-------
DATA QUALITY RATINGS MEANIMG
BLANK No value in AP-42 and no value or a
zero given as an emission factor in
NEDS emission factor list.
U Unrated - Not in AP-42
U/D Unrated - Different values given in
AP-42 and NEDS emission factor list
U/E Unrated - Emission factor is an
approximation based on an equation
presented in AP-42
U/R Unrated - Emission factor was derived
from AP-42 data for another similar
source category
U/0 Unrated - Emission factor was derived
from other EPA documents, State data
or other sources
U/B Unrated - An obsolete process from an
earlier version of AP-42
T Technology Transfer - Emission factor
developed by NAPAP
T/V Technology Transfer but emission
factor reported in AP-42
V No emission factor in NEDS emission
factor list but emission factors are
reported in AP-42
»
The confidence in emission factors for SC>2 and NOjj is quite good in that
over 92% of the calculated emissions of both species resulted from the
application of emission factors with quality ratings of C or higher. The
emission factor data quality for_the remaining criteria pollutants is less
reliable than for SC>2 and NOX with large percentages of each resulting from
non AP-42 factors or D and E rated emission factors.
PRIMARY SULFATE EMISSION FACTORS
For the 1980 NAPAP Emissions Inventory an emission factor report was
written to describe the development of primary sulfate emission factors.
Part of the 1985 allocation factor development work, included the development
of speciation data for total suspended particulate. The speciation work
identified primary sulfate as a chemical constituent of TSP. Therefore, an
analysis was undertaken to select the best methodology for determining primary
sulfate emissions. The analysis compared the use of the speciation profiles
to that of the traditional emission factor approach. In addition, a review of
the NEDS emission factors was undertaken to ensure that they reflect sulfur
dioxide and not sulfur oxides. This ensured tha-t primary sulfate emissions
were not being reported twice or were not being included as S02 emissions.
4-13
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The particulate speciation document was developed to aggregate existing
data on particulate speciation and size classification for chemical
constituents present in particulate emissions. After reviewing the data it
was decided that the primary sulfate emission factor document contained the
more reliable data for primary sulfate. A review of the NEDS emission factors
for 502 found small contributions due to SOj (in the case of residual oil
firing), or SOj and gaseous sulfates (in the case of coal firing). These
contributions amount to 0.7 percent of S(>2 emissions for utility or industrial
coal firing 1.4 percent for utility residual oil firing, and 1.0 percent for
industrial residual oil firing. These relatively small contributions did not
justify altering the S(>2 emission estimates.
The primary sulfate emission factor report discussed four major
activities:
• Primary sulfate formation mechanisms thought to be prevalent in
combustion processes
• Assessment of the state-of-the-art methodology for primary sulfate
sampling and analysis of source emissions
• Review of the SURE and WG3B primary sulfate emission inventories
• Collection, review, and calculation of primary sulfate emission
factors derived from additional field studies not included in the
SURE and WG3B inventories
Primary sulfate emission fa'ctors recommended for .use in the annual
inventory are presented in Table 4-3. The factors are reported for a number
of SCCs based on an analysis of available data. Most of the current
data set is for fossil fuel combustion in the industrial and utility sectors.
The ratings listed in Table 4-3 are similar to—the rating categories used in
AP-42 to indicate confidence in the data. Data reliability decreases from A
through E.
— o
The emission factors given in Table 4-3 reflect sulfate emitted as SO^ .
Source test data suggest that sulfate emissions can be markedly affected by
fuel sulfur content and other parameters such as furnace oxygen levels and
trace element content of the fuel. For source categories other than fossil
fuel combustion, sulfate emission factors are reported in standard SCC units
or as percentages of mass particulate emissions. The reported TSP emissions
represent all particulate emissions including the sulfate component.
The calculation of specific primary sulfate emission factors was based on
the following hierarchical selection process:
1. Where available, all field measurements using standard measurement
techniques were considered as the prime data set.
2. Sulfate emission assessments were aggregated for different point
sources within the same source category only if fuel composition and
emission controls were similar.
4-14
-------
3. Emissions data acquired through the use of methods other than
standard measurement techniques were included only if multiple
measurements yielded data with minimal scatter.
There are a number of source categories for which no experimental field
data exist. However, most of the regional mass emissions of primary sulfates
should occur from those source categories for which emission factors have
already been substantiated (utility and industrial fossil fuel burning). The
best approach to improving the sulfate component may be an expanded field
measurement activity which focuses on the source categories which have not
been characterized sufficiently, but which are potentially significant
contributors to regional emissions. Improvements are needed in the data base
for low sulfur residual oil-fired industrial and commercial boilers. Field
measurements are recommended for this source type which is a significant
source of sulfur emissions in major metropolitan areas in the eastern United
States. In addition, sulfate emissions from the pulp and paper industry need
further characterization given the large emission factors reported by WG3B.
Pulp mill operations are concentrated in the acid deposition sensitive
Northeast and represent a major SO2 and particulate source contributor in the
southeastern United States.
The following conventions were used during the calculation of primary
sulfate emissions for the annual inventory. Separate emission factors were
listed for primary sulfate from eastern and western bituminous coal. These
emission factors vary by a factor of three. The NEDS system does not
distinguish between eastern and western bituminous coal. All bituminous coals
were assumed to be eastern.bituminous. The development of the western coal
emission factor was based on a small number of measurements. No explanation
for the large difference in the factors was presented.. The factors for
eastern coal are considered valid for a range of sulfur content coal from 0.9
to 4.7 weight percent.
The use of control equipment for sulfur dioxide and its impact on primary
sulfate emissions are complicated. Primary sulfate emissions are reduced by
S(>2 control devices and the SO^ emission factors have been revised to depend
on the primary SC>2 control device as reported to NEDS. For utility and
industrial fuel combustion SCCs, any nonblank code resulted in the use of a
lower emission factor. For the remaining SCCs the appropriate SO^ factor was
used regardless of control device except for cement manufacturing. The
emission factor for cement manufacturing assumes no particulate control
devices. If a particulate control efficiency was listed in the 1985 NEDS
file, the primary sulfate emissions were reduced accordingly.
4-15
-------
TABLE 4-3. PRIMARY SULFATE EMISSION FACTORS
Source category
Control
device
Primary sulfate
emission factor Rating
Electric Utilities -
External Combustion
Eastern bituminous coal
(1-01-002)
Lignite
(1-01-003)
Residual oil (>1Z sulfur
content)
(1-01-004)
Industrial - External
Combustion
Eastern bituminous coal
(1-02-002)
Residual oil
(1-02-004)
None
ESP and FGD
None
ESP
None
Fuel oil
additives
None
Multiclones
and FGD
None
Multiclones
and FGD
0.385S Ib/ton A
0.250S C
1.951S C
1.268
5.439S lb/1,000 gallons B
3.535S lb/1,000 gallons
2.646S Ib/ton B
0.462S C
5.296S lb/l,000^gallons D
2.616.S • D
Commercial/Institutional -
External Combustion
Residual oil (<12 sulfur
content ) ( 1-03-004 )
Space Heating - External
Combustion
Distillate oil
(1-05-001-05)
Industrial Process - Chemical
Manufacturing
- contact process
(3-01-023)
Fuel oil
additives
None
Demister
25.07S lb/1,000 gallons C
5.65S lb/1,000 gallons C
0.100 Ib/ton acid
produced
(continued)
4-16
-------
TABLE 4-3. (continued)
Source category
Control
device
Primary sulfate
emission factor Rating
Industrial Process - Primary
Metals
Primary copper smelters None
total
roaster (3-03-005-02)
smelter (3-03-005-03)
converter (3-03-005-04)
Primary zinc smelters None
(3-03-030)
Primary aluminum smelter None
(3-03-001)
Iron production None
(3-03-008)
Coke None
(3-03-003)
* '
Industrial Process - Wood
Products
Kraft pulp mill
(3-07-001-04)
Sulfite pulp mill
(3-07-002)
Wood/bark waste
(1-02-009)
None
None
22.5 Ib/ton
concentrated ore
1.08
5.76
15.66
55.5 Ib/ton processed
0.5% of S02
2.0% of S02
0.320 Ib/ton coal
charged
None
Multic lone
85% of NEDS TSPa
70% of NEDS TSPa
for Na base
25% of NEDS TSPa
for Ca base
3.6S Ib/ton bark
2.3S Ib/ton bark
C
C
D
D
(continued)
4-17
-------
TABLE 4-3. (continued)
Source category
Control
device
Primary sulfate
emission factor Rating
Industrial Process - Mineral
Product
Cement manufacturing
(3-05-006-06)
(3-07-007-06)
Gypsum manufacturing
(3-05-015)
Industrial Process - Petroleum
Industry
Fluid crackers
(3-06-002)
Sulfur recovery Glaus plants
(3-01-032)
Uncontrolled 5.6 lb/tona»b
None
ESP
None
562 of NEDS TSPa
15.0 lb/1,000 barrels
oil
2.8 Ib/ton produced
C
C
Reference 9.
Emissions are to be calculated using the control efficiency listed for
particulate emissions.
HYDROGEN CHLORIDE AND HYDROGEN FLUORIDE EMISSION FACTORS
Emission factors for hydrogen chloride and hydrogen fluoride were
developed for the 1980 NAPAP Emissions Inventory. A literature search was
conducted to identify significant anthropogenic emission sources and to
estimate emission rates for each of those sources. No changes to the emission
factors or methodologies were made for 1985. The emission factors are
summarized in Table 4-4. Emission factors based on tests performed by a sound
methodology and accompanied by adequate background data were used as a first
priority. Emission factors were evaluated on a scale of A through E, in a
manner similar to that used in AP-42. National emission estimates for base
year 1985 were calculated by multiplying the level of activity (production/use
rates) by the emission factor for that source.
4-18
-------
TABLE 4-4. HYDROGEN CHLORIDE AND HYDROGEN FLUORIDE EMISSION FACTORS
Source
Emission factor
Rating
Coal Combustion
Utility Boiler
Bituminous (1-01-002)
Anthracite (1-01-001)
Lignite (1-01-003)
Industrial Boilers
Bituminous (1-02-002)
Anthracite (1-02-001)
Lignite (1-02-003)
Comm./Inst. Boilers
Bituminous (1-03-002)
Anthracite (1-03-001)
Lignite (1-03-003)
Propylene Oxide Manufacture (3-01-205-01)
Incineration (5-01-001-01,02,5-01-005-07)
Municipal Waste (5-03-001-01,02,03,04)
Industrial Waste (5-03-007-01)
By-product Hydrochloric Acid Production.
