dominates, and the equations above are
used. Otherwise, the effective stack height is given by these
equations:
M-23
-------
49
119
,5/8
u
Buoyancy rise, Unstable or neutral, F.<55
Buoyancy rise, Unstable or neutral,
2.0715—^- Buoyancy rise, Stable
0 Momentum rise, all stability classes
Past a certain distance the plume is assumed to stop rising.
This distance is called the distance to final rise (xf) and is
calculated in a similar method as for the plume rise. The
calculation is dependent on which force dominates, the
atmospheric stability class, and the magnitude of the buoyant
flux. It is estimated by the following:
The estimated distance-dependent plume rise is the minimum
of the effective stack height for final rise and the height based
on that for buoyancy-dominated conditions .55 The
distance-dependent plume effective stack height he(x) is this:
he(x) =min
hs+1.60
,1/3
,2/3
U
This is sometimes referred to as the "2/3 law" of plume rise51
and follows from the assumptions that buoyancy is conserved and
that the initial plume momentum is negligible for a very buoyant
plume in unstratified surroundings. It is claimed56'57 that the
constant 1.60, based on the best fit to data in Table II of
Briggs51, can be expected to be accurate within 40 percent with
variations due to downwash or local terrain effects.
M.2.1.3 Estimation of Air Concentration Accounting for
Plume Depletion. The method used is that developed in Rao. 34
All estimations of concentration and deposition originate from
the steady-state form of the atmospheric advection-diffusion
equation:
u
w
where, C(x,y,z) = pollutant concentration at (x,y,z)
M-24
-------
x = downwind distance
y = horizontal crosswind distance
z = vertical distance
U = the constant average wind speed for the hour
W = the gravitational settling velocity (cm/g)
Ky = the eddy diffusivity in the crosswind direction
Kz - the eddy diffusivity in the vertical directions
For a continuous point source of strength Q located at
(0,0,11), the assumed boundary conditions are defined by this
equation:
C(0,y,z) = -2 6(y) 5{z-H)
C(°°,y,z) = C(x,±~,z) = C(x,y,°°) = 0
IKC+WC] = \v.c]
[ z z Jz=o L d Jz=o
where, U = wind speed (m/s)
Vd = depositional velocity (cm/s)
H = height above the ground
The first condition is the limiting condition of the mass
continuity equation at the source, with d being the Dirac delta
"function". This condition is implicated by the assumption that
the source is coming from an infinitely small point located at
height H above the ground.
The second condition (actually three separate boundary
conditions) is equivalent to the assumption that for all times
the concentration of the pollutant is zero infinitely far away
from the source in all directions.
The final condition is the one that accounts for possible
depletion of the plume. It is the mathematical formulation of
the assumption that at ground level (z=0) the sum of the
turbulent transfer of pollutant down the concentration gradient
(Kz Cz) and the downward settling flux due to the particles'
weight (W C) is balanced by the net flux of material to the
surface resulting from an exchange between the atmosphere and the
surface.58 The deposition velocity Vd is the parameter that is
assumed to characterize the interaction between the diffusing
pollutant and the surface. If the deposition velocity is 0, then
the lower boundary acts as a perfect reflector. If it is
infinite, it acts as a perfect sink. If the deposition velocity
M-25
-------
is equal to the settling velocity, then the net deposition due to
vertical diffusion is zero. For gases and small particles, the
settling velocity is approximately 0, while for particles the
settling and deposition velocities are estimated using the GARB
algorithms59 that represent empirical relationships for transfer
resistances as a function of particle size, density, surface
roughness, and friction velocity.
It is not difficult to derive an analytic expression for the
solution of the advection-diffusion equation satisfying the
boundary conditions above, with the solution involving nothing
more complicated than exponential and error functions.
The eddy diffusion coefficients are expressed in terms of
the standard deviations of the crosswind and vertical Gaussian
concentration distributions (sy and sz, respectively), for which
extensive empirical data exist. In particular, for Fickian
diffusion34 the relationships are these:
Ky - o>(x) Jl , xz = o>(x) Jl
In practice the dependence of the standard deviations on the
downwind distance is not usually explicitly noted. Also, as is
standard in the atmospheric dispersion literature, the partial
differential equation is solved as if the eddy diffusion
coefficients do not depend on the downwind distance x. In fact,
the solution to the advection-diffusion equation would be
different were this dependence considered, with the magnitude of
difference between the two solutions depending on how
"nonconstant" the standard deviations are with respect to x
(i.e., on the magnitude of the derivative of the eddy diffusion
coefficients with respect to x) .
It is assumed that the plume is allowed to travel in a
potentially vertically bounded layer called the mixing layer
(sometimes called the Ekman layer) ,49 The height of this layer
is called the mixing height, denoted here by L. If the effective
stack height exceeds the mixing height, then the plume is assumed
to fully penetrate the elevated inversion and the ground level
concentration is set to zero. The mixing height is estimated
based on twice-daily mixing heights using the RAMMET program,
which uses the Holzworth60 procedures. These mixing heights are
considered representative in rural areas only during periods of
instability or neutral stability (stability classes A-D). The
applicability of the Holzworth method to rural areas with stable
atmospheric conditions is considered questionable, because the
M-26
-------
minimum mixing heights include the heat island effect for urban
areas. In this case, unlimited vertical mixing is assumed.
Depending on the atmospheric stability class and mixing
layer depth, the air concentration was estimated in three
different ways, all of which are derived from the analytic
solution of the original advection-diffusion equation above. The
methods are summarized in Table M-9.
Only the vertical diffusion field was modified by
deposition, and for deposition velocities on the order of a few
centimeters per second, the shape of the vertical concentration
profile was modified only slightly.34
M.2.1.4 Estimation of the Atmospheric Dispersion
Parameters. The dispersion parameters sy and sz were estimated
using equations that approximately fit the Pasquill-Gifford
curves61 These equations approximately fit the Pasquill-Gifford
curves61 and were based on a rural setting:
ay = 465.11628 x tan(3(x))
where x is the downwind distance (in km), and
= 0. 017453293 ( c - d Inx)
The parameters c and d depend on the stability class and are
given in Table M-10. The vertical dispersion parameter is
estimated by
oz = a xb
where the parameters a and Jb depend on the stability class, and
are given in Table M-ll.
M.2.1.5 Deposition Processes. COMPDEP addresses both wet
and dry deposition, taking into account the fraction of an hour
during which precipitation occurs.
M-27
-------
Table M-9. The Three Main Cases for Determining Air
Concentration With Flume Depletion Effects
Condition
Stable or unlimited mixing
Unstable/neutral, non-uniform mixing
Unstable/neutral, limited and uniform
mixing
Criteria of Determination
Pasquill classes E or F or
unstable/neutral and L>5000 m
Pasquill classes A-D,sz <1.6 L and
L<5000 m
Pasquill classes A-D,sz > 1 .6 L and
L< 5000m
Method of Solution
Analytic solution used.
Multiple eddy reflections
from both the ground and
stable layer aloft (plume is
"trapped")
Non-uniform vertical term
integrated with limited
mixing from height 0 to
infinity
Table M-10. Parameters Used to Calculate Horizontal Dispersion
Parameter sv in COMPDEP61
Pasquill Stability Class
A
B
C
D
E
F
c
24.1670
18.3330
12.5000
8.3330
6.2500
4.1667
d
2.5334
1.8096
1.0857
0.72382
0.54287
0.36191
The air concentrations are calculated accounting for plume
depletion from dry deposition. The dry deposition rate, in
g/m2/time, is given by the product of ttie deposition velocity, the
air concentration and the fraction of the hour during which
precipitation does not occur. For particles, the settling and
deposition velocities were estimated using the CARE algorithms59
that represent empirical relationships for transfer resistances as
a function of particle size, density, surface roughness and
friction velocity. In general, the deposition velocity has values
that can range from zero up to 180 cm/s.62 COMPDEP calculates the
annual wet deposition flux according to the method developed by
Slinn63 and later modified by PEI and Cramer.64 The scavenging
M-28
-------
Table M-ll. Parameters Used to Calculate Vertical Dispersion
Parameter sz in COMPDEP61
Pasquill Stability Class
A'
B«
C«
D«
E«
Fa
x (km)
<0.10
0.10-0.15
0.16-0.20
0.21 -0.25
0.26 - 0.30
0.31 - 0.40
0.41 - 0.50
0.51 -3.11
>3.11
<0.20
0.21 - 0.40
>0.40
All
<0.30
0.31 - 1.00
1.01 - 3.00
3.01 - 10.00
10.01 -30.00
> 30.00
<0.10
0.11 -0.30
0.31 - 1.00
1.01 -2.00
2.01 - 4.00
4.01 - 10.00
10.01 - 20.00
20.01 - 40.00
> 40.00
<0.20
0.21 - 0.70
a
122.800
1 58.080
170.220
179.520
217.410
258.890
346.750
453.850
b
90.673
98.483
109.300
61.141
34.459
32.093
32.093
33.504
36.650
44.053
24.260
23.331
21.628
21.628
22.534
24.703
26.970
35.420
47.618
15.209
14.457
b
0.94470
1 .05420
1 .09320
1.1262
1 .26440
1 .40940
1.72830
2.1160
b
0.93198
0.98332
1.09710
0.91465
0.86974
0.81066
0.64403
0.60486
0.56589
0.51179
0.83660
0.81956
0.75660
0.63077
0.57154
0.50527
0.46713
0.37615
0.29592
0.81558
0.78407
M-29
-------
Table M-ll. Continued
Pasquill Stability Class
x(km)
0.71 -1.00
1.01 -2.00
2.01 - 3.00
3.01 - 7.00
7.01 - 15.00
15.01 .30.00
30.01 - 60.00
> 60.00
a
13.953
13.953
14.823
16.187
17.386
22.651
27.074
34.219
b
0.68465
0.63227
0.54503
0.46490
0.41507
0.32681
0.27436
0.21716
' If the calculated value of sz exceeds 5000 m, then it is set to 5000 m.
b a, is set to 5000 m.
process consists of repeated exposures of particles and gases to
cloud or precipitation elements with some chance of collection on
the element for each exposure.65 This has been addressed
historically as a first order decay process with decay constant L,
called the scavenging coefficient (units of inverse time). The
concentration at any distance x downwind is then given by C(x,y,z)
exp(-L t), where C(x,y,z) is the (steady-state) concentration
without scavenging and t is the time since precipitation began.41
For the purpose of modelling, t was replaced with x/us (the travel
time to the receptor); thus, the decay is essentially accounting
for previous scavenging upwind of the receptor.
The wet deposition flux Dyw at a given location is
&
Dyw = A f C(x,y,z)
where z is the height from which the precipitation falls. Because
it was assumed that the effects of dry deposition and gravitational
settling are negligible compared with precipitation scavenging, the
concentration used was that without deposition effects (deposition
and settling velocities set to zero). To facilitate evaluation of
the integral, it was extended to infinity as an approximation. The
deposition flux for a given hour then reduces to this equation:
M-30
-------
-A
Dyw = f A Q Rm e
where, f = the fraction of the hour that precipitation
occurs
RW = the integrated vertical relative concentration
(unit source strength) without depletion effects
RDW can be calculated by these equations:
jy — <
KDW ~'
-0.5
Simpleor intermediateterrain
2nusx/16
Complex terrain(sector-averaged)
As noted in PEI and Cramer64, there are several assumptions in
deriving the equation for wet deposition.
1) The intensity of precipitation is constant over the entire
path between the source and receptor.
2) The precipitation originates at a level above the top of
the plume so that hydrometeors (i.e., products formed by
condensation of water vapor) pass vertically through the
entire plume.
3) The time duration of the precipitation over the entire
path between the source and the receptor point is such
that exactly f (f is defined as the fraction of the hour
in which precipitation occurs) of the hourly emission is
subject to a constant intensity for the entire travel
time required to traverse the distance between the source
and the receptor. The remaining fraction is subject only
to dry deposition processes.
In COMPDEP, the scavenging coefficient may be intensity- and
particle-size dependent, in which case the total wet deposition is
the sum of the contributions of each category particle size
category. For particles, example scavenging coefficients are from
PEI and Cramer64 and are shown in Table M-12. Only a small fraction
of the pollutants of concern for this exposure assessment
M-31
-------
Table M-12. Example of Precipitation Scavenging Coefficients (per
second) in COMPDEP
Precipitation Intensity
Heavy
Moderate
Light
Particle Size Category (mM)
Less than 2
1 .46E-03
5.60E-04
2.20E-04
2 to 10
4.64E-03
8.93E-04
1 .80E-04
Greater than 10
9.69E-03
9.69E-03
9.69E-03
are particulate or particulate-bound. Estimation of the scavenging
coefficients for vapor phase pollutants are discussed in Section
M.2.2.1.
M.2.1.6 Treatment of Terrain. The "COMP" in the name COMPDEP
refers to the capability of the model to estimate concentrations
and deposition at receptor locations at or above stack top. This
can be done in three ways: (1) the effective stack height may be
modified based on the receptor height; (2) the concentrations may
be reduced by a height-dependent correction factor for receptors
above the stack top; and (3) sector-averaging is used for receptors
above stack top.
The method of adjusting the effective stack height Ht is based
on models developed by Briggs66 and Egan6"7. With this method the
amount of reduction depends on the receptor height and empirical
terrain adjustment factors.
Ht = max{H-rh-(l-ter) , H-ter,
where, H = the effective stack height calculated without
considering terrain
rh = the height of the receptor above the stack
base
ter = stability-class dependent terrain adjustment
factor
Hmin = the minimum distance between the plume
centerline and ground.
Following standard practice,
m.
The terrain adjustment factors used in the exposure assessment
are consistent with the method of Egan67 and are shown in Table
M-13 . By choosing these terrain factors in neutral and unstable
conditions the effective stack height is reduced by rh/2
M-32
-------
Table M-13. Terrain Adjustment Factors Used in Calculating
Terrain-Dependent Effective Stack Height
Pasquill Stability Class
A
B
C
D
E
F
Terrain Adjustment Factor
(unitless)
0.5
0.5
0.5
0.5
0
0
or H/2, whichever is smaller. It should be noted that Briggs66
suggests that the stack height be reduced by rh or H/2,
whichever is smaller. Briggs' method will result in slightly
higher ground-level concentrations for the surface of small
hills.41 It should also be noted that the reduction by H/2 is
based on potential flow theory and wind-tunnel experiments.41
Both Egan's and Briggs' methods assume terrain factors of zero
for stable conditions, in which case it is assumed that the plume
maintains a constant elevation (and so the effective height is
reduced by the receptor height).
For receptors whose ground level elevation exceeds the
effective stack height, the concentrations and deposition rates
are multiplied by a "correction" factor corr, given by this:
;400-Diff)/400
corr = \ 0
1
0
-------
calculated for the leeward side of a substantial hill will not
reflect this attenuation upwind, and so such concentrations
should be considered suspect.
By setting the correction factor to one in unstable or
neutral atmospheric stability conditions, the plume was assumed
to parallel the terrain feature at the terrain-dependent effect
stack height as calculated above.
For terrain above the effective stack height, sector
averaging was used to calculate the air concentration and was
subsequently used for deposition. This assumes that there is no
crosswind variation in concentration within an angular sector
equal to the resolution of the wind direction data (22.5 degrees
for this assessment). It should be noted that there was no
technical basis for using sector averaging for terrain above
stack height rather than point estimates for the horizontal
dispersion parameters. This decision was made consequent to
personal communication with Donna Schwede (7-20-94).
M.2.1.7 Downwash. Usually, emissions from an industrial
source will rise due to a combination of their initial vertical
momentum and buoyancy. Under certain peculiar conditions,
however, they may be trapped in either the wake of the stack or
nearby building, resulting in increased concentrations. Two
types of these phenomena are addressed in COMPDEP: stack-tip
downwash and building downwash (building wake effects).
Stack-tip Downwash. In practice, it has been observed that
if the stack exit velocity is low relative to the wind speed,
then the stack emissions may be pulled into the low pressure
cavity in the wake of the stack. The emissions are pulled down
and may not rise further, resulting in higher ground level
concentrations than if plume rise had occurred. In COMPDEP, this
phenomenon was assumed to occur when the ratio of the stack exit
velocity to the wind speed at stack top was below 1.5. This
value, which has survived without modification for 25 years, is
that recommended by Briggs50 and is based on wind tunnel
experiments by Sherlock and Stalker.68 It should be noted that
very buoyant sources may accelerate fast enough to avoid any
significant downwash.33'50
If stack-tip downwash occurs, then the physical stack height
used is that of Briggs69:
M-34
-------
From the above equation it can be seen that the maximum amount
the stack height will be reduced by this method is three times
the diameter of the stack.
Building Downwash. Building downwash can occur when the
stack emittants are captured in the wake of a nearby building. A
long-standing rule-of-thumb is that building effects should not
occur if the stack height is at least 2.5 times the height of any
adjacent building. Because this was considered overly
restrictive (from a design perspective) for tall, thin buildings,
Briggs66 proposed a modification of this rule in which building
downwash was assumed not to occur if the stack height was greater
than the sum of the building height and 1.5 times the minimum of
the building height and width.
In the use of COMPDEP for the mercury assessment, building
downwash was considered if the plume height, calculated from the
sum of the stack height and the distance-dependent plume rise at
a distance of two building heights, was greater than either
(a) 2.5 times the building height, or (b) the sum of the building
height and 1.5 times the building width. If wake effects were
predicted, then the effective stack height was reduced by
reducing the estimated plume rise. First a distance-dependent
plume rise is estimated based on momentum-dominated conditions.53
max
1/3
1/3
Unstable or neutral
Stable
The effective stack height was then set to the maximum of this
value and the distance-dependent buoyancy-dominated effective
stack height:
=h
, 1.60
,2/3
U
M-35
-------
The dispersion parameters were also modified based on the
dimensions of the building. These modifications are based on
Huber and Snyder building downwash procedures70-71 and are
principally based on the results of wind-tunnel experiments using
a model building with a crosswind double that of the building
height. Because the atmospheric turbulence simulated in the
wind-tunnel experiments was intermediate (between a slightly
unstable Pasquill C category and neutral D) , the data upon which
the formulas were based reflect a specific stability, building
shape, and orientation with respect to the mean wind direction.72
The basic idea was to estimate modified lateral and vertical
dispersion parameters, and then use the minimum of these and the
dispersion parameters estimated without wake effects. In
general, the ratio of the building width to building height plays
a key role.
Setting
0.35^ + 0.067(x-3h2) if
if
where hm is the minimum of the building width and height, and
and h2 are given by
if v V5
else
and
if wb
else
and xy is the lateral virtual distance, given by
where the coefficients p and g depend on the stability class, and
the coefficients cw and ch are given by:
M-36
-------
Cv = 1
0.85 if
0.35 if
0 if V V5
and
o if v vi
0.5 if
0.85 if
The virtual source location was calculated by requiring that
By' (10 h,) = .35 hj, + 0.5 h2.13
Table M-14 presents the coefficients used to calculate
lateral virtual distances.
The vertical dispersion-term is modified similarly.
0.7hm + 0.067(x-3hm)
az(x+xz)
if 3hmsx<10ha
if
where h^ is the minimum of the building width and height, and xz
is the vertical virtual distance, given by
where the coefficients a and b are given in Table M-ll above. The
virtual source location xz is calculated by requiring that s ' (10
hj = 1.2 h
74
and is added in order to account for the enhanced
initial plume growth caused by the building wake.
75
M.2.1.8 Buoyancy-Induced Dispersion. It has been observed
that the initial dispersion of plumes may be augmented by
turbulent motion of the plume and turbulent entrainment of
ambient air. This is addressed by increasing the calculated
standard deviations in the crosswind and vertical directions
using the method of Pasquill:76
M-37
-------
Table H-14. Coefficients Used to Calculate Lateral Virtual
Distances for Pasquill Dispersion Rates
Pasquill Stability Class
A
B
C
D
E
F
P
209.14
1 54.46
103.26
68.26
51.06
33.92
q
0.890
0.902
0.917
0.919
0.921
0.919
1/2
1/2
where, sye = the effective standard deviation of lateral
concentration distributions (m) for
buoyancy-induced dispersion
oze = the effective standard deviation of vertical
concentration distributions (m) for
buoyancy-induced dispersion
sy = the standard deviation of lateral
concentration distributions (m) without
buoyancy effects
sz = the standard deviation of vertical
concentration distributions (m) without
buoyancy effects,
D.h = the estimated plume rise (m) .
M.2.1.9 Meteorological Data. COMPDEP uses hourly
meteorological data to estimate hourly concentrations and
deposition rates. If wet deposition is not to be modeled, then
the only data file required is one containing hourly values for
average wind speed, wind direction, Pasguill stability class,
mixing height, and ambient air temperature.
If wet deposition is to be modeled, then a data file
containing a summary of the hourly precipitation intensities and
fraction of hour for which precipitation occurred is also
M-38
-------
required. COMPDEP only considers four precipitation intensity
classes. These are summarized in Table M-15.
M.2.2 Application of the COMPDEP Model for the Exposure
Assessment
To estimate local mercury concentrations in environmental
media, hypothetical sources (model plants) were designed using
available information. Each model plant/control scenario/
emission speciation estimate was placed in the hypothetical
locations. In this section modifications made to the COMPDEP
model for this exposure assessment, as well as parameter values
used are discussed.
M.2.2.1 Modifications of COMPDEP for the Exposure
Assessment. Several modifications were made to COMPDEP in order
to address more effectively the atmospheric deposition of mercury
species. These modifications were necessary, because mercury
exists primarily in the vapor phase, and the previous version of
COMPDEP (version 93340) could not estimate deposition for vapor.
Specification of Vapor phase/Particle-Bound Phase Ratio.
This modification consisted of adjusting COMPDEP to allow the
user to specify the fractions of the emissions of a particular
pollutant that are in vapor phase and particle-bound phase. The
modification was necessary because the transport properties of
the two phases can be quite different and the forms of mercury
considered in this report are primarily in the vapor phase.
Dry Deposition of Vapor Phase Contaminants. The mercury
species assumed to be emitted (elemental and divalent mercury)
are predominantly in the vapor phase; however, the algorithms in
COMPDEP for calculating deposition velocities can only be used
for particles. For this reason, COMPDEP was modified so that,
for the vapor phase fraction of a pollutant, the user can specify
atmospheric stability class-dependent deposition velocities.
These were used in the concentration algorithms with plume
depletion, with the gravitational settling velocity set to zero
as is recommended for gases.34 Section M.2.2.3 gives a
description of the deposition velocities used in the exposure
assessment.
Wet Deposition of Vapor Phase Contaminants. To estimate wet
deposition COMPDEP requires a scavenging coefficient for each
pollutant. This scavenging coefficient can depend on the
precipitation intensity and particle size. No values are given
for gases, for which the scavenging coefficient will depend
strongly on the chemical properties of the pollutant in the vapor
phase (e.g., solubility).
M-39
-------
Table M-15. Precipitation Intensities Considered by COMPDEP
Intensity Class
0
1
2
3
Precipitation Rate (in/hr)
0
trace to 0.10
0.11 to 0.30
greater than 0.30
Modifications were made to COMPDEP to enable the user to
specify a unitless washout ratio W for the vapor phase portion of
the pollutant. The washout ratio is the ratio of the
concentration in air to the concentration in precipitation.
Connection Between the Washout Ratio and Scavenging
Coefficient. By definition, the washout ratio W is the ratio of
the concentration in surface-level precipitation to the
concentration in surface level air.63 Let Cw and Ca denote the
concentrations in surface-level precipitation and surface-level
air, respectively (units of g/m3) . Then
cw
W = —-
Using the scavenging coefficient to estimate air concentration
during periods of precipitation, the concentration in air is
given by this equation:
= c
(x,y,zs)
where, zs = height of the receptor (m)
x = downwind distance to the receptor
y = crosswind distances to the receptor (m),
t = travel time (seconds) to the receptor
L = scavenging coefficient (units of inverse seconds)
M-40
-------
The concentration in precipitation can be approximated by the wet
deposition flux divided by the precipitation rate for the time
period:
H
~ht
A c (x,y, z) e~ht dz
C =
where, H = height from which the precipitation falls (m)
and
P = precipitation rate (m/s).
The washout ratio is then given by these formulae:
H H
/, z) e'A t dz A Jc (x,y, z) dz (A H) (1/H) fc (x,y, z) dz
z, z.
y,zs) e"A c p c (x,y, zs) P C (x,y, zs)
where Overline{C('x/yJ)} is the vertically averaged air
concentration at (x,yj . Assuming that Overline{Cfx/yj} is
approximately equal to CCx,y, zs), and that the height H is the
mixing height HL, the equation reduces to
A HL
and conversely
A =
Because only the intensity classes (0-3) of the
precipitation were assumed to be in the precipitation data file,
the user must specify the representative precipitation rate for
each intensity class.