(3-01-011-01) (with final scrubber)
1.90 Ib/ton burned
0.91 Ib/ton burned
0.01 Ib/ton burned
1.90 Ib/ton burned
0.91 Ib/ton burned
0.01 Ib/ton burned
1.48 Ib/ton burned
3.07 Ib/ton burned
0.35 Ib/ton burned
7.46 Ib/ton produced
5.0 lb/tona
5.35 lb/tona
0.2 Ib/ton
A
A
A
A
A
A
C
C
C
E
E
—HF—
Coal Combustion
Utility Boiler
Bituminous (1-02-002)
Anthracite (1-01-001)
Lignite (1-01-003)
Industrial Boilers
Bituminous (1-02-002)
Anthracite (1-02-001)
Lignite (1-03-003)
Comm./Inst. Boilers
Bituminous (1-03-002)
Anthracite (1-03-001)
Lignite (1-03-003)
Hydrogen Fluoride Manufacture
Tail gas vent (3-01-012r06)
Controlled - caustic scrubber
0.23 Ib/ton burned
0.18 Ib/ton burned
0.01 Ib/ton burned
0.23 Ib/ton burned
0.18 Ib/ton burned
0.01 Ib/ton burned
0.17 Ib/ton burned
0.13 Ib/ton burned
0.063 Ib/ton burned
0.2 Ib/ton
A
A
A
A
A
A
C
C
C
(continued)
4-19
-------
Table 4-4. (continued)
Source
Emission factor
Rating
Primary Aluminum Production
Anode baking furnace (3-03-001-05)
Prebaked reduction cell (3-03-001-01)
Prebaked fugitive emissions (3-03-001-08)
Vertical soderberg stud cells (3-03-001-03)
VSS - fugitive emissions (3-03-001-10)
Horizontal soderberg stud cells (3-03-001-02)1.9 Ib/ton
HSS - fugitive emissions (3-03-001-09) 2.2 Ib/ton
0.52 Ib/ton
4.9 Ib/ton
1.2 Ib/ton
0.6 Ib/ton
4.9 Ib/ton
Phosphate Fertilizer Industry
Phosphoric acid production
Reactor (3-01-016-01)
Condenser (3-01-016-03)
Gypsum ponds (3-01-016-02)
Triple Superphosphate Manufacture
Reactor/dryer (granulator) (3.01-029-06)
Diammonium Phosphate Manufacture
Dryers and coolers (3-01-030-01)
Ammoniator/granulator (3-02-030-02)
0.37 Ib/ton
0.043 Ib/ton
0.42 Ib/ton
21.0 Ib/ton
0.3 Ib/ton
0.3 Ib/ton
A
A
A
A
A
A
A
C
C
D
A
A
Hydrogen chloride is emitted from coal combustion, waste incineration,
and organic chemical manufacture. Hydrogen fluoride is emitted primarily as a
by-product of coal combustion and primary aluminum production. Other sources
of HF include the fertilizer industry and the hydrogen fluoride manufacturing
industry.
The rates at which HC1 and HF are emitted during coal combustion are
functions of coal composition and air pollution control techniques. A study
of coal combustion in utility boilers conducted by the Bureau of Mines found
the majority of chlorine contained in coal to volatilize and form HC1. °
There is a need for additional scientific data to directly assess the chemical
form of fluorine emitted during coal combustion. In lieu of such data and
because of the chemical similarity between fluorine and chlorine, it is
assumed that all fluorine in the feed coal is released as HF.
4-20
-------
Data compiled in 1979 on trace element compositions in coal obtained from
an EPA study* were used to calculate emission factors for coal combustion in
utility and industrial boilers. These factors were assigned an A quality
rating due to the number of tests conducted, availability of information
concerning accuracy, and type of test methods used. Recent data developed by
the Department of Energy's Pittsburgh Energy Technology Center resulted in
emission factors which compare favorably with those developed from the EPA
study.
Control devices were not factored into the calculation of HC1 and HF
emissions from coal combustion. Scrubbers, electrostatic precipitators
(ESPs), cyclones, and baghouses are used frequently on coal-fired utility
boilers as flue gas control techniques. The primary purpose of these controls
is to remove particulate matter from the flue gas stream. Under normal
operating practices, baghouses, ESPs, and cyclones have no significant effect
on removal of HC1 or HF. The efficiency of wet scrubbing devices has been
reported at about 80 percent for HC1 and HF emissions from bituminous
coal-fired utility boilers. Baghouses which have sorbent or alkaline
materials introduced may remove a substantial amount of HC1 and HF. A study
of the use of nacholite and sodium bicarbonate as dry sorbent resulted in a 95
to 98 percent HC1 removal. Another control technique, flue gas
desulfurization, is used to remove sulfur oxides from coal combustion. Data
have indicated that flue gas desulfurization is at least 95 percent effective
in removal of HC1. No data are available to quantify removal efficiencies of
HF. Since control devices were not considered in the development and
application of the emission factors for HC1 and HF, the emissions may be
overestimated for some individual sources.
Several emission factors received low quality ratings because of limited
data. Factors for HC1 from residential boilers, hydrochloric acid
manufacturing, and waste incineration received intermediate to low quality
ratings because of the small number of plants actually tested and the absence
of information concerning test methods. Factors for HF emissions from
residential boilers, phosphoric acid production, and hydrogen fluoride
manufacture were assigned low quality ratings because of the low number of
tests, the absence of information concerning accuracy of test methods, and the
number of assumptions made in determining these factors. Additional data
which address emission rates of HC1 and HF from these sources are needed.
AMMONIA EMISSION FACTORS
Ammonia emission factors for the 1980 NAPAP inventory were developed
using a methodology similar to that used for the HC1 and HF emission factors.
The methodology included a literature search, the identification of the best
available data, and the assignment of quality ratings. Emission factors for
NH^ were assigned a quality rating on a scale of A through E, as described for
the other emission factors.
A comparative study of four sets of NHg emission factors was conducted as
part of the 1985 NAPAP emissions inventory development effort. The findings
of this study formed the basis for the selection of NH^ emission factors for
4-21
-------
the annual inventory. The emission factors used in the annual inventory are
presented in Table 4-5. The majority of the emission factors were taken
from the 1980 NH^ emission factor document. Anthropogenic sources of ammonia
emissions include field application of livestock waste management practices,
beef cattle feedlots, fertilizer manufacture and use, fuel combustion, ammonia
synthesis, petroleum refineries coke manufacture, and transportation.
Ammonia volatilization rates from livestock wastes applied to
agricultural land are based on several data sources. Van Dyne*-* compiled
information on amounts of manure used in land application while estimates of
ammonia volatilization for various waste management practices and for various
types of livestock were calculated from data developed by Hoff and
Westerman. ' Ammonia emission factors were calculated from these data.
Volatilization rates are thought to vary weather conditions and waste
management practices. The development of these emission factors, was based on
several assumptions and, therefore, these factors have a high degree of
uncertainty.
Ammonia emissions from beef cattle feedlots were reported by Peters and
Hutchinson. » Emissions data based on these studies ranged over three
orders of magnitude. An average of the data from these two studies was used
for the development of the emission factor and should also be considered
highly uncertain.
Point Sources
AP-42 data were used to characterize emissions from .fertilizer
manufacture, ammonia synthesis, petroleum refineries, and coke manufacture.
The emission factor for fertilizer manufacture, can vary over several orders of
magnitude depending on the manufacturing process. Emissions from petroleum
refineries are primarily from catalytic cracking operations. Sources in the
manufacture of coke include oven charging, door leaks, coke pushing, and
quenching. Emission factors from all of these source categories were rated at
a high level of confidence except coke manufacture, which is based on a small
amount of data from tests conducted in Poland. Other point sources of ammonia
include combustion of fuels such as coal, fuel oil, natural gas, and gasoline.
Emission factors for these sources were assigned intermediate to low quality
ratings because they were based on a small amount of reliable test data.
Several emission factors for fuel combustion were found to originate from
estimates published three decades ago that lack a published basis; despite
this deficiency, these estimates have been repeated in numerous subsequent
reports. Additional data addressing ammonia emissions from combustion sources
that use current technology are needed to properly characterize NH-j emissions.
Area Sources
Area sources, particularly range animal excrement and livestock waste
management, dominate anthropogenic sources of ammonia. To estimate emissions
from these sources, activity data were obtained from the 1982 Census of
Agriculture. Emission factors were developed to reflect the proportion of
4-22
-------
TABLE 4-5. AMMONIA EMISSION FACTORS
Source
Emission
factor
Rating
Combustion Sources
Coal
Fuel oil
Natural gas
utility boilers
industrial boilers
commercial boilers
Mobile sources
highway gasoline
diesel
0.00056 Ib/ton coala
0.8 lb/103 gallons fuel3
a
3.2 lb/106 ft3 gas
3.2 lb/106 ft3 gas"
0.49 lb/106 ft3 gas
0.56 lb/103 gallons fuela
0.95 lb/103 gallons fuela
Livestock Wastes
Beef cattle feedlots (3-02-02-002) 13 Ib/animal marketed13
Livestock waste management
beef cattle 36.9
dairy cattle 36.4
swine 7.4
sheep 4.1
(Ib/animal marketed)
(Ib/animal marketed)
(Ib/animal marketed)
(Ib/animal marketed)
laying hens
broilers
turkeys
0.34 (Ib/animal marketed)
0.04 (Ib/animal marketed)
0.29 (Ib/animal marketed)
Ammonium Nitrate Manufacture
Neutralizer (3-01-027-04, -11, -21)
Solids formation
evaporation/concent rat ion (-17X-27)
high density prill towers (-12)
low density prill towers (-22)
rotary drum granulators (3-01-027-07)
pan granulator (3-01-027-08)
high density prill coolers (-14)
low density prill coolers (-24)
low density prill dryers (-25)
rotary drum granulator coolers (-29)
Wastewater Treatment
Anhydrous Ammonia Fertilizer
Application
18.0 Ib/ton
17.00 lb/tonc
57.2 lb/tonc
0.26 lb/tonc
59.4 lb/tonc
0.14 lb/tonc
0.04 lb/tonc
0.30 lb/tonc
1.6 lb/tonc
1.19 lb/tonc
19 lb/106 gallons
D
E
C
C
C
D
E
E
E
E
E
E
E
E
A
A
A
A
A
A
A
A
A
19 Ib/ton fertilizer
(continued)
4-23
-------
TABLE 4-5 (continued)
Source
Emission
factor
Rating
Petroleum Refineries
FCC units (3-06-002-01)
TCC units (3-06-003-01)
Reciprocating engine
compressors
Ammonia Synthesis
Carbon dioxide regeneration
(3-01-003-08)
Condensate stripping
(3-01-003-09)
54 lb/103 barrels feedd
6 lb/103 barrels feedd
0.2 lb/103 ft3 gasa
2.0 lb/tonc
2.2 lb/tonc
18.2 lb/tonc
Urea Manufacture
Solution formation/ (3-01-040-02)
concentration
Solid formation
nonfluidized bed prilling
(agricultural grade) (3-01-040-08) 0.87 lb/tonc
•fluidized bed prilling
(agricultural grade) (3-01-040-10) 2.9 lb/tonc
(feed grade) (3-01-040-11)
drum granulation (3-01-040-04)
rotary drum cooler (3-01-040-12)
4.1 lb/"tonc
2.2 lb/tonc
0.0051 lb/tonc
Coke Manufacture
Oven charging (3-03-003-02)
Door leaks (3-03-003-08)
Coke pushing (3-03-003-03)
Quenching (contaminated water)
(3-03-003-04)
Ammonium Phosphate
Manufacture (3-01-030-02)
0.02 Ib/ton coal charged
0.06 Ib/ton coal charged
0.1 Ib/ton coal charged
0.28 Ib/ton coal charged
0.14 lb/tonc
B
B
A
A
A
A
A
A
D
D
D
D
aRefers to pounds ammonia emitted per unit of fuel burned.
Defers to pounds ammonia emitted per head of cattle marketed from feedlots,
cRefers to pounds ammonia emitted per ton of product.
^Refers to pounds ammonia emitted per 103 barrels of feed to the cracking
unit.
4-24
-------
confined versus unconfined animals. ^ Two new livestock categories for turkey
and sheep waste management were added to the inventory.
Estimates for anhydrous ammonia fertilizer application were obtained by
multiplying the emission factor by county-level activity data. The activity
data were obtained from the National Fertilizer Development Center in the form
of fertilizer application by state. The application on a county basis was
estimated by dividing the percentage of farmland in each county of the State
by the total fertilizer sold in the State.
The potential for emissions of ammonia for some other source categories
was investigated as part of the NAPAP inventory development effort. The
additional categories were wildlife excrement, forest fires, human
perspiration, human breath and cigarette smoking. Emissions for these
categories were not included in the annual or modelers' inventories. The
decision to exclude these categories from the inventory was based on the high
uncertainty of the emission factors, the lack of reliable activity rate data
or because the calculated emissions were very small on the national level.
The issue of ammonia emissions from wildlife excrement is controversial.