For a discussion of the washout ratios used in this study
see M.2.2.3.
M.I.2.2 Meteorological Data and Receptor Locations Relative
to Local Source. For both of the locations, meteorological data
M-41
-------
were obtained for one year (1989). The types of data files and
their use are described in Table M-16.
M.2.2.3 Vapor Deposition Parameters Dry Deposition
Velocities. The dry deposition velocities for divalent vapor
were based on those used by the RELMAP model and were estimated
based on assumed similar deposition properties of nitric acid.
Deposition velocities depend on the season, land use, time of
day, and stability class. Table M-17 shows the
seasonally-averaged deposition velocities as a function of
land-use. Dry deposition rate is proportional to the dry
deposition velocities. In order to address the fact that
deposition is lower during nighttime conditions, it was assumed
that the deposition velocity for divalent vapor was 0.3 cm/s for
stability classes D-F, which occur primarily at night, and 1 cm/s
for stability classes A-C.
Washout Ratios. For this assessment, it was assumed that
both elemental and divalent mercury species would be deposited
via wet deposition. Because of its higher solubility, the
divalent form would be washed out at significantly higher rates.
The washout ratio is a function of the concentration, total
carbon, and ozone air distribution. For divalent vapor, a
washout ratio of 1.6xl06 was used, while for the elemental phase
a value of 1.6xl04 was used; this was roughly the average of the
washout ratio for elemental vapor for both locations (see Section
M.I).
In order to calculate the scavenging coefficient, the
precipitation rate for the hour is required. In the
precipitation data file, the precipitation rate is classified as
either none, light (trace to 0.1 in/hr), moderate (0.11 to 0.3
in/hr), or heavy (greater than 0.3 in/hr). Thus, a
representative precipitation rate is required and is specified by
the version of COMPDEP modified for this assessment. For the
light and moderate categories, the midpoint of the range was
used, while for the heavy category the representative rate was
assumed equal to 0.3 in/hr.
An informal examination of the scavenging coefficients
computed for divalent vapor (using the representative rates
discussed above) showed that they were in the same range as the
upper end scavenging coefficients for particles as estimated by
PEI and Cramer.64 Table M-16 presents divalent mercury vapor
deposition velocities. It was assumed that the elemental mercury
vapor has a dry deposition velocity of zero.
M-42
-------
Table M-16. Description of Meteorological Files Used to Make
Input Files for COMPDEP
Data File
Hourly surface observations (CD144)
Mixing Height Data file
Use
Used by RAMMET program to create meteorological data file.
Used by ORNL precipitation preprocessor to create precipitation
data file for wet deposition calculations.
Used by RAMMET to create meteorological data file.
Table M-17. Divalent Mercury Vapor Seasonally-Averaged
Deposition Velocities (cm/a)
Land-use
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed forest/wetland
Water
Barren Land
Non-forested wetland
Mixed agric./rangeland
Rocky open areas
Pasquill Stability Class
A
4.63
1.81
1.71
3.45
3.45
3.32
1.00
1.07
1.92
1.76
1.94
B
4.59
1.77
1.68
3.40
3.40
3.27
0.98
1.06
1.89
1.73
1.90
C
4.36
1.63
1.54
3.14
3.14
3.06
0.89
0.97
1.77
1.59
1.74
D
4.03
1.42
1.34
2.80
.2.80
2.77
0.77
0.85
1.59
1.39
1.51
E
2.46
0.61
0.56
1.39
1.39
1.54
0.31
0.31
0.85
0.58
0.59
F
0.36
0.20
0.19
0.29
0.29
0.29
0.13
0.18
0.21
0.19
0.20
M.2.2.4 Other Parameters Used in Air Modeling. This
section presents the values for all parameters not already
discussed that were used in the air modeling for all of the model
plants. These are given in Tables M-18 to M-20.
M.3 Description of the IEM2 Indirect Exposure Methodology
Atmospheric mercury concentrations and deposition rates
estimated from RELMAP and COMPDEP drive the calculations of
mercury in watershed soils and surface waters. The soil and
water concentrations, in turn, drive calculations of
concentrations in the associated biota and fish, which humans and
other animals are assumed to consume. These "indirect" exposure
M-43
-------
Table M-18. Air Modeling Parameter Values Used In the Exposure
Assessment: Generic Parameters
Parameter
Particle Density (g/cm3)
Surface Roughness Length (m)'
Anemometer Height (m)
Wind Speed Profile Exponents
Stability Class A
Stability Class B
Stability Class C
Stability Class D
Stability Class E
Stability Class F
Terrain Adjustment Factors
Stability Class A
Stability Class 6
Stability Class C
Stability Class D
Stability Class E
Stability Class F
Distance Limit for Plume Centerline (m)
Model Run Options
Terrain Adjustment
Stack-tip Downwash
Building Wake Effects
Transitional Plume Rise
Buoyancy-induced dispersion
Calms Processing Option
Value Used in Exposure
Assessment
1.8
0.30
10
0.07
0.07
0.10
0.15
0.35
0.55
0.5
0.5
0.5
0.5
0
0
10
Yes
No
No
Yes
Yes
No
1 This is used to estimate deposition velocities for particles.
M-44
-------
Table M-19. Model Plant Parameter Values Used in COMPDEP
Plant Type
Large Municipal Waste
Combustor
Small Municipal Waste
Combustor
Continuous Medical Waste
Incinerator
Intermittent Medical Waste
Incinerator
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali Plant
Primary Copper Smelter
Primary Lead Smelter
Stack
Height (ft)
230
140
40
40
732
465
266
290
10
505
350
Stack
Diameter
(ft)
10
5
3
1
27
18
12
14
1
15
20
Stack Exit
Temperature
(F)
285
375
1500
1500
273
275
295
322
Ambient*
430
347
Stack Exit
Velocity (m/s)
21.9
21.9
7.3
7.3
31.1
26.7
6.6
20.7
0.1
6
2.80
Total
Mercury
Emission
Rate
(kg/yr)
1330
170
80
2.4
230
90
10
2
380
536
2680
" Average annual temperature was used (see Appendix B).
Table M-20. Model Plant Mercury Speciation of Emissions
Plant Type
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Speciation of Emissions
% Elemental
Mercury
Vapor
50
50
50
50
% Divalent
Mercury
Vapor
30
30
30
30
% Divalent
Paniculate
20
20
20
20
Speciation of Emissions: "Error-
Bounding Estimate"
%
Elemental
Mercury
Vapor
85.00
85.00
85.00
85.00
% Divalent
Mercury
Vapor
15.00
15.00
15.00
15.00
% Divalent
Particulate
0.00
0.00
0.00
0.00
M-45
-------
calculations were modified from the Indirect Exposure Document77
as updated in an Addendum (in preparation). Relevant sections of
the updated methodology, IEM2, are described below. The
equations were implemented in a spreadsheet and parameterized.
IEM2 uses atmospheric chemical loadings to perform mass
balances on a watershed soil element and a surface water element,
as illustrated in Figure M-l. The mass balances were performed
for total mercury, which was assumed to speciate into three
components: Hg°, Hg(II), and methylmercury. The fraction of
mercury in each of these components was specified for the soil
and the surface water elements. Loadings and chemical properties
were given for the individual mercury components, and the overall
mercury transport and loss rates are calculated by the
methodology.
IEM2 first performs a terrestrial mass balance to obtain
mercury concentrations in watershed soils. Soil concentrations
were used along with vapor concentrations and deposition rates to
calculate concentrations in various food plants. These were
used, in turn, to calculate concentrations in animals. IEM2 next
performs an aquatic mass balance driven by direct atmospheric
deposition along with runoff and erosion loads from wateshed
soils. Methylmercury concentrations in fish were derived from
total dissolved water concentrations using bioaccumulation
factors (BAF).
IEM2 was developed to handle individual chemicals, or
chemicals linked by kinetic transformation reactions. The
kinetic transformation rates affecting mercury components in
soil, water, and sediments — oxidation, reduction, methylation,
and demethylation — were considered too uncertain to implement in
this study. For this study, the methodology was expanded to
handle multiple chemical components in a steady-state
relationship. The fraction of each chemical component in the
soil and water column was specified by the user. The methodology
predicts the total chemical concentration in watershed soils and
the water body based on loading and dissipation rates specified
for each of the components.
The nature of this methodology is basically steady with
respect to time and homogeneous with respect to space. While it
tracks the buildup of watershed soil concentrations over the
years given a steady depositional load and long-term average
hydrological behavior, it does not respond to unsteady loading or
meteorological events. There are, thus, limitations on the
analysis and interpretations imposed by these simplifications.
M-46
-------
Figure M-l. Overview of the ZEM2 Watershed Modules
Dry
Advection
Water column
benthic
transformation
Burial
Definitions for Figure M-1
chemical concentration in upper soil
chemical concentration in water body
vapor phase chemical concentration in air //g/m3
average dry deposition to watershed
average wet deposition to watershed
mg/L
mg/L
mg/yr
mg/yr
M-47
-------
The methodology cannot be used to predict the response to
reduction or elimination of loadings . The model ' s calculations
of average water body concentrations are less reliable for
unsteady environments, such as streams, than for more steady
environments , such as lakes .
M.3.1 The Terrestrial Equations
The IEM2 framework for estimating watershed soil impacts
from stack emissions calculates surface soil concentrations,
including dissolved and sorbed phases, as illustrated in Figure
M-2 . The model accounts for three routes of contaminant entry
into the soil: deposition of particle-bound contaminant through
dryfall; deposition through wetfall; and diffusion of vapor phase
contaminant into the soil surface. The model also accounts for
five dissipation processes that remove contaminants from the
surface soils: decay of total contaminants (sorbed + dissolved)
within the soil horizon; volatilization (diffusion of gas phase
out of the soil surface) ; runoff of dissolved phase from the soil
surface; leaching of the dissolved phase through the soil
horizon; and erosion of particulate phase from the soil surface.
Key assumptions in the watershed soil impact algorithm were
these:
• Soil concentrations within a depositional area are
assumed to be uniform within the area, and can be
estimated by the following key parameters: dry and wet
contaminant deposition rates, a wind-driven gaseous
exchange rate with the atmosphere, a soil dissipation
rate, a soil bulk density, and a soil mixing depth.
• The partitioning of the contaminant within the
soil/water matrices can be described by partition
coefficients.
M.3.1.1 Chemical Mass Balance in Watershed Soils. A mass
balance equation can be written for total mercury in watershed
soils, balancing areal deposition fluxes with chemical loss
processes:
Sc = - w- — (1 - e~ksTc) 100 + Cch
ks Z BD sb
M-48
-------
Figure M-2. Overview of the IEM2 Soils Processes
Background
A
atm
Q
yws
Volatilization
Diffusion
v
Dry Fall
v
Wet Fall
v
^HRT
Runoff
=>
C'
Erosion
=>
Transformation
st
PS
V
Leaching
cs,
H
R
T
Definitions for Figure M-2
vapor phase chemical concentration in air /jg/m3
average dry deposition to watershed
average wet deposition to watershed
total chemical concentration in soil
reaction product concentration in soil
background chemical concentration in soil mg/L
chemical concentration in soil gas
chemical concentration in soil water
chemical concentration on soil particles
Henry's Law constant
universal gas constant
temperature
soil/water partition coefficient
mg/yr
mg/yr
mg/L
mg/L
//g/m3
mg/L
atm-m3/mole
atm-m3/mole-°K
°K
L/kg
M-49
-------
where: Sc = average watershed soil concentration after
time period of deposition (ug pollutant /g
soil)
LW = yearly average load of pollutant to watershed
on an areal basic (g pollutant/m2-yr)
ks = total chemical loss rate constant from soil
(yr-1)
Tc = total time period over which deposition has
occurred (yr)
Z = representative watershed mixing depth to
which deposited pollutant is incorporated
(cm)
BD = representative watershed soil bulk density
(g/cm3)
100 = units conversion factor (ug-m2/g-cm2)
Csb = background "natural" soil concentration (ug
pollutant/g soil)
The first term in the equation represents the steady-state
concentration achieved after a sufficient period of constant
loading. The exponential term gives the fraction of the
steady-state response achieved after Tc years of loading. The
final term gives the natural background concentration found in
soils. The background soil concentration of mercury was assumed
to be negligible in this study. The major terms in this equation
are discussed in sections below.