The net contribution of ammonia from wildlife excrement was assumed to be zero
for application to the NAPAP program. This position is in conflict with other
research results that suggest that ammonia emissions from wildlife may be
large.
Wastewater Treatment was added as an area source category for the annual
inventory (see Section 3). Activity data in the form of total wastewater flow
by county were obtained from the 1984 EPA study . Ammonia emissions were
obtained by multiplying the wastewater flow by an emission factor.
Mobile source estimates were obtained by multiplying the total amount of
gasoline sold by an emission factor. For this application, an average
emission factor for leaded and unleaded gasoline was used. The emission
factor was based on the ratio of total sales of leaded versus unleaded
gasoline.
4-25
-------
SECTION A
REFERENCES
1. NEDS Source Classification Codes and Emission Factor Listing. Prepared
by U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards, National Air Data Branch, October 1985.
2. Compilation of Air Pollutant Emission Factors. Volume I? Stationary
Point and Area Sources. AP-42. Fourth Edition
(GPO No. 055-000-00251-7), U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina, September 1985.
3. Stockton, M.B. and J.H.E. Stelling. Criteria Pollutant Emission Factors
for the 1985 NAPAP Emissions Inventory. EPA-600/7-87-015
(NTIS PB87-198735). U.S. Environmental Protection Agency, Air and Energy
Engineering Research Laboratory, Research Triangle Park, North Carolina,
May 1987.
4. Homolya, J.B., Primary Sulfate Emission Factors for the NAPAP Emissions
Inventory. EPA-600/7-85-037 (NTIS PB86-108263), U.S. Environmental
Protection Agency, Air and Energy Engineering Research Laboratory,
Research Triangle Park, North Carolina, September 1985.
5. Shareef, G. and L. Bravo. Air Emissions Species Manual Volume II -
Particulate Matter (PM) Species Profiles. EPA-450/2-88-003b (NTIS
. PB88-225800). U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina. April 1988.
*
6. Klemm, H.A. and R.J. Brennan. Emissions Inventory for the SURE Region, •
GCA Technology Division, prepared for Electric Power Research Institute,
EA-1913, Research Project 862-5, Final Report, April 1981.
7. Rivers, M.E. and K.W. Riegel. Work Group 3B: Emissions, Costs and
Engineering Assessment, U.S./Canada Memorandum of Intent on Transboundary
Air Pollution, Work Group 3B, Final Report, June 1982.
8. Final Report P-3901/G, "Anthropogenic Sources and Emissions of Primary
Sulphates in Canada," January 20, 1982, Ontario Research Foundation.
PG. 57 states acidic sulfate estimated to be 832 of total sulfate; pg. 54
estimates particulate control device efficiencies on sulfates ranges from
30 to 50Z.
9. Misenheimer, D., R. Battye, M.R. Glowers, and A.S. Werner. Hydrogen
Chloride and Hydrogen Fluoride Emission Factors for the NAPAP Emission
Inventory. EPA-600/7-85-041 (NTIS PB86-134020), U.S. Environmental
Protection Agency, Air and Energy Engineering Research Laboratory,
Research Triangle Park, North Carolina, October 1985.
4-26
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10. Abernethy, R.F., F.H. Gibson and W.H. Frederic. Phosphorus, Chlorine,
Sodium, and Potassium in U.S. Coals. Publication No. RI 6579, U.S.
Department of the Interior, Bureau of Mines, Washington, DC 1964.
11. Surprenant, N.F., W. Battye, D. Roeck and S.M. Sandberg. Emissions
Assessment of Conventional Stationary Combustion Systems: Volume V.
Industrial Combustion Sources. EPA-600/7-81-003c (NTIS PB81-225559).
U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina, July 1981.
12. Misenheimer, D.C., I.E.- Warn and S. Zelmanowitz, Ammonia Emission Factors
for the NAPAP Emission Inventory. EPA-600/7-87-001 (NTIS PB87-152336),
U.S. Environmental Protection Agency, Air and Energy Engineering Research
Laboratory, Research Triangle Park, North Carolina, January 1987.
13. Van Dyne, D.L. and C.B. Gilbertson. Estimating U.S. Livestock and
Poultry Manure and Nutrient Production. ESCS-12, U.S. Department of
Agriculture, Washington, DC, 1978. 145 pp.
14. Hoff, J.D., D.W. Nelson and A.L. Sutton. Ammonia Volatilization From
Liquid Swine Manure Applied to Cropland. Journal of Environmental
Quality, 10:90-94, 1981.
15. Westerman, P.W., L.M. Safley, Jr., J.C. Barker and G.M. Chescheir, III.
Available Nutrients in Livestock Waste. Journal of the North Carolina
Research Service, Raleigh, North Carolina, Paper No. 9998, 1985. 15 pp.
16. Peters, J.A. and T.R. Blackwood. Source Assessment: Beef Cattle
Feedlots. EPA-600/2-77-107 (NTIS PB270282), U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, June 1977.
17. Hutchinson, G.L., A.R. Mosier and C.E. Andre. Ammonia and Amine
Emissions from a Large Cattle Feedlot. Environmental Quality,
ll(2):288-293, 1982.
18. U.S. Department of Agriculture, Agricultural Statistics Board Computer
Media Files, Washington DC gives a listing of states for which there is
data.
19. Robbins, J.D.W. Environmental Impact Resulting from Unconfined Animal
Production. EPA-600/12-78-046, U.S. Environmental Protection Agency, Ada
OK. February 1978.
20. Technical Tables to the 1984 Needs Survey Report to Congress: Assessment
of Needed Publicly Owned Wastewater Treatment Facilities in the United
States. EPA-430/9-84-011 (NTIS PB85-172682), U.S. Environmental
Protection Agency, Office of Municipal Pollution Control, Washington, DC,
February 1985.
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SECTION 5
DEVELOPMENT AND QUALITY ASSURANCE OF THE
1985 NAPAP MODELERS' EMISSIONS INVENTORY VERSION 2
BACKGROUND
The emissions estimates included in the 1985 NAPAP Emissions Inventory
Version 2 were used as input to a series of computer programs that convert the
annual totals into a modelers' inventory of speciated, gridded, hourly
emissions data. The modelers' inventory is made up of a series of modelers'
tapes which can be used as input to atmospheric transport models such as the
Regional Acid Deposition Model (RADM). These models, in turn, are used to
simulate source-receptor relationships and ultimately assist in the assessment
of possible control strategies.
Table 5-1 highlights the differences in resolution between the 1985 NAPAP
annual and modeling inventories. While both inventories encompass the same
study area, the resolved inventory is structured as a matrix of 63,000 grid
cells extending from 50 to 125 degrees west longitude and from 25 to 60
degrees north latitude. Annual emissions estimates are disaggregated to the
hourly level for each of 12 different day types. Finally, three of the NAPAP
pollutants (total hydrocarbons, particulates, and oxides of nitrogen) are
disaggregated into constituent species or groups of species which share
similar reaction chemistry.
Data collection for base year 1985 at the level of resolution required by
RADM is precluded by resource and logistical constraints. Instead, annual
data are allocated using statistical representations of the temporal, spatial,
and species distributions of the pollutants in question. These "allocation
factors" are then applied to annual emissions data in a series of computer
programs known as the Flexible Regional Emissions Data System (FREDS). FREDS
also adjusts hydrocarbon emissions data to a consistent basis, performs
quality control checking, and reformats the data to facilitate their use as
RADM inputs.
This section describes the development of the 1985 NAPAP Modelers'
Emissions Inventory Version 2, with an emphasis on the measures undertaken to
ensure the quality of the modelers' inventory. The development and
application of allocation factors are described in greater detail in
Section 6, and the results of data processing for the resolved inventory are
discussed in Section 9.
DEVELOPMENT OF THE 1985 NAPAP MODELERS1 EMISSIONS INVENTORY VERSION 2
The development of the 1985 NAPAP Modelers' Emissions Inventory Version 2
required an analysis of the allocation factors used to develop the 1980 NAPAP
inventory and an assessment of the availability of, and need for, updated
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TABLE 5-1. 1985 NAPAP RESOLVED INVENTORY COVERAGE
Annual
Emissions Inventory
Resolved Modeling
Emissions Inventory
Geographic 48 contiguous States including the District of Columbia and
domain part of Canada
Pollutants/
Species
Resolution
S02, S04~2, CO, TSP,
HC1, HF, NOX, NH3,
VOC, THC
S02, S0^~, CO, TSP (reactive Ca,
Mg, K, Na: 0-2.5 microns, 2.5-10
microns, and total; TSP: 0-2.5,
2.5-6, and 6-10 microns), HC1, HF,
NOX, NO, N02, NH3, VOC, THC, 32
hydrocarbon species classes
(see Tables 6-8 and 6-9)
Temporal
Resolution
Annual
Hourly emissions values for a typical
weekday, Saturday, and Sunday for all
four seasons (12 temporal scenarios
in all; see Table 6-3)
Spatial Point source locations
Resolution specified by latitude
and longitude; area
sources at the county
level
Point and area sources assigned
to grid cells 1/6 degree latitude by
I/A degree longitude (approximately
20 x 20 km)
allocation data. Concurrently, recommendations for improvements and
enhancements to FREDS to accommodate the requirements of the modelers'
inventory were discussed with EPA. Following a two-month series of integrated
tests on the new FREDS code and input files, the 1985 NAPAP Emissions
Inventory Version 1 was developed for U.S. anthropogenic point and area source
data.
Following the development of the 1985 NAPAP Modelers' Emissions Inventory
Version 1, annual emissions data from Canada were made available to NAPAP.
These data, provided by Environment Canada, included information on spatial
and temporal emissions distributions, as well as the supporting information
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required to link Canadian sources to speciation profiles developed for U.S.
data. In addition, the Canadian data included estimates of particulate
emissions for several categories of natural sources including unpaved roads
and wind erosion, which were separated into a 1985 Canadian Natural
Particulate Inventory.
The 1985 U.S. Natural Particulate Inventory was developed from emissions
flux estimates provided by NAPAP Task Group II. Natural particulate data were
provided at the county level for three source categories: unpaved roads, wind
erosion, and dust devils. Appropriate temporal, spatial, and species
allocation factors were derived from information provided by Task Group II and
EPA.
Modifications to allocation methodologies and the FREDS computer code
were made where necessary to accommodate the Canadian anthropogenic emissions
data and the U.S. and Canadian natural particulate data. Following the
completion of all of the input files, the FREDS computer code was executed to
develop the 1985 NAPAP Modelers' Emissions Inventory Version 2. The modelers'
inventory includes the U.S. anthropogenic emissions data (1985 U.S. NAPAP
Emissions Inventory Version 2), the Canadian anthropogenic emissions data
(1985 Canadian NAPAP Emissions Inventory), the U.S. natural source alkaline
particulate emissions data (1985 U.S. Natural Particulate Emissions Inventory)
and the Canadian natural source alkaline particulate emissions data (1985
Canadian Natural Particulate Emissions Inventory).
Quality assurance of the modelers' inventory was based on the quality
assurance of FREDS input files, the FREDS computer code and the annual
emissions inventories. The remainder of this section provides a general
description of the quality assurance/quality control activities that were
completed during the development of the modelers' inventory.
DEVELOPMENT AND QUALITY ASSURANCE OF ALLOCATION FACTORS
Temporal, spatial, and pollutant species allocation factors were
originally developed for processing the 1980 NAPAP Emissions Inventory.
These factors and the methodologies developed for assigning them to emissions
records formed the starting point for the 1985 allocation factor development
effort. The methods, data, and documentation used to develop the 1980 factors
were examined. Limitations were identified; the availability of new data was
investigated; and recommendations for changes to existing allocation factors
were discussed with EPA. In particular, updated hydrocarbon and particulate
speciation data were used to create entirely new NAPAP speciation factors.
Improvements and corrections were also made to the temporal and spatial
factors that were used for the development of the 1980 NAPAP inventory.
The 1985 annual inventory contains county-level emissions estimates for
several area source categories not included in previous inventories. In some
cases, these new source types could be adequately characterized by application
of existing allocation data. When this was not possible, new profiles were
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developed. Additional issues were noted and addressed on a case-by-case
basis.
In addition, to ensure the quality of the modelers' inventory, extensive
quality control checks were performed both on the new factors and those
adopted from the 1980 database, and corrections were made accordingly. The
computer programs used to process the allocation data into a format suitable
for use with FREDS were also tested and checked for errors in logic or data
format. Summaries of these activities are provided in the following
subsections.
Temporal Allocation Factors
Temporal allocation factors were derived primarily from those developed
for the 1980 NAPAP Emissions Inventory development effort. New allocation
profiles were generated for 10 new NAPAP area source categories. Plant level
seasonal operating data were developed from DOE databases for all utilities
and point-specific operating patterns were developed for 56 electric utility
sources from TVA generation data. Additional analyses of the temporal
allocation factors were performed to ensure the internal consistency of the
factors and to evaluate the reasonableness of factors derived from new data
sources.
One significant improvement that affected the quality of all emissions
sources was the conversion of the temporal allocation factors from EBCDIC
files into SAS files. The use of SAS to represent the allocation factors
greatly improves the precision of the individual factors. Following
normalization of the factors and creation of SAS data files, point and area
source factor files were compared with original EBCDIC fil-es to ensure'that no
factors were disproportionately receded. As a final test of the nev files;
computer programs were developed to check that all accuracy errors were
eliminated by normalization and that new factors summed to unity at the
various aggregated levels.
Alternative data sources were evaluated for application to the temporal
allocation of annual point source emissions. During previous efforts to
develop emissions inventories for modeling applications generic temporal
operating patterns were applied to specific sources. For the 1985 NAPAP
Modelers' Emissions Inventory Version 2, the primary source of seasonal
factors was point-specific seasonal throughput data. Because of the
importance placed on the development of this database and the extensive QA/QC
that was performed, these data were considered more reliable than the more
general source (SCO specific seasonal factors.
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Spatial Allocation Factors
Spatial allocation factors were developed to apportion area source
emissions from counties to the NAPAP inventory grid cells. The actual spatial
distributions of emissions are estimated according to the distribution of
surrogate indicators. Fourteen unique spatial distribution surrogates were
available for application to the 1985 NAPAP Modelers' Emissions Inventory
Version 2. The spatial factors were derived primarily from the 1980 NAPAP
inventory development effort. The spatial factor development for the 1985
inventory was primarily a quality assurance effort with a focus on problems
that were noted in the 1980 spatial allocation factors.
Quality control procedures for the spatial allocation factors fell into
three general categories: evaluation of emissions losses in the FREDS Spatial
Allocation Module (SAM); analyses of counties requiring normalization for
spatial surrogates deviating by 20 percent or more; and other analyses which
are discussed below.
The causes of the emissions losses in SAM were evaluated by comparing
county and SCC level emissions totals before and after spatial allocation,
matching state/county codes in the emissions file with the Spatial Allocation
Factor File (SAFF), and tracking specific counties through various stages of
the spatial factor processing. Counties and surrogates identified by the
processing messages from the software that generates the SAFF were analyzed
using grid maps for each state, atlases, and data from various segments of the
spatial factor process. In addition, segmented maps were plotted for
reference which included the locations of various towns. Location data for.
the^se maps were obtained from the National Cartographic Information Center.
Other quality control procedures included close examination of the spatial
software and applications of these programs for other projects.
Quality control checks applied to the 1980 NAPAP—spatial allocation
factors revealed several problems, including missing counties, missing grid
cells, incorrect county code assignments, problems with location data,
inaccurate county to APCD conversions for Massachusetts, and problematic
spatial factor processing algorithms.
Analysis of the FREDS Quality Control Module output revealed six missing
Virginia counties (Alexandria, Chesapeake, Fairfax, Nansemond, Portsmouth, and
Richmond), a problem caused by Virginia's defining independent cities as
county equivalent geopolitical units. Additional analyses identified five
other counties that were missing from the 1980 spatial factor database
(Sabine, LA; Barbour, WV; and Menominee, WI; Cibola, NM and Yellowstone
National Park, MT).
Missing grid cells, incorrect county code assignments and problems with
location data in the census and land use files were discovered by analyzing
the processing messages in the Spatial Allocation Factor Preprocessor (SAFP).
The analysis showed missing grid cells in the land use file for four counties
(Monroe, FL; Alexander, IL; Queen Annes, MD; and Dare, NC). The origin of
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this problem was traced to the county-to-grid file used for the land use data
processing. A portion of Monroe County, FL that falls below the southern
boundary of the NAPAP inventory region was identified. Incorrect county code
assignments were responsible for the loss of census based spatial fractions in
five counties: Saginaw, MI; Lincoln, OR; Linn, OR; Jackson, SD; and
Washabaugh, SD. For Saginaw, Jackson, and Washabaugh Counties, the error
resulted from incorrect code assignments in a file used for census processing.
In Oregon, county codes for Lincoln and Linn were reversed in the land use
county-to-grid file.
Problems with location data resulted in the loss of data in three
counties: Pulaski, VA; Rappahannock, VA; and Adams, WI. For Pulaski, a grid
cell containing a fraction of the census data was dropped when an independent
city assigned to the county was not physically located within the county. In
Rappahannock County, a grid cell was erroneously assigned outside the county
by the census data processing, most likely due to incorrect latitude/longitude
data for a subcounty unit. Census data for Adams, WI were lost due to
incorrect latitude and longitude data on the county and subcounty records.
In Massachusetts, emissions data are reported for Air Pollution Control
Districts rather than by county. Erroneous spatial fractions resulted from
the incorrect county to APCD conversion in the program which merges census and
Landsat data. Other minor problems with algorithms include: calculating
column and row numbers for border grids for a subset of the NAPAP grid,
calculating column and row from grid number, and normalizing county-level
factors not summing to 100 percent.
Corrections for all of the above problems were applied to the spatiaj.
fraction file and the spatial factor preprocessor (SAFP) was used to create a
corrected spatial allocation factor file for input to FREDS for the processing
of the modelers' inventory. Processing messages were evaluated to assure that
all factors were correctly applied.
Speciation Factors
Speciation data for hydrocarbons and particulate matter were reformulated
by NAPAP specifically for application to the modelers' inventory. Hydrocarbon
data were used to construct the input files to PSPLIT, an independent Fortran
program used to create THC speciation factors. Particulate Speciation data
were converted directly to FREDS-compatible format for the Speciation Factor
File (SFF).
Hydrocarbon speciation—-Hydrocarbon speciation data were developed by
NAPAP for use in PSPLIT as a set of four files. These four files include the
hydrocarbon profile file, the species class assignment file and the area
source and point source SCC-profile index assignment files. Each of these
files was subjected to both individual checks and cross checks for
completeness and consistency. The hydrocarbon profile file lists the weight
percents of the component hydrocarbon species included in each of the 323
surrogate hydrocarbon profiles. The profile file was checked for blank
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records, duplicate profile number/species combinations, and missing data.
Records with the same profile number were aggregated to detect profiles whose
species contributions did not sum to 100 percent.
The species class assignment file lists the nearly 600 species that are
included in the speciation profiles and the associated class assignment for
each species. Species file checks included the detection of missing or
duplicate SAROAD codes, the detection of missing or zero molecular weights,
and the analysis of anomalous molecular weights.
The point and area source SCC-profile index assignment files list the
valid NAPAP SCC codes and the speciation profile code assigned to represent
the speciation of VOC emissions. Point and area source SCC-profile index
assignment files were analyzed for invalid data and duplicate SCC occurrences,
as well as the frequency of use of industry-specific or overall average
default hydrocarbon profiles.
File cross-checking was performed to identify any inconsistencies between
data in the separate files. The file cross checks included checking for
SAROAD codes that were listed in the hydrocarbon profile file but absent in
the species class assignment file and checking for profiles that were listed
in the SCC-profile index assignment files but not in the hydrocarbon profile
file.
These checks identified a number of problems and temporary corrections
were made where necessary to permit FREDS testing to proceed. All
inconsistencies were reported to EPA for review and correction. For example,
duplications of species within profiles were observed in early versions of the
.files. Since these duplications did not cause the aggregate weight percent of
the profiles to exceed 100, the individual weight percents for duplicate
entries were summed to produce a total weight percent for each unique species.
Without this modification, the files could not be properly translated by
inventory processing software.
Particulate speciation—Speciation files for particulate matter were also
developed by NAPAP for application to the modelers' inventory. These files
were also subject to both manual and automated quality control checks. The
particulate speciation database consists of several independent files
including a profile-species listing and point and area source SCC-profile
index files. In addition, the PM database contains a fraction file that
indicates the fraction of total particulate falling within selected size
ranges for each profile and a reactivity file that specifies the reactive
fraction of the various alkaline dust species.
With the exception of the point and profile data, the particulate
speciation files were small enough to permit manual inspection, which
identified a number of minor inconsistencies. The profile file was reduced to
the species data of interest to permit careful screening of the fractions to
be used in the NAPAP inventory development efforts. The particulate matter
speciation profiles were checked for completeness and accuracy. The completed
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point and area source Speciation Factor Files were also checked against
original input data to ensure the accuracy of the transformation process.
As an additional check of the particulate speciation profiles, the
speciation fractions were applied to an early version of the 1985 annual point
source data. Two problems were identified as a result of this analysis.
First, a total of 195 SCCs present in the NAPAP point source file were not
included in the SCC-profile assignments. Speciated particulate emission
estimates could not be obtained for sources with these SCC codes. In
addition, approximately 16 point source SCC codes were assigned to more than
one profile. These problems were addressed and corrected in subsequent file
updates.
A number of supplementary modifications to the PM data files were
required following the receipt of the final data. These modifications
included removal of duplicate observations and addition of profile assignments
for new area source categories 66 through 70.
During preliminary FREDS executions, Speciation Module output revealed a
previously undetected problem with PM speciation data which required
correction prior to continued FREDS processing. NAPAP totals for speciated
particulates indicated that the mass of emissions within the fine fraction (0
to 2.5 micron) exceeded the total mass for the same alkaline component.
Analyses identified 20 of the 159 PM profiles in which the sum of the 0 to 2.5
micron fraction and the 2.5 to 10 micron fraction was greater than the
fraction designated as total mass. Further examination of the profiles
indicated that the problem was associated with the representation of the total
species fraction. In the absence of an algorithm with which to recalculate
the fraction,' the inconsistent total species values were replaced with
fractions representing the minimum possible total species value based on the
sum of the mass in the smaller size fractions. For 15 of the 20 profiles, the
0 to 10 micron fraction was used; the remaining five profiles were assigned
minima based on the sums of the two smaller fractions. A new Speciation
Factor File was then created using the new total mass fractions.
FREDS ENHANCEMENTS AND QUALITY CONTROL MEASURES
The FREDS was originally created to process the final version of the 1980
NAPAP Emissions Inventory. Following this effort, improvements to FREDS were
identified for application to the 1985 NAPAP Modelers' Emissions Inventory
Version 2. This analysis formed the basis for the FREDS enhancement effort,
described in more detail in Section 9. As the annual inventory format and
requirements for the modelers' inventory became better defined, additional
modifications were made.
FREDS Quality Control Module
One of the enhancements to the FREDS software for the 1985 emissions
inventory development effort was the incorporation of additional quality
control measures for intermediate emissions files. Since the inclusion of new
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quality control procedures in each module entailed a significant amount of
receding, an independent module capable of accepting and checking an emissions
file at any stage of FREDS processing was developed. This module was designed
to supplement the checks carried out within the other FREDS modules.