M.3.1.2 Equilibrium Speciation in Watershed Soils. Total
mercury in the soil was assumed to be distributed among three
components — Hg°, Hg(II), and methylmercury . The steady-state
fraction of the total in each component is specified by the user,
so that:
ScHg0 = Sc • fsl
= SC
= SC ' fs3
where:
Sc = soil concentration of total mercury (ug
pollutant/g soil)
ScHg0 = soil concentration of elemental mercury
pollutant/g soil)
M-50
-------
= soil concentration of divalent mercury (ug
pollutant/g soil)
ScMeHg = soil concentration of methylmercury (ug
pollutant /g soil)
fsl = fraction of soil concentration that is
elemental mercury
fs2 = fraction of soil concentration that is
divalent mercury
fs3 = fraction of soil concentration that is
methylmercury
The total concentration of each mercury component in soil
was assumed to reach equilibrium between its particulate and
aqueous phases according to the following equations:
C = SC BD
Sc. Kd . BD
C. ~ 1 s'2
pSl1 0 + Kd . BD
S S , 2.
Sc. BD
6 + Kd . BD
S S / J.
Where:
SCi = total soil concentration of component "i" (ug/g)
6S = volumetric soil water content (L^ter/M
Kdsi = soil/water partition coefficient for component "i"
(L/kg)
BD = soil bulk density (g/cm3)
Cstii = total soil concentration of component "i" (mg/L)
Cds,i = concentration of "i" dissolved in pore water
(mg/L)
CpS,i = concentration of "i" in particulate phase (mg/kg)
A derivation of the soil equilibrium equations is given in
Section M.S.3.2.
M.3.1.3 Loads to Watershed Soils. The total pollutant load
term Lw in the mass balance equation is the sum of the loadings
for each component "i." Component loadings include wetfall and
M-51
-------
dryfall fluxes, atmospheric diffusion fluxes and internal
transformation loads:
LWii = Dydw.
where :
= yearly average dry depositional flux of
component "i" (g/m2-yr)
= yearly average wet depositional flux of
component "i" (g/m2-yr)
LISii = internal transformation load of component "i" per
areal basis (g/m2-yr)
LDiF,i = atmospheric diffusion flux of component "i" to
soil (g/m2-yr)
Internal transformation loads are set to 0 in the equilibrium
component approach. Wet and dry depositional fluxes were
determined by measurement or by air modeling and were specified
as input to this model. The load due to vapor diffusion is given
as the following:
LDiF,i = 0.31536 Kti.
where :
LDIF,I = atmospheric diffusion flux of component "i" to
soil (g/m2-yr)
Ktii = gas phase mass transfer coefficient for component
"i" (cm/s; see Eq [4-6], IED)
Catm i =
-------
The total chemical loss rate constant for component
several physical and chemical processes:
i" is due to
ksri
ksgi
ksvi
where:
soil loss constant for component "i" due to all
processes (yr"1)
soil loss constant due to leaching (yr"1)
soil loss constant due to erosion (yr"1)
soil loss constant due to runoff (yr"1)
soil loss constant due to chemical transformation/
degradation (yr"1)
soil loss constant due to volatilization (yr"1)
The degradation constant, ksgi, i-s set to 0 in tne equilibrium
component approach. The other four constants are given by these
equations:
ksli =
P + I - Ro - EV
1.0
BD/6
0.1 X SD ER
kse. = e-
a ED Z
Kds d BD
e
7
ksr. =
ezi+Kd. BD/e
ksvi = Kei Kti
where:
P
I
Ro
Ev
es
Z
BD
SD
ER
Kde
average annual precipitation (cm/yr)
average annual irrigation (cm/yr)
average annual runoff (cm/yr)
average annual evapotranspiration (cm/yr)
volumetric water content (dimensionless; cm3/cm3)
watershed mixing zone depth (cm)
soil bulk density (g/cm3)
sediment delivery ratio
contaminant enrichment ratio
soil-water partition coefficient for component "
(cm3/g)
M-53
-------
Xe = unit soil loss (kg/m2-yr; see Eq [9-3]78)
Kei = equilibrium coefficient for component "i" (s/cm-
yr); see Eq [4-5]79)
Kti = gas phase mass transfer coefficient for component
11 i" (cm/s; see Eq [4-6]79)
0.1 = units conversion factor
Sc is the concentration resulting from contaminated
particles depositing on and mixing with surface soils. For
mercury components, where Kds/i values are large, SCi was
essentially equal to the sorbed concentration, Cps/i, and the
dissolved phase concentration, CdSii, was small. Mercury
components depositing as particles were assumed to reequilibrate
in the soil/soil water system (see the state equations above).
In the listing of state equations, the reequilibrated sorbed
phase concentration, Cps/i, and the dissolved phase concentration,
Cds.i' were used to estimate loads to the water body due to soil
erosion and surface runoff, respectively.
M.3.2 The Aquatic Equations.
The following framework for estimating surface water impacts
from stack emissions estimates water column as well as bed
sediment concentrations. Water column concentrations included
dissolved, sorbed to suspended sediments and total (sorbed plus
dissolved, or total contaminant divided by total water volume).
This framework also provides three concentrations for the bed
sediments: dissolved in pore water, sorbed to bed sediments, and
total. The model accounts for five routes of contaminant entry
into the water body: erosion of chemical sorbed to soil
particles; runoff of dissolved chemical in runoff water;
deposition of particle-bound contaminant through wetfall and
dryfall; and diffusion of vapor phase contaminants into the water
body. The model also accounts for four dissipation processes
that remove contaminants from the water column and/or bed
sediment reservoirs: decay of total contaminants (sorbed +
dissolved) within the water column; decay of total contaminants
(sorbed + dissolved) within the bed sediment; volatilization of
dissolved phase out of the water column; and removal of total
contaminant via "burial" from the surficial bed sediment layer.
This burial rate constant is a function of the deposition of
sediments from the water column to the bed; it accounts for the
fact that much of the soil eroding into a water body annually
becomes bottom sediment rather than suspended sediment. The
impact to the water body was assumed to be uniform. This tends
to be more realistic for smaller water bodies as compared to
large river systems. Key assumptions in the surface water impact
algorithm are the following.
M-54
-------
The partitioning of the contaminant within the
sediment/water matrices - suspended solids in the water
column, and bed sediments in the benthos of the water
body - can be described by partition coefficients.
One route of entry into the surface water body is
direct deposition. A second route of entry is
contaminant dissolved in annual surface runoff. This
is estimated as a function of the contaminant dissolved
in soil water and annual water runoff. A third route
of entry is via soil erosion. A sorbed concentration
of contaminant in soil, together with an annual soil
erosion estimate, a sediment delivery ratio and an
enrichment ratio, can be used to describe the delivery
of contaminant to the water body via soil erosion. A
sediment delivery ratio serves to reduce the total
potential amount of soil erosion (where the total
potential equals a unit erosion rate in kg/m2
multiplied by the watershed area, in m2) reaching the
water body recognizing that most of the erosion within
a watershed during a year deposits prior to reaching
the water body. The enrichment ratio accounts for the
fact that eroding soils tend to be lighter in texture,
be more abundant in surface area, and have higher
organic carbon. All these characteristics lead to
concentrations in eroded soils that tend to be higher
in concentration as compared to in situ soils. A
fourth and final route of entry is via diffusion in the
gaseous phase. The dissolved concentration in a water
body is driven toward equilibrium with the vapor phase
concentration above the water body. At equilibrium,
gaseous diffusion into the water body is matched by
volatilization out of the water body. Gaseous
diffusion is estimated with a transfer rate (determined
internally given user inputs) and a vapor phase air
concentration. This air concentration is specified by
the user and is an output of the atmospheric transport
model.
For the surface water solution algorithm, it is assumed
that equilibrium is maintained between contaminants
within the water column and contaminants in surficial
bed sediments. Equilibrium is established when the
dissolved phase concentration in the water column is
equal to the dissolved phase concentration within the
surficial bed sediments. This condition is imposed by
the water body equations.
M-55
-------
• A rate of contaminant "burial" in bed sediments is
estimated as a function of the rate at which sediments
deposit from the water column onto the surficial
sediment layer. This burial represents a permanent
sink, recognizing that a portion of the eroded soil and
sorbed contaminant becomes bottom sediment while the
remainder becomes suspended sediment. This solution
assumes that there will be a net depositional loss,
even though resuspension and redeposition of sediments
is ongoing, particularly with moving water bodies. For
cases where the net deposition rate is zero, there will
be no burial loss calculated.
• Separate water column and benthic decay rate constants
allow for the consideration of decay mechanisms that
remove contaminants from the water body, optionally
linking them through internal loading to a reaction
product. For the equilibrium component approach to
mercury, decay constants are set to 0.
Figure M-3 displays the framework for this analysis, with a
listing of the ten concentrations that were part of the solution
algorithm. In the following sections, the mass balance equations
and the equilibrium state equations that link the concentrations
are developed.
M.3.2.1 Chemical Mass Balance in the Water Body. Taking
Figure M-4 as a control volume for the water body, it can be seen
that a steady-state mass balance equation can be written that
balances chemical loadings with outflow and loss:
where:
Cwtot = total water body concentration, including water
column and benthic sediment (mg/L)
LT = total chemical load into water body, including
deposition, runoff, erosion, atmospheric
diffusion, and internal chemical transformation
(g/yr)
Vfx = average volumetric flow rate through water body
(mVyr)
M-56
-------
Figure M-3. Overview of the IEM2 Water Body Processes
c
>-*JJE i ::, •»
V Rur
c ,
^ps ' 1
Era
Transfc
Transfc
lOff "
sion
rmationXx^
i
c
rmationx?
C'tt
A
Volatilization
IT °
Dry Fall
Wet Fall
JV V V
r* KJSW ^^ T<
f\
'i
***
«t y
3t
c
dw ^ ^sw
Exchange
It
, II
db ^*»
~sb
Burial
J
R(
n
5S
i
3
Advection
Water
Column
Benthic
Sediment
<7
Burial
Definitions for Figure M-3
Cd£ concentration dissolved in soil water
Cps concentration sorbed to soil
Dybs yearly dry deposition to surface water
Dyws yearly wet deposition to surface water
C,tm vapor phase atmospheric concentration
Cm total concentration in water column
€„,„, total water concentration in surface
water system, including water column
plus benthic sediment (not shown in figure)
Cdw dissolved phase water concentration
Csv, sorbed phase water concentration
Cbt total concentration in bottom sediment
Cdb concentration dissolved in bed sediment pore water
Csb concentration sorbed to bottom sediments
mg/L
mg/kg
mg/yr
mg/yr
fjg/m3
mg/L
mg/L
mg/L
mg/kg
mg/L
mg/L
mg/kg
M-57
-------
Figure M-4. IEM2 Steady State Sediment Balance in Water Bodies
W
erosion
advection
TSS
(W )
1 dep '
X
BS
Definitions for Figure M-4
Xw soil erosion input from depositional area
X, advective loss from water body
Xd deposition onto bottom sediment
Xb burial below bottom sediment layer
TSS suspended solids concentration
BS bottom sediments concentration
Wdep rate of deposition onto bed sediment
Wb rate of burial
g/yr
g/yr
g/yr
g/yr
mg/L
g/L
m/yr
m/yr
M-58
-------
Vz = total volume of water body or water body segment
being considered, including water column and
benthic sediment (m3)
= total first order dissipation rate constant,
including water column and benthic degradation,
volatilization, and burial (yr'1)
= fraction of total water body contaminant
concentration that occurs in the water column
dj, = depth of the water column (m)
dz = total depth of water body, <^,+db (m)
The first term in the denominator accounts for the advective flow
of chemical from the water column, while the second term accounts
for loss processes from the bed and water column. This mass
balance equation is derived in Section M.3.3.3. The terms in
this equation are discussed in sections below.
M.3.2.2 Sediment Mass Balance in the Water Body. Before
calculating chemical fate, a mass balance equation for sediments
in the water body must be solved. Taking Figure M-4 as a control
volume for the water body, it can be seen that a steady-state
mass balance equation can be written that balances sediment
loadings with outflow and loss:
Xo WAT SD 103
TSS = e L
where:
TSS = suspended solids concentration (mg/L)
Xe = unit soil erosion flux, calculated in the soils
section from the USLE equation (kg/m2-yr)
WAL = watershed surface area (m2)
SD = watershed sediment delivery ratio (unitless)
Vfx = average volumetric flow rate through water body
(mVyr)
Wdep = suspended solids deposition rate (m/yr)
WA,, = water body surface area (m2)
103 = units conversion factor
The first term in the denominator accounts for the advective flow
of sediment from the water column, while the second term accounts
for depositional loss from the water column. This mass balance
equation is derived in Section M.3.3.3. The terms in this
equation are discussed in sections below.