The Quality Control Module (QCM) is a SAS program which calculates
emissions sums for any specified stage of FREDS processing and compares them
to sums calculated from the emissions input to FREDS. A diagnostic report is
printed which indicates any discrepancies found in the data. The emissions
sums checked by QCM include national and state level sums of the ten major
pollutants (S02, S04, NOX, CO, HCl, HF, NH3, TSP, VOC, and THC), as well as
national sums of these ten pollutants for" 20 user-selected source categories.
QCM also checks seasonal, daily, and hourly temporal allocation factors, when
present, to ensure that they sum to within user-specified tolerances.
Pollutant tolerances are defined relative to the magnitude of the
corresponding baseline emissions input to FREDS. For example, a tolerance
factor of 1 x 10 specified for S(>2 indicates that the difference between the
baseline total of SC^ and the calculated total of S02 should not be greater
than 1 x 10 times the baseline total of SC^ • Tolerances specified for
temporal allocation factors define how much allocation factor totals may vary
from unity. Diagnostic reports from QCM include error messages which allow
the user to identify the source and magnitude of each error.
QCM diagnostics were designed to complement the error trapping features
of the other FREDS modules, as well as detect inconsistencies in the emissions
data, allocation factor files, and FREDS program code. Most FREDS modules are
equipped to detect and remove observations which fail certain basic criteria
(e.g., missing time zone data or speciation profile). These orphan records
are retained in separate files, and orphan emission totals are checked against
QCM findings to ensure that they represent the only source of emissions
losses. If not, more detailed analyses are undertaken to determine if the
problem lies in the emissions data set, a peripheral file, or the FREDS code
itself. For example, during area source processing, a record was orphaned in
the Spatial Allocation Module. Emissions losses reported in QCM matched the
emissions from the orphaned record which was determined to have been correctly
removed. However, QCM also detected discrepancies in the TAM output files
even though there were no orphan records from this module. A detailed
analysis was undertaken, which revealed that the discrepancy was caused by
rounding to four significant figures within the TAM program code (see
Section 9 for additional details).
FREDS Preview Program
The creation of the modelers' inventory from the NAPAP annual inventory
files is a time and resource intensive process. It is desirable that all
FREDS processing steps proceed as smoothly as possible to limit the number of
steps that must be repeated. To this end, a computer program was developed to
screen the annual point and area source emissions data and supporting files
prior to the initiation of FREDS processing. By diagnosing and correcting
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problems prior to starting FREDS, time and computer resources can be
optimized.
In brief, the preview program performs stepwise checks of the annual file
and relevant factor files using streamlined versions of algorithms employed by
individual FREDS modules. By match-merging data from appropriate files, the
program screens for the following:
Duplicate process-level_(SCC or source category) records
States outside the NAPAP study area (e.g., Alaska, Hawaii, Guam)
Missing time zone data
Missing temporal factors and/or incomplete operating schedule data
Missing location data
Row and column values outside the NAPAP grid
SCC or source category with no corresponding speciation factors
Outputs include a summary report listing numbers of records which failed
each check, as well as problem specific data sets which make flagged records
available for further analysis.
QUALITY CONTROL OF THE MODELERS1 INVENTORY
It is clear that the quality of data in the 1985 NAPAP Modelers'
Emissions Inventory Version 2 is dependent on both the quality of the various
input files and the accuracy of the methodology by which the data are
manipulated (FREDS software). Hence, the quality control procedures mentioned
above all serve a vital role in achieving and maintaining a high standard of
quality in the final inventory. However, analysis of the first resolved
inventory (Version 1.0) was also employed to examine more subtle aspects of
the data.
The spatial distribution of emissions were analyzed using a series of
grid level maps encompassing the NAPAP study area. This examination
identified a number of point sources erroneously located off land. More
refined analyses were subsequently undertaken to identify and correct sources
whose coordinates placed them outside their county boundaries.
Additional grid level examinations of Canadian area source data
identified a problem with the conversion of Canadian spatial factors from
latitude/longitude to row/column format. Lack of precision in reported
longitude, coupled with an algorithm that truncates fractions when calculating
row numbers, caused emissions data to be allocated to incorrect grid rows.
The spatial fractions for Canada were regenerated using modified algorithms
and rechecked to ensure proper grid representation.
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SECTION 5
REFERENCES
1. Sellars, P.M., T.E. Fitzgerald, Jr., J.M. Lennon, L.J. Maiocco, N.M.
Monzione, and D.R. Neal, Jr. National Acid Precipitation Assessment
Program Emission Inventory Allocation Factors. EPA-600/7-85-035 (NTIS
PB86-104247). U.S. Environmental Protection Agency, Research Triangle
Park, NC. September 1985.
2. Demmy, J.L., W.M. Tax, and T.E. Warn. Area Source Documentation for the
1985 National Acid Precipitation Assessment Program Inventory.
EPA-600/8-88-106 (NTIS PB89-151A27). U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1988.
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SECTION 6
DEVELOPMENT OF TEMPORAL, SPATIAL, AND SPECIES ALLOCATION FACTORS
INTRODUCTION
Allocation factors were originally developed to process the 1980
NAPAP Emissions Inventory into a resolved modeling inventory. In response to
changing modeling requirements, the focus and resolution of the NAPAP factors
were expanded for processing the 1985 NAPAP Modelers' Emission Inventory
Version 2 (e.g., the addition of particulate size classes). In addition, to
assure the quality of the resolved emissions estimates, extensive quality
control checks were performed on the new factors as well as those adapted from
the 1980 NAPAP database. The remainder of this section documents the
development of the various allocation factors for the 1985 NAPAP U.S. and
Canadian anthropogenic point and area source inventories. Information on
allocation factors specific to the Canadian anthropogenic inventory and the
U.S. and Canadian natural source inventories is provided in Sections 7 and 8,
respect ively.
TEMPORAL ALLOCATION FACTORS
Form and Use of Temporal Allocation Factors
The emissions totals reported in the annual inventory were resolved
temporally to support NAPAP modeling applications. The temporal allocation
strategy was to apportion annual emissions totals into 24 hourly totals for a
typical weekday, Saturday or Sunday in each of the four season's -of the year.
The NAPAP temporal allocation factors were developed to accomplish this
resolution.
The temporal allocation factors include three sets of fractional
multipliers, applied to the NAPAP annual emissions records in sequence as
follows:
1. Four seasonal factors divide the annual total into four
subtotals representing emissions in each of the four seasons.
2. Three daily factors per season divide each seasonal total into
three subtotals representing emissions for a typical weekday,
Saturday and Sunday in each season.
3. Twenty-four hourly factors per day divide each daily total into
24 subtotals representing emissions for each hour of that day.
After this final step, the annual emissions total has been
divided into 288 subtotals (four seasons x 3-day "types'Vseason
x 24 hrs/day "type").
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The seasonal multipliers for each record sum to unity, as do the hourly
multipliers for each season/day type combination. Since daily emissions
totals represent emissions for one typical weekday, Saturday or Sunday in each
season the governing equation for daily allocation factors is:
(65 x weekday factor) + (13 x Saturday factor)
+ (13 x Sunday factor) = 1 (6.1)
where a season is defined as 91 days (13 weeks). For the purposes of the
NAPAP inventory, the four seasons are defined as:
Season Months
Winter December, January, February
Spring March, April, May
Summer June, July, August
Fall September, October, November
All temporal factors are defined according to local time (e.g., the
factor for "Hour 1" corresponds to the period between midnight and 1:00 a.m.,
local time). However, modeling applications require that these factors be.
offset to Greenwich Mean Time (GMT) to produce standardized allocation for all
U.S. sources. Offset to GMT is*accomplished in the Temporal Allocation Module
(TAM) of FREDS, which integrates local factors with time zone data to produce
a standardized set of factors. Where appropriate, additional data adjust the
factors to compensate for Daylight Savings Time during the appropriate
seasons.
Development of Temporal Allocation Factors
Temporal allocation factors were developed for all U.S. NAPAP area source
categories. For most point sources, temporal allocation of emissions was
accomplished using plant- and point-specific operating data contained in the
NAPAP inventory; however, explicit state- and SCC-specific factors were
developed for a subset of all U.S. electric utilities. The factors used in
the modelers' inventory were derived from previous studies, earlier NAPAP
inventories, and new research. Many of the factors were based upon data from
the Northeast Corridor Regional Modeling Project (NECRMP).1 In the NECRMP
effort, temporal patterns were developed for point and area sources of
emissions in 15 states in the eastern United States. In most cases, the
NECRMP factors were retained for the NECRMP states. Other factors were often
developed for states outside the NECRMP study area.
6-2
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U.S. Area Source Factor Development—
Temporal allocation factors were developed for the original 88 area
source categories in the 1980 NAPAP Emissions Inventory. Many of these
factors were based on factors developed for the NECRMP effort. Others were
derived from new research conducted for successive versions of the 1980 NAPAP
Emissions Inventory.
The 1980 NAPAP temporal allocation factors for area sources were
developed at several different levels of resolution. The factors for a given
area source category applied either to a single state, a group of states, or
to the entire nation. In determining the appropriate resolution of a given
category, the relative contribution of the category to total emissions, as
well as the availability of data, was taken into account.
All area source temporal allocation factors used in the 1980 NAPAP effort
were retained for processing of the 1985 area source inventory. The addition
of several new emissions source categories necessitated the development of new
temporal profiles. Some of these profiles were derived from similar, existing
area source factors. Others were based on analyses of operating schedule data
from analogous point source categories. A description of the development of
the area source temporal allocation factors for each category or set of
categories follows. Source categories added for the 1985 NAPAP inventory are
identi fied.
Residential Fuel Combustion (Categories 1-6)—Monthly average heating
degree days for a representative meteorological recording station from each
state were obtained from State, Regional, and National Monthly and Seasonal
Heating Degree Days Weighted by Population.^ The seasonaltemporalfactors for
each state- were developed from these data." The hourly variations in
residential fuel use were developed with data from NOAA. Monthly averages of
the 3-hour meteorological records were obtained for each meteorological
station to determine these diurnal patterns. For each month, the. average
temperature was subtracted from 18.7°C, (the value used to calculate degree
days). Negative values, indicating temperatures above 18.7°C, were set to
zero. The resulting eight values were proportional to the variation in
diurnal heating for a selected station for a month. Months were then averaged
to obtain seasonally adjusted diurnal factors for each State.
Commercial/Institutional Fuel Use (Categories 7-12)—Seasonal, daily, and
hourly fuel use patterns for these categories were developed from EPA
Guidelines. For the NECRMP study area, daily and hourly patterns were
developed from data collected for the Philadelphia AQCR Inventory.
Industrial Fuel Use (Categories 13-20)—National seasonal patterns were
developed from the EPA Guidelines0,in which a uniform distribution is
recommended. The daily pattern was based on U.S. Bureau of Laboi- statistics
on average overtime at manufacturing facilities. The assumption was made
that all overtime hours were worked on weekends, and that three times as much
overtime was worked on Saturdays as on Sundays. The daily patterns developed
6-3
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from these data indicate that approximately 93 percent of emissions occur on
weekdays, 5 percent of Saturdays, and 2 percent on Sundays. This distribution
varied slightly depending on the season. The hourly pattern was developed
during the Philadelphia AQCR inventory effort. In the Philadelphia study,
SO percent of industrial coal and oil fuel use emissions were allocated
uniformly from 7 a.m. to 4 p.m., to reflect greater production during business
hours. The remaining 50 percent of emissions were uniformly allocated to the
remaining hours in a day. This pattern was applied nationwide to all
industrial fuel use categories in the NAPAP inventory.
Onsite Incineration and Open Burning (Categories 21-26)—Seasonal, daily,
and hourly patterns were developed from existing inventories. o»'»°,9,10
Highway Vehicles, Light and Medium Duty (Categories 27-34)—The U.S.