M-59
-------
In the second part of the solids balance, the mass deposited
to the bed, X^, was set equal to the mass buried, Xb. Solving
for the burial rate gives the following:
TSS IP'6
b deP BS
where :
Wb = burial rate (m/yr)
wdep = deposition rate (m/yr)
TSS = suspended solids concentration (mg/L)
BS = benthic solids concentration (kg/L)
10"6 = conversion factor (kg/mg)
Finally, the benthic porosity, the volume of water per
volume of benthic space, was calculated from the benthic solids
concentration and sediment density:
where :
6bs = benthic porosity (L/L)
BS = benthic solids concentration (kg/L)
ps = solids density, 2.65 kg/L
For input benthic solids concentrations between 0.5 and 1.5 kg/L,
benthic porosity ranged between 0.8 and 0.4.
The suspended solids, benthic solids, and benthic porosity
were used in the chemical equilibrium speciation equations. The
burial rate was used in the chemical burial equation. These
equations are developed below.
M.3.2.3 Equilibrium Speciation in Water Body. Total
mercury in the water body is assumed to be distributed among
three components — Hg°, Hg(II), and methylmercury . The
steady-state fraction of the total in each component in the water
column is specified by the user using the following relationship:
c = c • f
^wt.Hg(II) ^~wt v2
= c • f
^wt w3
M-60
-------
where :
Cwt = water column concentration of total mercury
(ug/L)
C«t,Hgo = water column concentration of elemental
mercury (ug/L)
cwt,Hg(n> = water column concentration of divalent
mercury (ug/L)
cwt MeHg = water column concentration of methylmercury
(ug/L)
fwl = fraction of water column concentration that
is elemental mercury
fw2 = fraction of water column concentration that
is divalent mercury
fw3 = fraction of water column concentration that
is methylmercury
The total concentration of each mercury component in the
water body, Cwtot>i, was assumed to reach equilibrium between the
benthic and water column compartments and between its particulate
and aqueous phases within each compartment. Cwtot>i gives the mass
per volume of the entire water body, including both water column
and benthic sediment. The water column concentration, Cwt>i, was
based on the water column volume; the benthic concentration,
cbt,i/ was based on the benthic volume. The equilibrium
relationships are given by the following equations, which are
derived in Section M.3.3.4.
Surface Water System —
= see mass balance equation
water,i
* benth.i
Water Colximn —
wt,i water,! wtoc,i z' w
M-61
-------
= c • f
i ^wt.i ^dw.i
1-6
C — Kd ' C
sw, i sw, i dw, i
Bed Sediment —
'
Q
bs
Csb,i - Kdbs.i'Cdb,i
Note that by substituting the relationship between Cwt,i and
into the expression for Cbtii, one can obtain benthic
concentrations as a function of water column concentrations
= C
^
t, i
»-6
= C
^~
where:
ebs
Kd
•sw, i
Kd,
•bs,i
TSS
BS
bed sediment porosity
= suspended sediment/surface water partition
coefficient for component "i" (L/kg)
= bottom sediment/pore water partition
coefficient for component "i" (L/kg)
total suspended solids (mg/L)
bed sediment concentration (g/cm3)
depth of the water column (m)
depth of the upper benthic layer (m)
total depth of water body, dw+dj., (m)
M-62
-------
fwater,i = fraction of total water body component "i"
concentration that occurs in the water column
fbenth,i = fraction of total water body component "i"
concentration that occurs in the bed sediment
fi = soil erosion load (g/yr)
LDif,i = diffusion of vapor phase component "i" (g/yr)
LI;i = internal transformation load, equal to 0 for
equilibrium mercury chemistry (g/yr)
The runoff and erosion loads required estimation of average
contaminant concentration in watershed soils that comprise the
depositional area. These concentrations were developed in
terrestrial sections above.
Load due to direct deposition — The load to surface waters via
direct deposition is solved as follows:
k
where:
Loep,i = direct component "i" deposition load (g/yr)
= yearly dry deposition rate of component "i"
onto surface water,body (g pollutant/m2-yr)
= yearly wet deposition rate of component "i"
onto surface water body (g pollutant/m2-yr)
= water body area (m2)
M-63
-------
Load due to impervious surface runoff — A fraction of the wet and
dry chemical deposition in the watershed will be to impervious
surfaces. Dry deposition may accumulate and be washed off during
rain events. If the impervious surface includes gutters, the
pollutant load will be transported to surface waters, bypassing
the watershed soils. The average load from such impervious
surfaces is given by this equation:
RI,i
WA
where :
WAT
D.
yww,
Jydw,
impervious surface runoff load for component "i"
(g/yr)
impervious watershed area receiving pollutant
deposition (m2)
yearly wet deposition flux of component "i" onto
the watershed (g/m2-yr)
yearly dry deposition flux of component "i" onto
the watershed (g/m2-yr)
Load due to pervious surface runoff — Most of the chemical
deposition to a watershed will be to pervious soil surfaces.
These loads are accounted for in the soil mass balance equation.
During periodic runoff events, dissolved chemical concentrations
in the soil are transported to surface waters as given by this
equation:
= Ro
Sci BD
Qs + Kds i BD
10
-2
where :
Ro
SCi
BD
A
Kds>i
WAj
ID'2
pervious surface runoff load for component "i"
(g/yr)
average annual runoff (cm/yr)
component "i" concentration in watershed soils
(ug/g)
soil bulk density (g/cm3)
volumetric soil water content (cm3/cm3)
soil-water partition coefficient for component "i"
(L/kg or cmVg)
total watershed area receiving pollutant
deposition (m2)
impervious watershed area receiving pollutant
deposition (m2)
units conversion factor (g2/kg-ug)
M-64
-------
Load due to soil erosion — During periodic erosion events,
particulate chemical concentrations in the soil are transported
to surface waters as described by this relationship:
(WAT - WAT) SD ER
Li J.
Sc. Kds . BD
i S f 1
+ Kd . BD
S / 2,
10
-3
where:
LEii = soil erosion load for component "i" (g/yr)
Xe = unit soil loss (kg/m2-yr)
SCi = component "i" concentration in watershed soils
(ug/g)
BD = soil bulk density (g/cm3)
Qs = volumetric soil water content (cm3/cm3)
KdSii = soil-water partition coefficient for component "i"
(L/kg or cm3/g)
WAL = total watershed area receiving pollutant
deposition (m2)
WAX = impervious watershed area receiving pollutant
deposition (m2)
SD = watershed sediment delivery ratio (unitless)
ER = soil enrichment ratio (unitless)
10"3 = units conversion factor (g-cm2/ug-m2)
Load due to gaseous diffusion — The change in the total water
concentration over time due to volatilization is given by this:
3Cwtot,i i Kv,.
a £ I voJat
\
>-6
f f C
water, i dw, i wtot, i
where:
C-wtot.i = total water body component "i" concentration
(mg/L)
K^i = overall component "i" transfer rate (m/yr)
D = depth of water body (m)
f water,! = fraction of total water body component "i"
concentration that occurs in the water column
f
-------
Tk = water body temperature (°K)
10"6 = units conversion factor
This treatment of volatilization is based on the well-known
two- film theory80, as implemented in standard chemical fate
models.81-82 The right side of the volatilization equation
contains two terms. The first term constitutes a first order
loss rate of aqueous contaminant, which is covered in more detail
below. The second term in the volatilization equation describes
diffusion of gas-phase contaminant from the atmosphere into the
water body. Because this term is independent of water body
contaminant concentration, it can be treated as an external load.
As formulated above, this term has units of mg/L-yr. It must be
converted to loading units by multiplying by the water column
volume, V. Noting that V/D is equal to the surface water area
, we see that the atmospheric diffusion load is given as this:
where :
LDif.i = diffusion of vapor phase component "i" (g/yr)
K^i = the overall component "i" transfer rate (m/yr)
WA^, = surface water body area (m2)
catm,i = component "i" vapor phase air concentration over
water body (ug/m3)
Hi = component "i" Henry's Constant (atm-m3/mole)
R = universal gas constant (8.206 x 10~5
atm-m3/mole-°K)
Tk = water body temperature (°K)
10"6 = units conversion factor
M.S.. 2. 5 Advective Flow From The Water Body. The first term
in the denominator of the chemical mass balance equation accounts
for advective flow from the water body. It is the product of the
average annual volumetric flow rate, Vfx; the fraction of the
chemical in the water body that is present in the water column,
fwater/' an<^ the adjustment factor dj/d,,, which normalizes the
outflowing chemical concentration to a water column volume basis.
An impacted water body derives its annual flow from its watershed
or effective drainage area. Flow and watershed area, then, are
related, and compatible values should be specified by the user.
Given the area of drainage, one way to estimate annual flow
volume is to multiply total drainage area (in length squared
units) by a unit surface water runoff (in length per time) . The
Water Atlas of the United States83 provides maps with isolines of
M-66
-------
annual average surface water runoff, which is defined as all flow
contributions to surface water bodies, including direct runoff,
shallow interflow, and groundwater recharge. The values ranged
from 5 to 40 in/yr in various parts of the United States.
M . 3 . 2 . 6 Chemical Dissipation Within The Water Body . The
second term in the denominator of the chemical mass balance
equation accounts for dissipation within the water body. It is
the product of the water body volume, Vt, and the total first
order dissipation rate constant, k^. The water body volume, in
units of m3, together with the annual flow rate, in mVyr,
determines the average residence time of a pollutant traveling
through the water body. The residence time for Lake Erie is
about 10 years, for example, while for the larger Lake Superior
it is estimated to be 200 years. For a swiftly moving river, on
the other hand, the residence time can be on the order of hours
(1 hour = 0.00011 yr) . Larger volumes and residence times allow
the internal dissipation processes to have a larger effect on
pollutant concentration, while smaller volumes and residence
times lessen the effect. It is necessary to specify reasonable
volumes for the type of surface water body being represented. In
addition, compatible values for related water body parameters,
such as surface area, WAW must be used. The water body volume
divided by the surface area gives the average depth, which can
vary from a fraction of a meter for small streams to a few meters
for shallow reservoirs to tens of meters for deep lakes.
The total dissipation rate constant, k^, applies to the
total water body concentration, Cwtot, and is the weighted sum of
the chemical loss rate constants for each component "i":
where:
kSi = total soil loss constant for component "i" (yr'1)
fsl = fraction of soil concentration that is component
"i" (i.e., elemental, divalent, and methylmercury)
The total chemical loss rate constant for component "i" includes
processes affecting any of the chemical phases — dissolved or
sorbed in the water column or benthic sediments. Volatilization,
water column and benthic degradation, and burial are considered
in this relationship:
k = f ]<• + f lr + f Tf + f If
wt,i •'"water, i ^gw, i ^benth.i gb,i water, i v,i ^benth.i b,i
M-67
-------
where :
= overall total water body dissipation rate constant
for component "i" (yr"1)
= water column degradation or transformation rate
constant for component "i" (yr"1)
kgb,i = benthic degradation or transformation rate
constant for component "i" (yr"1)
kvfi = water column volatilization rate constant (yr"1)
kb i = benthic burial rate constant for component "i"
(yr"1)
f water,! = fraction of total water body component "i"
concentration that occurs in the water column
fbenth,i = fraction of total water body component "i"
concentration that occurs in the benthic
sediment
These processes are described below.
Chemical /Biological Degradation — Contaminants can be degraded
and transformed by a number of processes in the water column or
in the benthic sediment. Mercury components are subject to
oxidation, reduction, and methylation. In the equilibrium
approach taken here, the transformation rates were set to 0 and
the fraction of total chemical in each component was specified
directly.
Volatilization — Volatile chemicals can move between the water
column and the overlying air, as described by Equation (3-41) .
The right side of this equation contains two terms. The second
term describes diffusion into the water from the atmosphere and
was treated as an external load. The first term,
(Kv,ifWater,ifdw,iCwtot,i/D) , constitutes a first order loss rate of
aqueous contaminant. This term includes the quantity
f water, if dw,iCwtot,i' which is equal to the water column dissolved
phase concentration CdWii and which is subject to volatilization
loss. The rate constant for volatilization from the water
column, k^i, is given as this:
where :
kvfi = water column volatilization loss rate constant for
component "i" (yr"1)
Kvfi = overall transfer rate, or conductivity for
component "i" (m/yr)
M-68
-------
- dw, i ~
fraction of component "i" in the water column that
is dissolved
water body depth (m)
The overall transfer rate, K^i or conductivity, was
determined by the two-layer resistance model.80'81-82 The
two-resistance method assumes that two "stagnant films" are
bounded on either side by well mixed compartments. Concentration
differences serve as the driving force for the water layer
diffusion. Pressure differences drive the diffusion for the air
layer. From mass balance considerations, it is obvious that the
same mass must pass through both films; thus, the two resistances
combine in series, so that the conductivity is the reciprocal of
the total resistance:
K
-1 _
*„
R
-i
-i
where :
R
Hi
liquid phase resistance (year/m)
liquid phase transfer coefficient (m/year)
gas phase resistance (year/m)
gas phase transfer coefficient (m/year)
universal gas constant (atm-m Vmole-°K)
Henry's law constant for component "i"
(atm-m Vmole)
water body temperature (°K)
The value of K^, the conductivity, depends on the intensity
of turbulence in a water body and in the overlying atmosphere.
As the Henry's Law coefficient increases, the conductivity tends
to be increasingly influenced by the intensity of turbulence in
water. As the Henry's Law coefficient decreases, the value of
the conductivity tends to be increasingly influenced by the
intensity of atmospheric turbulence.
Because Henry's Law coefficient generally increases with
increasing vapor pressure of a compound and generally decreases
with increasing solubility of a compound, highly volatile low
solubility compounds are most likely to exhibit mass transfer
limitations in water, and relatively nonvolatile high solubility
compounds are more likely to exhibit mass transfer limitations in
the air. Volatilization is usually of relatively less magnitude
in lakes and reservoirs than in rivers and streams.
M-69
-------
The estimated volatilization rate constant was for a nominal
temperature of 20°C. It is adjusted for the actual water
temperature using the equation:
(T-20)
where:
6 = temperature correction factor, set to 1.026.
T = water body temperature (°C)
There have been a variety of methods proposed to compute
the liquid (KL/i) and gas phase (K^) transfer coefficients. The
particular method that was used in the exposure assessment is the
0' Connor-Dobbins84 method.
The liquid and gas film transfer coefficients computed under
this option vary with the type of water body. The type of water
body was specified as one of the surface water constants and can
either be a flowing stream, river or estuary, or a stagnant pond
or lake. The primary difference is that in a flowing water body,
the turbulence is primarily a function of the stream velocity,
while for stagnant water bodies, wind shear.may dominate. The
formulations used to compute the transfer coefficients vary with
the water body type, as shown below.
Flowing Stream or River — For a flowing system, the transfer
coefficients are controlled by flow-induced turbulence. For
these systems, the liquid film transfer coefficient (KL) was
computed using the O'Connor-Dobbins84 formula:
KT , =
, „-- 1/2
10
d»
(3.15x10 7)
where:
KL/i = liquid phase transfer coefficient for component
"i" (m/year)
u = current velocity (m/s)
DWri = diffusivity of the component "i" in water (cm2/s)
dw = water depth (m)
10 "4 = units conversion factor
3.15x10 7 = units conversion factor
The gas transfer coefficient (KQ) was assumed constant at
36500 m/yr for flowing systems.
M-70
-------
Quiescent Lake or Pond — For a stagnant system, the transfer
coefficients are controlled by wind-induced turbulence. For
stagnant systems, the liquid film transfer coefficient (KL) was
computed using the O'Connor85 equations:
.15x10
= »
.0.33
A,
Sc~°i (3.15xl07
where:
u* = Cd°'5 W
V
3" Pa ^.,
D .-
= (1.32 + 0.009 T ) x 10
-i
Sc . =
W'2 - Dv,l
=
w, i
22xlO'5
M-71
-------
pw = 1 - 8.8x10
-5
log(u) =
1301
998.333 + 8.1855(T -20) + 0.00585(T -20)2
- 3.0233
and:
u
w
Pa
Pw
k
SC«
Sc,
Da,
Dw,
Pa
Uw
, i
shear velocity (m/s)
drag coefficient (= 0.0011)
wind velocity, 10 m above water surface (m/s)
density of air corresponding to the water
temperature (g/cm 3)
density of water corresponding to the water
temperature (g/cm 3)
von Karman's constant (= 0.4)
dimensionless viscous sublayer thickness (= 4)
air Schmidt number for component "i"
(dimensionless)
water Schmidt number for component "i"
(dimensionless)
diffusivity of component "i" in air (cm2/sec)
diffusivity of component "i" in water (cmVsec)
viscosity of air corresponding to the air
temperature (g/cm-s)
viscosity of water corresponding to the water
temperature (g/cm-s)
dynamic viscosity of air (cm2/sec)
molecular weight of component "i"
= air temperature (°C)
= water temperature (°C)
3.15xl07 = units conversion factor
Deposition and Burial — The benthic burial rate, Wb, was
determined as a function of user input variables as part of the
sediment balance. This burial rate is used to determine the mass
loss of contaminant from the benthic sediment layer. As seen in
Figure M.3, the burial loss rate was applied to the total benthic
contaminant concentration, Cbt. The water body contaminant
burial loss rate was solved by equating the mass loss rate of
total water body chemical with mass loss rate of benthic
chemical:
M-72
-------
w,
\r \r - r v —
i Vt Kb.i ^bt,i Vb ~^
where :
cwtot,i = total water body component "i" concentration,
including water column and benthic sediment
(mg/L)
Vt = total volume of water body or water body segment
being considered, including water column and
benthic sediment (m3)
kb
-------
- - ks-Sc
BD-Z
This is a first order, ordinary differential equation with this
general solution:
100 Lw ksf
{•*** « • s-i _ ~KS' C
oC -
ks-BD-Z x
where Cx is an unknown constant. Applying the condition Sc=0 at
t=0 gives the following:
ks-BD-Z
If a stable natural background concentration exists in the
soil that is independent of the loading and loss processes, then
this Csb can be added to the above solution to obtain the overall
concentration .
M.S. 3. 2 Terrestrial Equilibrium Relationships. Within the
soil, the dissolved chemical concentration equilibrium with the
sorbed concentration is defined by a partition coefficient:
CPS,i = Cds,i • **..!
Assuming that the soil gas phase is negligible on a mass basis,
the total soil chemical concentration is composed of dissolved
chemical plus sorbed chemical :
Substituting the partitioning relationship between dissolved and
sorbed chemical, one gets the total soil chemical concentration:
The fraction of chemical dissolved in the soil water is the
following:
cstii es
M-74
-------
The fraction of chemical sorbed in the soil is l-fds/i
6 + Kd .-BD
S S i JL
The concentration of chemical dissolved in the soil water is
derived as follows:
SC.-BD
Kds i'BD
and the concentration of chemical sorbed to the soil is this
Sc.-JCd .-BD
, = f__ ,'Sc. =
M.S. 3. 3 Surface Water Mass Balance Relationships . Taking
Figure M-3 as a control volume for the water body, at
steady-state total chemical loading equals the sum of chemical
outflow and chemical loss. The chemical outflow is the product
of the volumetric flow rate and the water column concentration:
Outflow = Vfx-Cwt
The fraction of the total water body chemical concentration that
is in the water column is defined in this way:
water
,--.
wtot
The outflow can, thus, be given in terms of the total water body
concentration:
Outflow = Vfx-fwater-Cwtot-dz/dw
The chemical loss can also be given in terms of the total water
body concentration:
Loss = kwt-Cwtot-Vz
Equating loading to outflow plus loss, and solving for the total
water body concentration gives this:
M-75
-------
wtot
In a similar manner, the mass balance equation for sediment
in the water body can be derived. Taking Figure M-4 as a control
volume, the soil eroding into the water body, y^, equals the sum
of the amount depositing into the upper bed, Xd, and the
advective loss from the water column, Xa. X« is the product of
the areal soil erosion flux, the watershed surface area, and the
watershed sediment delivery ratio, with a factor converting kg to
mg:
Xd is the product of the suspended solids concentration and the
deposition rate:
Xa is the product of the suspended solids concentration and the
volumetric flow rate:
Xa = Vf -TSS
a X
Substituting these relationships into the solids mass balance and
solving for the suspended solids concentration in the water
column gives this equation:
TSS =
WAL SD 103
M.3.3.4 Surface Water Equilibrium Relationships . Within
the water column, the dissolved chemical concentration
equilibrium with the sorbed concentration is defined by a
partition coefficient:
, i
, i
The total chemical concentration in the water column is composed
of dissolved chemical plus sorbed chemical:
M-76
-------
Substituting the partitioning relationship between dissolved and
sorbed chemical, the total chemical concentration is calculated
as this:
The fraction of chemical dissolved in the water column becomes
the following:
Within the bed sediment, the dissolved chemical
concentration equilibrium with the sorbed concentration is
defined by a partition coefficient:
Cdi>,i = Csb,i ' Kdbs,i
The total benthic chemical concentration is composed of dissolved
chemical plus sorbed chemical :
Substituting the partitioning relationship between dissolved and
sorbed chemical, one calculates the total benthic chemical
concentration :
The fraction of chemical dissolved in the benthic pore water is
the following:
CO O
Jl_ • W , O .
—. qjp j. J&5 bs
db,i = T; = "S '
The total chemical concentration on a total water body basis
is the sum of the water column and the benthic concentrations,
weighted by the respective volumes is given by these
relationships:
V V^ d d_
- = n . ^ + c ' = r • - + C • -
^Wt.i y ^bt.l rr UWt, i J ^bt.i -J
2 VZ Z Z
M-77
-------
Substituting in the relationships for C^i and Cbtii, and noting
that at equilibrium CdWii = C^i, one obtains the total water body
concentration as a function of the dissolved water column
concentration :
Q-*) -dw/dz + (0^ i
The fraction of the chemical that is in the water column is
defined as this:
f =
water, i r->
wtot,i
Substituting in the relationships for Cwt/i and C^^^, one obtains
expressions for f water,! an<3 fbenth,i as functions of environmental
and chemical properties :
f =
water'i
-dw/dz + (Qbs + KdbSi .-BS)
'db/dz
M-78
-------
M.S.4 Summary of Notation
AS = surface area of watershed soil element (m2)
BD = representative watershed soil bulk density (g/cm3)
BS = benthic solids concentration (kg/L)
catm,i = component "i" vapor phase air concentration over
watershed (ug/m3)
Cbtii = total benthic component "i" concentration (mg/L)
Cd = drag coefficient ( = 0.0011)
Cds/i = concentration of "i" dissolved in pore water
(mg/L)
Cps>i = concentration of "i" in particulate phase (mg/kg)
Csb = background "natural" soil concentration (ug
pollutant/g soil)
Cst/i = total soil concentration of component "i" (mg/L)
Cwt = water column concentration of total mercury (ug/L)
cwt,Hgo = water column concentration of elemental
mercury (ug/L)
Cwt.HgdD = water column concentration of divalent
mercury (pg/L)
Cwt.MeHg = water column concentration of me thy liner cury
(ug/L)
Cwtot.i = total water body component "i" concentration,
including water column and benthic sediment
(mg/L)
Cwtot = total water body concentration, including water
column and benthic sediment (mg/L)
db = depth of the upper benthic sediment layer (m)
dv, = depth of the water column (m)
d2 = total depth of water body, d^+db (m)
D = depth of water body (m)
Da/i = diffusivity of component "i" in air (cm 2/sec)
DW;i = diffusivity of component "i" in water (cm2/sec)
= yearly dry deposition rate of component "i"
onto surface water body (g pollutant/m2-yr)
= yearly average dry depositional flux of
component "i" onto watershed (g/m2-yr)
= yearly wet deposition rate of component "i"
onto surface water body (g pollutant/m2-yr)
= yearly average wet depositional flux of
component "i" onto watershed (g/m2-yr)
ER = soil enrichment ratio (unitless)
Ev = average annual evapotranspiration (cm/yr)
M-79
-------
fbenth,i = fraction of total water body component "i"
concentration that occurs in the benthic
sediment
fdb,i - fraction of bed sediment component "i"
concentration that is dissolved
fds,i = fraction of soil component "i" concentration that
is dissolved
fdw,i = fraction of water column component "i"
concentration that is dissolved
fPs,i = fraction of soil component "i" concentration that
is s orbed
fsi = fraction of soil concentration that is component
"i" (i.e., elemental, divalent, and methylmercury)
fsi = fraction of soil concentration that is elemental
mercury
fs2 = fraction of soil concentration that is divalent
mercury
fs3 = fraction of soil concentration that is
methylmercury
f water,! = fraction of total water body component "i"
concentration that occurs in the water column
fwl = fraction of water column concentration that is
elemental mercury
fw2 = fraction of water column concentration that is
divalent mercury
fw3 = fraction of water column concentration that is
methylmercury
Hi = Henry's law constant for component "i"
(atm-m 3/mole)
I = average annual irrigation (cm/yr)
kb i = benthic burial rate constant for component "i"
(yr'1)
kgb,i = benthic degradation or transformation rate
constant for component "i" (yr"1}
kgw.i = water column degradation or transformation rate
constant for component "i" (yr"1)
k^i = water column volatilization loss rate constant for
component "i" (yr"1)
kwt = total first order dissipation rate constant,
including water column and benthic degradation,
volatilization, and burial (yr"1)
= overall total water body dissipation rate constant
for component "i" (yr"1)
ks = total chemical loss rate constant from soil (yr"1)
M-80
-------
co*sP'
0*&
^
& ,:s
*°
c°
0
y^-
-------
©c
v
<*t
:©s
S0
*J
Sc,,
te>^s
-e>
,«;??*>.:e *•
,$ir.^-45?-2,.