Department of Transportation (USDOT) collects Continuous Traffic Count (CTC)
data from all SO states, and uses a subset of these data representing
13 states as a basis for estimating national traffic patterns. Data from
these 13 states allow hourly temporal allocation factors to be derived for
each day of the week in each month of the year for six roadway types. This
data set was obtained from the Federal Highway Administration U.S. DOT, and
analyzed to yield seasonal, daily, and hourly temporal allocation factors for
light and medium duty vehicles.
Highway Vehicles, Heavy Duty (Categories 35-38, 40-43)—These eight
categories represent gas and diesel heavy duty vehicles on limited access,
urban, suburban, and rural roads. The NECRMP uniform seasonal pattern was
used for NAPAP after its applicability was confirmed by the U.S. Trucking
Association.
Off-Highway Vehicles (Categories 39, 44)—State-specific seasonal
emission patterns were derived from data contained in Highway Statistics.
Monthly distribution of off-highway motor fuel use was calculated by ~
subtracting monthly on-highway fuel use from total monthly fuel use. Seasonal
allocation factors were then derived from the monthly pattern.
Daily patterns were derived from EPA Guidelines, and are a composite
(weighted by emissions strength) of the daily patterns for the five
subcategories of off-highway vehicles (agricultural equipment, construction
equipment, industrial equipment, lawn and garden equipment, and motorcycles)
for which factors are separately described in the Guidelines.
Separate hourly patterns were derived for gasoline (Category 39) and
diesel (Category 44) vehicles using data from a variety of sources, including
the EPA Guidelines, the Regional Air Pollution Study^^ and several statewide
area source emission inventories. »
Railroads (Category 45)—The temporal factors developed for railroad
locomotives in the NECRMP study were based on information provided by Conrail
and in the Philadelphia AQCR inventory. These data are probably not
representative of the entire nation. Thus, the NECRMP-developed temporal
6-4
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profile for railroads was used only for those states within the NECRMP study
area; new seasonal, daily, and hourly patterns, developed from factors in the
EPA Guidelines, were used for the remaining states in the NAPAP inventory.
Aircraft (Categories 46-48, 56)—For civil aircraft, seasonal, daily, and
hourly patterns were derived from the EPA Guidelines. For military aircraft,
patterns were derived from the Philadelphia AQCR Inventory. For commercial
aircraft, patterns were derived from data presented in Seasonally Adjusted
Traffic and Capacity.
Vessels (Categories A9-52)—Patterns for coal- (Category 49), diesel-
Category 50), and residual oil - (Category 51) powered vessels were developed
from the EPA Guidelines. For gasoline powered vessels (predominately
pleasure boats), the Guidelines recommend that seasonal patterns be based on
the number of months the mean temperature exceeds 45°F: provided this
information, and seasonal patterns were created from information available in
the document State, Regional, and National Monthly and Annual Average
Temperatures Weighted by Area^.Daily and hourly patterns were developed from
the Guidelines.
Gasoline Marketed (Category 54)—The primary sources of information for
seasonal patterns were the U.S. DOE Petroleum Marketing Monthly ^ and the U.S.
DOT's Highway Statistics, I960.11- Analyses of these data yielded the
conclusion that evidence was insufficient to justify using other than a
uniform seasonal distribution. Daily and hourly patterns were based on the
EPA Guidelines.^
Unpaved Roads (Category 55)—The seasonal, daily, and hourly patterns for
this category were assigned those for Light Duty Vehicles on rural roads
(Category*28).
Agricultural, Structural, and Forest Fires (Categories 60, 61, 62, 64)—
Managed burning (Category 61) and agricultural field burning (Category 62)
used the patterns developed for field/slash burning in the NECRMP study.*
Structural fires (Category 64) and forest fires (Category 60) were assumed to
occur randomly, 7 days per week, 24 hours per day. Structural fires were
assumed to occur uniformly throughout the year. It was estimated that
90 percent of forest fires occur during summer or fall, and that the remaining
10 percent are split evenly between winter and spring.
Ammonia Emissions from Vehicular Sources (Categories 66-68) (Added for
1985 NAPAP Inventory.)—These categories contain ammonia emissions for
three vehicle types: light-duty gasoline, heavy-duty gasoline, and heavy-duty
diesel. Temporal profiles were taken directly from corresponding urban
vehicular categories for which emissions of other pollutants were reported
(Categories 30, 38, and 43), respectively).
Livestock Waste Management (Categories 69-75) (Categories 69 and 70 added
for 1985 NAPAP inventory.)—Agricultural Extension Agents in 12 states across
the country (Indiana, Wisconsin, Nebraska, Kansas, North Dakota, Idaho, Iowa,
6-5
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North Carolina, Alabama, New York, Texas and Arkansas) were contacted; they
provided information on the seasonal pattern of manure application in their
states. These patterns were extended to nearby states not contacted directly.
Daily patterns were governed by the assumption that most farm activity takes
place on weekdays and Saturdays; hourly patterns assume this activity takes
place primarily during daylight hours.
For the 1985 effort, emissions for turkey and sheep waste management
(Categories 69 and 70) were provided in addition to the livestock categories
reported previously (Categories 71-75). Temporal profiles for these new
sources were assumed to be similar to those used for other livestock waste
management categories, and were copied directly from Category 71 (beef cattle
waste management).
Fertilizer Application (Category 76) — The 12 agricultural extension
agents contacted to provide information on manure spreading practices (see
above) were also asked to share their knowledge of NH^ fertilization
practices. Daily and hourly factors were assigned under the same assumptions
which governed the selection of factors for the manure spreading categories
(see above).
Beef Cattle Feed Lots (Category 77) — Emissions were assumed to be uniform
throughout the year and the week.
Solvent Use Categories (Categories 78-95) — U.S. Department of Labor
statistics on 1980 working hours0 were consulted to derive seasonal and daily
temporal variation in these emission categories. These data were broken down
by SIC code; the NAPAP emission categories were matched with the most
appropriate code in the %data set. Monthly data on total hours worked served
as the basis for daily factors; the assumption was made that all overtime
hours were worked on weekends, and that three times as much overtime was
worked on Saturdays as on Sundays.
Utility Point Source Emissions (Categories 96-98) — The seasonal factors
were based on electric generation data contained in the EPRI Regional
Systems . ^° Daily factors are governed by the assumption that utility activity
is constant during the week; hourly factors assume that most activity around
these plants occurs during normal working hours.
Minor Point Sources (Category 99) — The temporal variation for this minor
emissions category assumes an operating "schedule" of 52 weeks per year,
5 days per week, and 8 hours per day.
Miscellaneous VOC Categories 100-109 )— (Added for the 1985 NAPAP
inventory). County-level VOC emissions estimates were calculated for
application to the 1985 NAPAP Emissions Inventory Version 2 for several
categories which have not previously been included as area source categories.
Some of these categories, such as Bakeries and Synthetic Fiber Manufacturing,
were included to reconcile the difference between the total emissions reported
in the National Air Pollutant Emissions Estimates 19AO-198A, 17 and the
6-6
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emissions already accounted for by the NEDS point source data files. The
remaining categories such as Publicly-Owned Treatment Works (POTWs) and
Hazardous Waste Treatment, Storage, and Disposal Facilities (TSDFs) were
included due to the difficulty of measuring emissions from specific points
within these categories (e.g., aeration basins).
To develop temporal profiles for these ten categories, operating schedule
data for similar point source categories in the NAPAP category were analyzed.
As a result, it was found that all but two of the new categories were best
represented by uniform profiles. For the remaining two categories (101 and
105), factors were derived from mean operating schedule data for similar
categories in the point source file. Data included seasonal throughput
percentages, as well as hours per day and days per week of process operation.
U.S. Point Source Factor Development—
Point-specific temporal allocation factors were developed for a
subset of all electrical generation sources. Operating schedule data
contained in the emissions records themselves formed the basis for the
temporal allocation of emissions for the remaining point sources
in the inventory.
Point Source Temporal Factors for Electric Utilities—Because of the
importance of electric power plants to total U.S. emissions of S02 and NOX,
and because detailed data are available for these sources, special temporal
factors were developed for electric utilities. Process-level (fuel- and
state-specific) profiles 'were created for the 1980 NAPAP point source
inventory, primarily on the basis of existing NECRMP factors. During the 1985
effort, 58 point-level profiles were added to the temporal factor file.
Point source temporal factors were originally developed during the NECRMP
study for power plants within the Northeast Corridor. In development of the
1980 NAPAP Emissions Inventories, most of the NECRMP factors were retained,
while some were replaced by new factors derived from additional data. For
sources outside the NECRMP region, some factors were based on NECRMP figures;
however, most were developed using other data sources.
Seasonal factors for the temporal distribution of point source emissions
were developed on a fuel- and state-specific basis for facilities in the
NECRMP study area, using information from the U.S. DOE's 1979 Data Reports.^
Daily factors were developed at the national level from weekly load cycle
listings in the EPRI Regional Systems. ^ These daily factors were calculated
by normalizing the averages of the daily load statistics contained in the EPRI
report. The factors, which were compiled on a season-specific basis, assume
75-76 percent of weekly emissions are produced on weekdays, 12-13 percent are
produced on Saturdays, and the remainder are generated on Sundays.
Fuel- and state-specific weekday hourly patterns for utility operation
were also developed during the NECRMP effort. * Profiles were derived from
6-7
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hourly power plant fuel use data collected during the development of the SURE
inventory. The SURE database included hourly fuel use data for approximately
300 power plants within the SURE region (the eastern portion of the United
States). These data were collected for several study periods in 1977-1978 and
included data for each season. Fuel- and season-specific fuel use patterns
were averaged by state, then normalized to generate the NECRMP hourly
patterns. Since there were no hourly fuel use data available for Rhode Island
or Vermont, weekday factors were assigned from Connecticut and New Hampshire,
respectively. Outside the Northeast Corridor, hourly factors for weekdays
were developed by taking averages of the NECRMP values. Since no weekend
hourly factors were developed during the NECRMP study, national Saturday and
Sunday hourly profiles were developed from weekly load cycle listings in EPRI
Regional Systems. These profiles were calculated by normalizing the averages
of hourly load statistics.
Continuing efforts to enhance the accuracy of temporal allocation for
electric utility sources led to the addition of point-level temporal profiles
for some utility sources in the 1985 NAPAP point source inventory. Profiles
were develop on the basis of the generation data obtained from 58 TVA
coal-fired boilers. Hourly generation values were used to calculate average
seasonal, daily, and hourly factor surrogates.
Operating Data-Derived Factors—Most NAPAP point source emission records
contain operating schedule data which make possible the point-specific
temporal allocation of emissions. These data consist of information on:
• Seasonal 'throughput percentages
• Days/week of process operation and
• Hours/day of process operation
This information is used to temporally apportion point source data for
which specific temporal factors were not developed.
Seasonal factors were taken directly from seasonal throughout
percentages. Daily factors were derived from the number of days per week
which a process operates. Factors were calculated according to the following
schedule:
If the process operates
x days/week
1
2
3-5
6
7
Emissions are allocated as follows
Saturdays only
Equally on Saturdays and Sundays
Equally on weekdays only
Equally on weekdays and Saturdays
Equally on all days of the week
6-8
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Hourly factors are based on the number of hours per day which a process
operates. Factors were developed using the following method:
If the process operates
x hours/day Emissions are allocated as follows
1-17 Zero for midnight to hour 7
(0600-0700 A.M.)
Equally for x hours beginning with
hour 8 (0700-0800 A.M.)
Zero for hours remaining before
midnight.
>17 Equally among 24 hours of day.
During development of the 1985 point source inventory, extensive updates were
made to seasonal throughput percentages on point source emissions records.
New data were developed from U.S. DOE databases that were made available to
NAPAP. Data from Energy Information Administration Form 759 were the main
source of seasonal activity information at the plant-fuel or state-fuel level.