^
:^r-^'^^o,V9t
-------
= soil loss constant for component "i" due to all
processes {yr"1)
= soil loss constant due to leaching (yr"1)
kseA = soil loss constant due to erosion (yr"1)
= soil loss constant due to runoff (yr"1)
= soil loss constant due to chemical transformation/
degradation (yr"1)
= soil loss constant due to volatilization (yr"1)
K^i = gas phase transfer coefficient (m/year)
KL;i = liquid phase transfer coefficient for component
"i" (m/year)
Ktri = gas phase mass transfer coefficient for component
"i" (cm/s; see Eq [4-6], IED)
K^i = overall transfer rate, or conductivity for
component "i" (m/yr)
.i = bottom sediment/pore water partition
coefficient for component "i" (L/kg)
Kds-i = soil-water partition coefficient for component "i
(L/kg or crnVg)
K^sw.i = suspended sediment /surf ace water partition
coefficient for component "i" (L/kg)
Kej_ = equilibrium coefficient for component "i"
(s/cm/yr) ; see Eq [4-5], IED)
Kti = gas phase mass transfer coefficient for component
"i" (cm/s; see Eq [4-6], IED)
LDeP,i = deposition of particle bound component "i" (g/yr)
LDIF,I = atmospheric diffusion flux of component "i" to
soil (g/m2-yr)
Loif,i = diffusion of vapor phase component "i" (g/yr)
LEii = soil erosion load for component "i" (g/yr)
LI;i = internal transformation load, equal to 0 for
equilibrium mercury chemistry (g/yr)
Lls>i = internal transformation load of component "i" per
areal basis (g/m2-yr)
LR i = pervious surface runoff load for component "i"
(g/yr)
LRI/i = runoff load from impervious surfaces (g/yr)
LT = total chemical load into water body, including
deposition, runoff, erosion, atmospheric
diffusion, and internal chemical transformation
(g/yr)
LT/i = total component "i" load to the water body (g/yr)
Lw = yearly average load of pollutant to watershed on
an areal basis (g pollutant/m2-yr)
= molecular weight of component "i"
M-81
-------
P = average annual precipitation (cm/yr)
R = universal gas constant (8.206 x 10~5
atm-m3/mole-°K)
RG,! = gas phase resistance (year/m)
RLji = liquid phase resistance (year/m)
Ro = average annual runoff (cm/yr)
Sc = average watershed soil concentration after time
period of deposition (ug pollutant/g soil)
Sca,i = air Schmidt number for component "i"
(dimensionless)
SCi = total component "i" concentration in watershed
soils (ug/g)
ScHg0 = soil concentration of elemental mercury (ug
pollutant/g soil)
ScHg(II) = soil concentration of divalent mercury (ug
pollutant/g soil)
ScMeHg = soil concentration of methylmercury dag
pollutant /g soil)
Scw
-------
WAL = total watershed area receiving pollutant
deposition (m2)
WA,, = water body surface area (m2)
Xe = unit soil erosion flux, calculated in the soils
section from the Universal Soil Loss Equation
(USLE) ; see Eq [9-3], IED (kg/m2-yr)
Z = representative watershed mixing depth to which
deposited pollutant is incorporated (cm)
k = von Kantian's constant ( = 0.4)
A2 = dimensionless viscous sublayer thickness ( = 4)
ua = viscosity of air corresponding to the air
temperature (g/cm-s)
Uw = viscosity of water corresponding to the water
temperature (g/cm-s)
pa = density of air corresponding to the water
temperature (g/cm3)
ps = solids density, 2.65 kg/L
pw = density of water corresponding to the water
temperature (g/cm 3)
va = dynamic viscosity of air (cm2/sec)
6 = temperature correction factor, set to 1.026.
6S = volumetric soil water content
6bs = bed sediment porosity (Lwater/L)
M-83
-------
M.4 References
1. Johnson, W. B., D. E. Wolf, and R. L. Mancuso, Long-term
regional patterns and transfrontier exchanges of airborne
sulfur pollution in Europe. Atmospheric Environment 12:
511-527. 1978.
2. Bhumralkar, C. M., R. L. Mancuso, D. E. Wolf, R. H.
Thuillier, K. D. Nitz, and W. B. Johnson, Adaptation and
application of a long-term air pollution model ENAMAP-1 to
Eastern North America. Final Report, EPA-600/4-80/039, U.S.
Environmental Protection Agency, Research Triangle Park, NC.
1980.
3. Johnson, W. B., 1983. Interregional exchanges of air
pollution: Model types and applications. Journal of the
Air Pollution Control Association 33: 563-574. 1983.
4. Eder, B. K., D. H. Coventry, T. L. Clark, and C. E.
Bellinger, RELMAP: A regional Lagrangian model of air
pollution - users guide. Project Report, EPA/600/8-86/013,
U.S. Environmental Protection Agency, Research Triangle
Park, NC. 1986.
5. Clark, T. L., P. Blakely, and G. Mapp, Model calculations
of the annual atmospheric deposition of toxic metals to Lake
Michigan. 85th Annual Meeting of the Air and Water
Management Assoc., Kansas City, MO, June 23-37. 1992.
6. Petersen, G., A. Iverfeldt and J. Munthe, Atmospheric
mercury species over Central and Northern Europe. Model
calculations and comparison with observations from the
Nordic Air and Precipitation Network for 1987 and 1988.
Atmospheric Environment 29:47-68. 1995.
7. Munthe, J., The aqueous oxidation of elemental mercury by
ozone. Atmospheric Environment 26A: 1461-1468. 1992.
8. Brosset, C. and E. Lord, Mercury in Precipitation and
Ambient Air. A New Scenario. Water, Air and Soil Pollution
56:493-506. 1991.
9. Iverfeldt, A., Occupance and Turnover of Atmospheric
Mercury over the Nordic Countries. Water, Air and Soil
Pollution 56:251-265. 1991.
M-84
-------
10. Lindqvist, 0., K. Johansson, M. Aastrup, A. Andersson, L.
Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M.
Meili, and B. Timm, Mercury in the Swedish environment.
Transformation and deposition processes. Water, Air and
Soil Pollution 55:49-63. 1991.
11. Penner, J. E., H. Eddleman and T. Novakov, Towards the
development of a global inventory for black carbon
emissions. Atmospheric Environment 27A:1277-1295. 1993.
12. Keeler, G. J., S. M. Japar, W. W. Brachaczek, R. A. Gorse,
Jr., J. M. Norbeck and W. R. Pierson, The sources of
aerosol elemental carbon at Allegheny Mountain. Atmospheric
Environment 24A:2795-2805. 1990.
13. Iverfeldt, A., and 0. Lindqvist, Atmospheric oxidation of
elemental mercury by ozone in the aqueous phase.
Atmospheric Environment 20:1567-1573. 1986.
14. Munthe, J., Z. F. Xiao and 0. Lindqvist, The aqueous
reduction of divalent mercury by sulfite. Water, Air and
Soil Pollution, 56:621-630. 1991.
15. Munthe, J. and W. McElroy, Some aqueous reactions of
potential importance in the atmospheric chemistry of
mercury. Atmospheric Environment 26A: 553-557. 1992.
16. Schroeder, W. H. and R. A. Jackson, Environmental
measurements with an atmospheric mercury monitor having
speciation capabilities. Chemosphere 16:183-199. 1987.
17. Schroeder, W. H., G. Yarwood and H. Niki., Transformation
processes involving mercury species in the atmosphere -
Results from a literature survey. Wat. Air Soil Pollut.
56:653-666. 1991.
18. Sanemasa, I., The solubility of elemental mercury vapor in
water. Bulletin of the Chemical Society of Japan, 48:1795-
1798. 1975.
19. Seinfeld, J. H., Atmospheric Chemistry and Physics of Air
Pollution, John Wiley and Sons, New York, NY. 1986.
M-85
-------
20. Hanson, P.«J., S. E. Lindberg, K. H. Kim, J. G. Owens, and
T. A. Tabberer, Air/surface exchange of mercury vapor in
the forest canopy: I. Laboratory studies of foliar mercury
vapor exchange. International Conference on Mercury as a
Global Pollutant, July 10-14, Whistler, British Columbia,
Canada. 1994.
21. Lindberg, S. E., T. P. Meyers, G. E. Taylor, Jr., R. R.
Turner and W. H. Schroeder, Atmosphere-surface exchange of
mercury in a forest: Results of modeling and gradient
approaches. Journal of Geophysical Research 97:2519-2528.
1992.
22. Walcek, C. J., R. A. Brost, J. S. Chang and M. L. Wesely,
SO2, sulfate and HN03 deposition velocities computed using
regional land use and meteorological data. Atmospheric
Environment 20:949-964. 1985.
23. Wesely, M. L., 1986. On the parameterization of dry
deposition of acidifying substances for regional models.
Internal Report (Nov. 1986): Interagency Agreement
DW89930060-01 to the U.S. Department of Energy, U.S.
Environmental Protection Agency, Atmospheric Sciences
Research Laboratory, Research Triangle Park, NC.
24. Lindberg, S. E., R. R. Turner, T. P. Meyers, G. E. Taylor,
Jr. and W. H. Schroeder, Atmospheric concentrations and
deposition of mercury to a deciduous forest at Walker Branch
Watershed, Tennessee, USA. Water, Air and Soil Pollution
56:577-594. 1991.
25. Deposition rate calculations for air toxics source
assessments. Report dated September .16, 1987, by California
Air Resources Board, 6 pp. 1987.
26. Fitzgerald, W. F., R. P. Mason and G. M. Vandal.,
Atmospheric cycling and air-water exchange of mercury over
mid-continental lacustrine regions. Wat. Air Soil Pollut.
56:745-767. 1991.
27. Burke, J., M. Hoyer, G. Keeler and T. Scherbatskoy., Wet
deposition of mercury and ambient mercury concentrations at
a site in the Lake Champlain basin. Wat. Air Soil Pollut.
80:353-362. 1995.
M-86
-------
28. Lamborg, C. H., W. F. Fitzgerald, G. M. Vandal and K. R.
Rolfhus., Atmospheric mercury in northern Wisconsin:
Sources and species. Wat. Air Soil Pollut. 80:189-198.
1995.
29. Dvonch, J. T., A. F. Vette, G. J. Keeler, G. Evans and R.
Stevens., An intensive multi-site pilot study investigating
atmospheric mercury in Broward County, Florida. Wat. Air
Soil Pollut. 80:169-178. 1995.
30. Glass, G. E., J. A. Sorenson K. W. Schmidt, G. R. Rapp, Jr.,
D. Yap and D. Fraser., Mercury deposition and sources for
the upper Great Lakes region. Wat. Air Soil Pollut.
56:235-249. 1991.
31. Sorenson, J. A., G. E. Glass and K. W. Schmidt., Regional
patterns of wet mercury deposition. Environ. Sci. Technol.