These data were supplemented with State-fuel data from unpublished monthly
reports developed by DOE ("R080 Report on Consumption").
Temporal Allocation Factor File Formats
»
The temporal allocation factor files represented are organized as SAS
data sets.
The 12 temporal scenarios in the 1985 NAPAP Modelers' Emissions Inventory
Version 2 are listed in Table 6-1. For area sources, the allocation factor
file contains 12 records per SCC - one for each day type. For point sources,
all 12 scenarios are processed together by the FREDS software, and are thus
accommodated by a single record. Hence, each record in the point source
factor file contains 12 seasonal factors, 12 daily factors, and 288 (12 x 24)
hourly factors.
SPATIAL ALLOCATION FACTORS
Background
The NAPAP grid system covers the contiguous United States and part of
Canada, extending from 50 to 125 degrees west longitude and from 25 to
60 degrees north latitude. Figure 6-1 illustrates the NAPAP grid. It should
be noted that this figure depicts 2/3 degree latitude by 1 degree longitude
grids for visual clarity. The actual grids employed are 1/6 degree latitude
by 1/4 degree longitude (approximately 20 x 20 km).
6-9
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TABLE 6-1. 1985 NAPAP EMISSIONS INVENTORY TEMPORAL ALLOCATION SCENARIOS
Scenario number
(Variable name MUM DAY) Scenario
1 Winter Weekday
2 Winter Saturday
3 Winter Sunday
4 Spring Weekday
5 Spring Saturday
6 Spring Sunday
7 Summer Weekday
8 Summer Saturday
9 Summer Sunday
•
10 Fall Weekday
*
11 Fall Saturday
12 Fall Sunday
Spatial allocation factors were developed for the 1980 NAPAP inventory to
apportion area source emissions from counties to individual grid cells as
required for modeling applications. The goal of the NAPAP spatial allocation
factor development effort was to create as many surrogate values as possible
for each county, allowing the user maximum flexibility in assigning county-
level emissions to specific grid cells. These surrogates are used to estimate
the subcounty distribution of area source emissions.
Fourteen surrogate indicators were developed for use with the NAPAP
inventory based on housing, population, and land use data. The categories and
sources of data are summarized in Table 6-2. Once the subcounty distribution
of each surrogate indicator was determined, area source emissions categories
were matched to the most appropriate surrogates. The resultant file, the
Spatial Allocation Factor File (SAFF), is input to the Spatial Allocation
6-10
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6-11
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TABLE 6-2. SPATIAL ALLOCATION SURROGATES AVAILABLE IN THE
1985 NAPAP SPATIAL ALLOCATION FACTOR FILES
Surrogate
Indicator No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Surrogate Indicator
Population
Housing
Urban Land
Agricultural Land
Range land
Deciduous Forest
Coniferous Forest
Mixed Forest and Forested Wetland
Water
Barren Land
Nonforested Wetland
Mixed Agricultural Land and Rangeland
Composite Forest
Land Area
Source
1980 Census
1980 Census
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
Landsat
EPA/Alliance
Note: The Landsat data are for 1972-1973.
6-12
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Module of the Flexible Regional Emissions Data System (FREDS) so that area
source emissions may be spatially distributed.
Development of Spatial Allocation Factors
The development of spatial allocation factors for application to the 1980
NAPAP Emissions Inventory ' was based on two main sources of data: U.S.
Department of Commerce, Bureau of the Census, Census of Population and Housing,
1980, and land use/classification data derived from 1972-1973 Landsat satellite
imagery and land use/cover maps. Separate software was created to process each
type of data. Once spatial fractions were generated from the census and Landsat
data, the files were merged to form a data set containing state, county, grid
column and row, and 14 spatial surrogates for each record. Column and row
numbers begin at the southwest corner of the grid system (e.g., 1,1) and
increase to the north and east. Data processing up to this step utilized the
National Computer Center's (NCC) Sperry UNIVAC. After the Census and Landsat
data were merged, the data were transferred to the NCC's IBM 3090.
Once on the IBM, spatial fractions for land use and census surrogates
were matched to area source categories in the Spatial Allocation Factor
Preprocessor (SAFP) using a surrogate selection file. SAFP writes an EBCDIC
file which is compatible with the Spatial Allocation Module of FREDS.
Spatial allocation factors for the 1985 NAPAP inventory are based on those
developed for the 1980 effort. Extensive quality control checks were performed
on the 1980 spatial factors to assure the quality of the spatially-resolved 1985
area source data. Based on the results of the quality control procedures,
several adjustments were made to the spatial factors for use with the 1985 NAPAP
inventory. Additional enhancements to the file were warranted due to the
development of new area source categories and the presence of a new county Iti
New Mexico.
For 1985 NAPAP applications, 6 of the 14 surrogates are used for spatial
allocation. Table 6-3 lists the spatial surrogates and their assignment to each
U.S. area source category.
Grid Description—
The grid system defined for U.S. spatial factor development is made up
of 37,440 grid cells (156 rows, 240 columns) each 1/6 degree latitude by 1/4
degree longitude (approximately 20 x 20 km). The boundaries extend from 65 to
125 degrees west longitude and from 25 to 51 degrees north latitude. The cell
at the southwestern corner of the grid is denoted (1,1), with column numbers
increasing west to east, and rows from south to north. Column and row numbers
are calculated from latitude and longitude as follows:
6-13
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TABLE 6-3. SPATIAL ALLOCATION SURROGATES FOR U.S. AREA SOURCES
Category
10
001
002
003
004
005
006
007
008
009
010
011
012
013
014
015
016
017
018
019
020
021
022
023
024
025
026
027
028
030
031
032
034
035
036
038
039
040
Surrogate
10
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
3
3
2
3
3
14
14
3
14
14
3
14
14
3
14
14
Surrogate
Indicator
Hous i ng
Hous i ng
Housing
Housing
Housing
Hous i ng
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Urban Land
Housing
Urban Land
Urban Land
Housing
Urban Land
Urban Land
Land Area
Land Area
Urban Land
Land Area
Land Area
Urban Land
Land Area
Land Area
Urban Land
Land Area
Land Area
Emissions Category Description
Residential Fuel - Anthracite Coal
Residential Fuel • Bituminous Coal
Residential Fuel • Distillate Oil
Residential Fuel - Residual Oil
Residential Fuel - Natural Gas
Residential Fuel - Wood
Commercial/Institutional Fuel • Anthracite Coal
Commercial/ Institutional Fuel - Bituminous Coal
Commercial/Institutional Fuel - Distillate Oil
Commercial/ Institutional Fuel - Residual Oil
Commercial/Institutional Fuel • Natural Gas
Commercial/Institutional Fuel - Wood
Industrial Fuel - Anthracite Coal
Industrial Fuel - Bituminous Coal
Industrial Fuel - Coke
Industrial Fuel - Disti Hate Oi I
Industrial Fuel - Residual Oil
Industrial Fuel - Natural Gas
Industrial Fuel - Wood
Industrial Fuel - Process Gas
On-Site Incineration • Residential
On-Si'te Incineration - Industrial
On-Site Incineratioij - Commercial/Institutional
Open Burning - Residential
Open Burning - Industrial
Open Burning - Commercial/ Institutional
Light Duty Gas. Veh. - Limited Access Roads
Light Duty Gas. Veh. - Rural Roads
Light Duty Gas. Veh. - Urban Roads
Light Duty Gas. Trucks - Limited Access Roads
Light Duty Gas. Trucks - Rural Roads
Light Duty Gas. Trucks - Urban Roads
Heavy Duty Gas. Veh. - Limited Access Roads
Heavy Duty Gas. Veh. - Rural Roads
Heavy Duty Gas. Veh. - Urban Roads
Off -Highway Gasoline Vehicles
Heavy Duty Diesel Veh.
(continued)
6-14
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TABLE 6-3. (Continued)
Category Surrogate Surrogate
ID ID Indicator
Emissions Category Description
041
043
044
045
046
047
048
049
050
051
052
053 *
054
056
060
061
062
064
066
067
068
069
070
071
072
073
074
075
076
077
078
079
080
14
3
14
3
1
1
1
1
1
1
1
1
1
14
13
13
4
2
14
14
14
4
4
4
4
4
4
4
4
4
1
1
1
Land Area
Urban Land
Land Area
Urban Land
Population
Population
Population
Population
Population
Population
Population
Population
Population
Land Area
Composite Forest
Composite Forest
Agricultural
Housing
Land Area
* Land Area
Land Area
Agricul tural
Agricul tural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Population
Population
Population
Land
Heavy Duty Diesel Vehicles -
Heavy Duty Diesel Vehicles -
Off-Highway Diesel Vehicles
Railroad Locomotives
Aircraft LTOs - Military
Aircraft LTOs • Civil
Aircraft LTOs - Commercial
Vessels - Coal
Vessels • Diesel Oil
Vessels - Residual Oil
Vessels - Gasoline
Solvents Purchased
Gasoline Marketed
Unpaved Airstrip LTOs
Forest Wild Fires
Managed Burning - Prescribed
Agricultural Field Burning
Structural Fires
Rural
Urban
Roads
Roads
Light Duty Gas Vehicles (ammonia)
Light Duty Gas Trucks (ammonia)
Heavy Duty Gas Trucks (ammonia)
Land
Land
Land
Land
Land
Land
Land
Land
Land
Livestock Waste Management -
Livestock Waste Management -
Livestock Waste Management -
Livestock Waste Management -
Livestock Waste Management -
Livestock Waste Management -
Livestock Waste Management •
Anhydrous Ammonia Fertilizer
Beef Cattle Feedlots
Degrees ing
Dry Cleaning
Graphic Arts/Printing
Turkeys
Sheep
Beef Cattle
Dairy
Swine
Cattle
Broi ler Chickens
Other
Chickens
Application
* This category is broken down into categories 78-94
(continued)
6-15
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TABLE 6-3. (Continued)
Category
ID
081
082
083
084
085
086
087
088
089
090
091
092
093
094
096
097
098
099
100
101
102
103
104
105
106
107
108
109
Surrogate
ID
1
1
1
1
1
1
1
1
1
1
14
1
1
1
1
1
1
1
1
Surrogate
Indicator
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Population
Land Area
Population
Population
Population
Population
Population
Population
Population
Population
Emissions Category Description
Rubber and Plastics Manufacturing
Architectural Coatings
Auto Body Repair
Motor Vehicle Manufacturing
Paper Coating
Fabricated Metals
Machinery Manufacturing
Furniture Manufacturing
Flat Wood Products
Other Transportation Equipment Manufacturing
Electrical Equipment Manufacturing
Ship Building and Repair
Miscellaneous Industrial Manufacturing
Miscellaneous Solvent Use
Minor Point Sources - Coal
Minor Point Sources - Oil
Minor Point Sources - Gas
Minor Point Sources • Process
Publicly Owned Treatment Works (POTWs)
Cutback Asphalt Paving Operation
Fugitive Emissions from Synthetic Organic Chemical Manufacturing
Bulk^Terminal and Bulk Plants
Fugitive' Emissions from Petroleum Refinery Operations
Process Emissions from Bakeries
Process Emissions from Pharmaceutical Manufacturing
Process Emissions from Synthetic Fibers Manufacturing
Crude Oil and Natural Gas Production Fields
Hazardous Waste Treatment, Storage, and Disposal Facilities (TSDFs)
6-16
-------
column = ((WLNG-Longitude) x DLON) + 1 (6.2)
row = ((Latitude - SLAT) x DLAT) + 1 (6.3)
where: WLNG = Western boundary of the grid (125 degrees for NAPAP)
DLON = The number of grids/degree longitude (4 for NAPAP)
SLAT = Southern boundary of the grid (25 degrees for NAPAP)
DLAT = The number of grids/degree latitude (6 for NAPAP)
The resultant integer parts are the column and row numbers; fractional
parts are truncated.