28:2025- 2032. 1994.
32. Hoyer, M., J. Burke and G. Keeler., Atmospheric sources,
transport and deposition of mercury in Michigan: Two years
of event precipitation. Wat. Air Soil Pollut. 80:199-208.
1995.
33. Overcamp, T.J. Modelling of Air Quality for Industrial
Pollution Control, Appendix from a Continuing Education
Short course taught at Clemson University on December 7,
1977.
34. Rao, K.S. Analytical solutions of a gradient-transfer model
for plume deposition and sedimentation. NOAA-TM-ERL ARL-
109. U.S. National Oceanic and Atmospheric Administration,
Silver Spring, MD. 1981.
35. U.S. EPA, User's Guide for the Industrial Source Complex
(ISC2) Dispersion Models, Volume II - Description of Model
Algorithms, EPA-450/4-92-008b, Research Triangle Park, North
Carolina 27711. 1992.
36. Randerson, D., D. Randerson, editor, Atmospheric Science and
Power Production, DOE/TIC-27601. 1984.
37. Hanna, S.R., G.A. Briggs, and R.P. Hosker, Jr. Handbook on
Atmospheric Diffusion, DOE/TIC-11223. Pp. 2-3. 1982.
38. Wark, K. and C.F. Warner Air Pollution Its Origin and
Control, Harper Collins. 1981.
M-87
-------
39. Pasquill, F. The Estimation of Dispersion of Windborne
Material, Meteorol.Mag., 90:33-49. 1961.
40. Gifford, F.A. 1961. Use of routine meteorological
observations for estimating atmospheric dispersion. Nuclear
Safety, 2, No. 4, 47-51.
41. Hanna, S.R., G.A. Briggs, and R.P. Hosker, Jr. Handbook on
Atmospheric Diffusion, DOE/TIC-11223. 1982.
42. Gifford, F.A. Turbulent Diffusion Typing Schemes - A
Review, Nucl.Saf., 17:68-86. 1976.
43. Smith, M.E., The Forecasting of Micrometerological
Variables, Meteorol. Monogr., 4:50-55. 1951.
44. Cramer, H.E. A Practical Method for Estimating the
Dispersal of Atmospheric Contaminants, in Proceedings of the
First National Conference on Applied Meteorology, Sec. C,
pp. C-33-C-35, American Meteorological Society, Hartford,
Conn. 1957.
45. Irwin, J. Estimating Plume Dispersion: A Recommended
Generalized Scheme, in Proceedings of the Fourth Symposium
on Turbulence, Diffusion, and Air Pollution, Jan. 15-18,
1979, Reno, Nev., pp. 62-69, American Meteorological
Society, Boston, Mass. 1979.
46. Catalano, J.A., D.B. Turner, and J.H. Novak. User's Guide
for RAM - Second Edition. EPA/600/8-87/046, U.S. EPA,
Research Triangle Park, North Carolina. 1987.
47. Pierce, T.E. and D.B. Turner, User's Guide for MPTER: A
multiple point Gaussian dispersion algorithm with optional
terrain adjustment. EPA-600/8-80-016. U.S. E.P.A., Research
Triangle Park, N.C. 1980
48. Wells, A.E. , Results of Recent Investigations of the
Smelter Smoke Problem, Ind. Eng. Chem, 9:640-646. 1917.
49. Pasquill, F., Atmospheric Diffusion, the Dispersion of
Windborne Material from Industrial and other Sources, Ellis
Horwood, New York. 1974.
50. Briggs, G.A. Plume Rise, AEC Critical Review Series, TID -
25075, National Technical Information Service, Springfield,
VA., 81 pp. 1969.
M-88
-------
51. Briggs, G.A. Some recent analyses of plume rise
observation, Paper presented at the Second International
Clean Air Congress of the International Union of Air
Pollution Prevention Associations, Washington, B.C.,
December 6-11, 1970.
52. Briggs, G.A. Discussion on Chimney Plumes in Neutral and
Stable Surroundings. Atmos. Environ. 6:507-510. 1972.
53. Bowers, J.R., J.R. Bjorkland and C.S. Cheney. Industrial
Source Complex (ISC) Dispersion Model User's Guide. Volume
I, EPA-450/4-79-030, U.S. EPA, Research Triangle Park, North
Carolina. 1979.
54. Briggs, G.A., Plume Rise predications, in Lectures on Air
Pollution and Environmental Impact Analysis, American
Meteorological Society, Boston, Massachusetts. 1975.
55. Ref. 52, p. 1030.
56. Ref. 41, p. 14.
57. Fay, J.A., M. Escudier, and D.P. Hoult, A Correlation of
Field Observations of Plume Rise, Fluid Mechanics Laboratory
Publication No. 69-4, Massachusetts Institute of Technology.
1969.
58. Rao, K.S. and L. Satterfield. A study of the probable
environmental impact of fugitive coal dust emissions at the
Ravenswood Power Plant, New York. ATDL Contribution 80/26,
NOAA, Oak Ridge, TN. 1980.
59. Subroutines for Calculating Dry Deposition Velocities Using
Sehmel's Curves. Prepared by Bart Croes, California Air
Resources Board. 1986.
60. Holzworth,G.C., Mixing Heights, Wind Speeds and Potential
for Urban Air Pollution Throughout the Contiguous United
States. Publication No.AP-101, U.S. EPA, Research Triangle
Park, North Carolina 27711. 1972.
61. Turner, D.B., Workbook of Atmospheric Dispersion Estimates.
Public Health Service Publication No. 999-AP-26, U.S.
E.P.A., Research Triangle Park, N.-C. 1970.
62. Sehmel,G.A., Deposition and Resuspension, in Atmospheric
Science and Power Production, D. Randerson (Ed.), DOE/TIC-
27601. 1984.
M-89
-------
63. Slinn, W.G.N. Precipitation Scavenging, in Atmospheric
Science and Power Production, D. Randerson, ed.
DOE/TIC-27601. 1984.
64. PEI Associates, Inc, and H.E. Cramer Company, Inc., Air
quality modeling analysis of municipal waste combustors.
Prepared for Monitoring and Data Analysis Division, Office
of Air Quality Planning and Standards, Research Triangle
Park, North Carolina 27711. 1986.
65. Engelmann, R.J. The Calculation of Precipitation
Scavenging, in Meteorology and Atomic Energy 1968, D.H.
Slade, editir. U.S. Atomic Energy Commission. 1968.
66. Briggs, G.A., Diffusion Estimation for Small Emissions,
Atmospheric Turbulance and Diffusion Laboratory,
Contribution File No. 79, Oak Ridge, Tennessee. 1973.
67. Egan, B.A., Turbulent Diffusion in Complex Terrain, in
Lectures on Air Pollution and Environmental Impacts
Analysis, pp. 112-135, D.Haugen (Ed.), American
Meteorological Society, Boston, Mass. 1975.
68. Sherlock, R.H. and E.A. Stalker, A Study of Flow Phenomena
in the Wake of Smoke Stack, Engineering Research Bulletin
29, University of Michigan, Ann Arbor. 1941.
69. Briggs, G.A., Diffusion Estimation for Small Emissions. In
ERL, ARL USAEC Report ATDL-106. U.S. Atomic Energy
Commission, Oak Ridge, Tennessee. 1974.
70. Huber, A.H. Incorporating Building/Terrain Wake Effects on
Stack Effluents, in Preprints of Joint Conference on
Applications of Air Pollution Meteorology, Salt Lake City,
Nov. 29-Dec.2, pp. 353-356, American Meteorological Society,
Boston, Mass. 1977.
71. Hubert, A.H. and W.H. Snyder (1976). Building Waste Effects
on Short Stack Effluents, in Third Symposium on Atmospheric
Turbulence, Diffusion, and Air Quality, Raliegh, NC, Oct.
19-22, pp. 235-242, American Meteorological Society, Bostan,
Mass.
72. Ref. 35, p. 1-20.
73. Ref. 36, p. 303.
74. Ref. 36, p. 303.
M-90
-------
75. Ref. 35, p. 1-24.
76. Pasquill, F., Atmospheric Dispersion Parameters in Gaussian
Plume Modeling: Part II. Possible Requirements for Change
in Turner Workbook Values, Report EPA-600/4-760306, U.S.
EPA. 1976.
77. U.S. EPA. Methodology for Assessing Health Risks Associated
with Indirect Exposure to Combustor Emissions. Interim
Final. Office of Health and Environmental Assessment,
Cincinnati, Ohio. EPA/600/6-90/003. 1990.
78. Wischmeier, W.H. and D.D. Smith., Predicting Rainfall
Erosion Losses — A Guide to Conservation Planning. USDA
Handbook No. 537. 1978.
79. Travis, C.C., C.F. Baes, III, L.W. Barnthouse, et al. ,
Exposure Assessment Methodology and Reference Environments
for Synfuel Risk Analysis. Oak Ridge National Laboratory.
ORNL/TM-8672. Prepared for U.S. Environmental Protection
Agency, Office of Research and Development. 1983.
80. Whitman, R.G., A Preliminary Experimental Confirmation of
the Two-Film Theory of Gas Absorption. Chem. Metallurg.
Eng. 29:146-148. 1923.
81. Burns, L.A., D.M. Cline, and R.R. Lassiter. Exposure
Analysis Modeling System (EXAMS): User Manual and System
Documentation, U.S. Environmental Protection Agency, Athens,
GA. EPA-600/3-82-023. 1982.
82. Ambrose, R.B. et al. WASP4, A Hydrodynamic and Water
Quality Model—Model Theory, User's Manual, and Programmer's
Guide. U.S. Environmental Protection Agency, Athens, GA.
EPA/600/3-87-039. 1988.
83. Geraghty, J.J., D.W. Miller, F.V. Der Leenden, and F.L.
Troise, Water Atlas of the United States, A Water
Information Ceter Publication, Port Washington, N.Y. 1973.
84. O'Connor, D.J. and W.E. Dobbins., Mechanism of Reaeration
in Natural Streams, ASCE Transactions, pp. 641-684, Paper
No. 2934. 1958.
85. O'Connor, D.J., Wind Effects on Gas-Liquid Transfer
Coefficients. Journal of Environmental Engineering, Volume
109, Number 9, pp. 731-752. 1983.
M-91
-------
-------
TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1 REPORT NO
EPA-453/R-96-013c
3. RECIPIENT'S ACCESSION NO.
4 TITLE AND SUBTITLE
Study of Hazardous Air Pollutant Emissions from Electric
Utility Steam Generating Units — Interim Final Report
Volume 3. Appendices H - M
5. REPORT DATE
October 1996
6 PERFORMING ORGANIZATION CODE
7 AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO
9 PERFORMING ORGANIZATION NAME AND ADDRESS
U.S. Environmental Protection Agency
Emission Standards Division/Air Quality Strategies and
Standards Division
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
10. PROGRAM ELEMENT NO.
11 CONTRACT/GRANT NO
12 SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
14 SPONSORING AGENCY CODE
15 SUPPLEMENTARY NOTES
16 ABSTRACT
This report has been prepared pursuant to section 112(n)(l)(A) of the Clean Air Act, and provides the
Congress and the public with information regarding the emissions, fate, and transport of utility HAPs.
The primary components of this report are: (1) a description of the industry; (2) an analysis of emissions
data; (3) an assessment of hazards and risks due to inhalation exposures to numerous HAPs (excluding
mercury); (4) an assessment of risks due to multipathway (inhalation plus non-inhalation) exposure to
radionuclides; and (5) a discussion of alternative control strategies. The assessment for mercury includes a
description of emissions, deposition estimates, control technologies, and a dispersion and fate modeling
assessment which includes predicted levels in various media based on modeling from four representative
utility plants using hypothetical scenarios. The EPA plans to publish a final report at a later date which
will include (1) a more complete assessment of the exposures, hazards, and risks: (2) conclusions, as
appropriate and feasible, regarding the significance of the risks and impacts to public health; and (3) a
detennination as to whether regulation of utility HAPs is appropriate and necessary.
17
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b IDENTIFIERS/OPEN ENDED TERMS
c. COSAT1 Field/Group
Air Pollution
Atmospheric Dispersion Modeling
Electric Utility Steam Generating Units
Hazardous Air Pollutants/Air Toxics
Air Pollution Control . *
•
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
286
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE
-------
U.S. Environmental Protection Agency
Region 5, Library (PL-12J)
77 West Jackson Boulevard, 12th Floor
Chicago, IL 60604-3590
------- |