Population and Housing Factors—
The starting point of the census-based spatial allocation surrogates is
the U.S. Department of Commerce, Bureau of the Census, Census of Population
and Housing, 1980. The file consists principally of sample data expanded to
represent the total population. However, data pertinent to persons and
housing units are based upon 100 percent counts and unweighted sample
counts.
Census data are summarized at the state (or state equivalent) level, and
are broken down, in hierarchical sequence, to the following levels: counties
or county equivalents: minor civil divisions (MCDs) or census county divisions
(CCDs); places or place segments within MCDs/CCDs and remainders of MCDs/CCDs;
census tracts or block numbering areas (BNAs); and block groups (BGs) or, for
areas that are not block-numbered, enumeration districts (EDs). In addition,
place and congressional districts (districts delineated for the 96th Congress)
are presented separately from the hierarchical summary organization.
Housing- and population-based spatial allocation factors are based on the
State-specific data files described above. This process involves executing
three separate Fortran programs on the Sperry UNIVAC: CREATE7A, CREATE5A, and
VIRGINIA.
Land Use Spatial Factors—
Development of the land use-derived spatial allocation factors uses two
data files: land use/cover percentages for 1/6 degree latitude by I/A degree
longitude grid cells and county-grid area relationships. The land use/cover
database was developed by Lockheed Engineering and Management Services
Company's Remote Sensing Laboratory under contract for the Meteorology and
Assessment Division of EPA's Environmental Sciences Research Laboratory
(ESRL), currently Atmospheric Research and Exposure Assessment Laboratory
(AREAL). The database was developed using Landsat mosaic images covering the
periods July 23 to October 31, 1972 and January 1 to March 31, 1973, and from
6-17
-------
Land Use and Land Cover Maps developed in the naiddle-to-late 1970"s. More
current Landsat data at the level of resolution required for the modelers'
inventory are not available. The development of the land use percentages are
documented elsewhere.
The total land use/cover in each grid cell is divided into the following
classifications:
• Urban Land • Mixed Forest Land (includes
forested wetlands)
• Agricultural Land
• Water
• Rangeland
• Barren Land
• Deciduous Forest Land
• Nonforested Wetland
• Coniferous Forest Land
• Mixed Agricultural Land
and Rangeland
Each land classification percentage reported by Lockheed represents the
portion of a grid cell comprised of a specific land use category. In addition
to the ten land use/cover categories supplied by Lockheed, two additional
categories were calculated for NAPAP:
• Composite Forest • Land Area'
Composite Forest is made up of coniferous, deciduous, and mixed forest
lands for each grid cell (this summation is performed prior to the allocation
of grid cells to counties). Land area is the fraction of a county's total
land area contained within a grid cell and is calculated from data in the
county-grid file.
To process the gridded land use percentages relative to county-level
emissions, a data file containing county-grid relationships was created. To
accomplish this, a file containing county boundaries (the County Dime File) is
used in conjunction with the NAPAP grid system and software developed by ESRL.
County-grid relationships are calculated as the fraction of each county in
each grid cell. An example of this process is illustrated in Figure 6-2.
Applying the county-grid file to the land use/cover percentages allows
gridded land use fractions to.be expressed as the fraction of a county's land
use/cover in a grid cell. The fractional totals for each county sum to 1;
fractional totals by grid cell do not sum to a particular number.
6-18
-------
COUNTY B
AREA = 1000
COUNTY C
AREA = 800
OVERLAP IN COUNTY A
GRID CELL X
AREA = 150
OVERLAP IN COUNTY B
COUNTY B GRID CELL X
AREA = 50
OVERLAP IN COUNTY C
GRID CELL X
AREA = 200
Figure 6-2. Example of County-to-Grid Cell Areal Relationship
6-19
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Spatial Factors for the 1985 MAPAP Inventory
Spatial allocation factors were originally developed for processing the
1980 NAPAP Emissions Inventory. To assure the quality of the
spatially-resolved 1985 area source emissions, extensive quality control
checks were performed on spatial factors adopted from the 1980 NAPAP database.
The goal of the spatial factor quality control procedures was to ensure
complete and accurate apportionment of NAPAP area source emissions. Based on
the results of the quality control checks, adjustments were made to the
spatial fractions for use with the 1985 modelers' inventory. Additional
modifications to the file were required for spatial allocation of the new 1985
NAPAP area source categories and Cibola County, New Mexico.
Quality Control Procedures—
Quality control procedures for the spatial allocation factors fell into
three general categories: evaluation of emissions losses in the Spatial
Allocation Module (SAM) based on the Quality Control Module (QCM) processing
messages; analyses of counties requiring normalization for spatial surrogates
deviating by 20 percent or more as noted in the Spatial Allocation Factor
Preprocessor (SAFP) processing messages; and other analyses including a
review of the spatial factor Fortran code and the Massachusetts county-to-APCD
conversion. These procedures were discussed in greater detail in Section 5 of
this report.
The quality control checks found isolated cases of missing or new
counties, missing grid cells, incorrect county codes and incorrect location
data. In addition, some erroneous spatial factors in Massachusetts revealed a*
problem with the Massachusetts county-to-APCD conversion. A review of the
spatial factor Fortran code revealed minor inaccuracies in some of the
software; these problems are discussed in greater detail in Section 5.
Adjustments to Spatial Allocation Factors—
For 1985 modelers' inventory applications, adjustments were made to the
1980 spatial fractions to account for missing and new counties, missing grid
cells, incorrect county code assignments and location data, inaccuracies in
the Massachusetts county-to-APCD conversion and the new area source
categories. All corrections were applied to the SAFP input file (the spatial
fraction file) and are summarized in Table 6-4. Once adjustments were made to
the spatial fraction file, SAFP was executed and the processing messages were
evaluated to assure the factors were correctly applied.
Spatial Allocation Factor Pile Format
The Spatial Allocation Factor File is an EBCDIC file containing one
record type. Each record contains all of the spatial allocation information
for a particular SCC code in a specific county.
6-20
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6-22
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SPECIATION FACTORS
Background
Several pollutants in the NAPAP annual inventory represent composites of
various individual chemical species. Modeling requirements dictate that three
of these pollutant aggregations - oxides of nitrogen (NOX), total suspended
particulates (TSP) and total hydrocarbons (THC) - be resolved into their
component species or groups of species which share similar chemistry. For the
1985 modeling inventory, NOX was apportioned Lnto NO and N02» THC was
allocated between 32 classes of species, and TSP was split into 15 categories
based on alkalinity class and size fraction. This resolution is accomplished
by applying speciation factors, which can be multiplied by the annual
pollutant totals to yield emission estimates for user-selected classes of
compounds.
Speciation Methodology
Hydrocarbons/NOZ— Regional models such as RADM employ algorithms to
simulate atmospheric transformation processes. These reaction mechanisms are
applied to classes of organic compounds, each of which may include a number of
individual species with similar reaction properties. As an example, several
C4 through C6 olefins may be satisfactorily expressed by a single set of
reaction statements, and therefore be represented in the model by a single
species class. The 32 hydrocarbon classes used in the 1985 NAPAP modeling
inventory are shown in Table 6-5.
Since the speciation requirement for hydrocarbon emissions may change
depending on the model chemistry used in a particular application, it is
desirable to keep speciation as flexible as possible. Speciated VOC data are
also required for other EPA activities including ozone and air toxics studies.
Hydrocarbon species data are therefore coded as a set of species profiles,
which provide a breakdown of the component species for a source's hydrocarbon
emissions. The profiles can be manipulated by a user to represent any
desired classification scheme.
For the NAPAP inventory, species profiles and class assignments are used
to create speciation factors by means of a computer program called the
Pollutant Splits Program (PSPLIT). PSPLIT is a Fortran program that can
divide hydrocarbon emissions into a maximum of 640 discrete species based on
source category-specific profiles. The individual species are then
reaggregated into classes based on the requirements of the selected chemical
mechanism.
PSPLIT requires three input files. The Hydrocarbon Profile File contains
the weight percent breakdown of the individual species that make up the
profile. Each of these species is assigned to one of up to 32 aggregation
classes in the Species-Class File. Finally, each point or area source SCC is
matched to a profile in an SCC-Profile Index File. PSPLIT then uses these
6-23
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TABLE 6-5. HYDROCARBON SPECIES CLASSES, 1985 NAPAP
MODELERS' INVENTORY
Class number Description
1 Methane
2 . Ethane
3 Propane
4 Alkanes (0.25 - 0.50 reactive)3
5 Alkanes (0.50 - 1.00 reactive)
6 Alkanes (1.00 - 2.00 reactive)
7 Alkanes (> 2.00 reactive)
8 Alkane/aromatic mix
9 Ethene
10 Propene
11 Alkenes (primary)
12 Alkenes (internal)
13 Alkenes (primary/internal mix)
14 Benzene, halobenzenes
15 Aromatics (< 2.00 reactive)
16 Aromatics (> 2.00 reactive)
17 Phenols and cresols
18 Styrenes
19 Formaldehyde
20 Higher aldehydes
21 Acetone
22 Higher ketones
23 Organic acids
24 Acetylene
25 Haloalkenes
26 Unreactive
27 Others (< 0.25 reactive)
28 Others (0.25 - 0.50 reactive)
29 Others (0.50 - 1.00 reactive)
30 Others (> 1.00 reactive)
31 Unidentified
32 Unassigned
aReactivity is defined with respect to rate constant
range (with OH), 10^ ppnf1 min-1.
6-24
-------
files to calculate the amount of the total hydrocarbon that contributes to
each of the 32 classes for each SCC. These "mole factors" are expressed in
units of moles of species class per ton of total hydrocarbon. The PSPLIT
output is used by the FREDS Speciation module to produce class-level
hydrocarbon emissions suitable for input to RADM.
PSPLIT is also used to disaggregate NOX emissions into NO and N02« Each
species profile also contains the weight fractions of NO and N02> which are
carried through PSPLIT and become the speciation factors for these two
compounds.
Particulates—Speciation factors for pollutants not handled by PSPLIT can
be input to FREDS using an independent Speciation Factor File (SFF). For the
1985 resolved modeling inventory, the SFF contains factors for 15 subspecies
of particulate matter. Since particle size is an important criterion in TSP
transport mechanisms, particulates are "speciated" by both alkalinity and size
fraction. Each of the four alkaline dust species - calcium, magnesium,
sodium, and potassium - is expressed in terms of 0 to 2.5 micron fraction, 2.5
to 10 micron fraction, and "total species" fraction. In addition, TSP is
speciated into 0 to 2.5, 2.5 to 6, and 6 to 10 micron components. Speciation
fractions included in the SFF are dimensionless weight fractions which are
multiplied by TSP emissions to yield the number of tons of each species class.
A complete listing of particulate species classes to be used in the 1985
resolved modeling inventory is shown in Table 6-6. SAROAD codes for
alkaline particulates distinguish between chemical species, but have no
provision for size class. In order to speciate by both species and size
fraction, a two-digit size class code was developed which is concatenated to
the front of the five-digit sp.ecies SAROAD code when the particulate
speciation data are input to the Speciation Module. ' This allows the two codes
to be interpreted as one during speciation.
Speciation Factor Development
00 00
The updated Air Emissions Species Manual ' serves as the basis for the
1985 NAPAP speciation files for total hydrocarbons and particulate matter
(PM). This document is the result of revisions to the "Volatile Organic
Compound (VOC) Species Data Manual - 2nd Edition" (EPA-450/4-80-015) and
updates to the "Receptor Model Source Composition Library" (EPA-450/4-85-002).
Existing profiles in these documents were evaluated and updated as
necessary, and new profiles were developed and incorporated into the existing
VOC and PM profile databases. The assignment of organic compound species to
classes was a collaborative effort coordinated by the National Center for
Atmospheric Research (NCAR) representing NAPAP Task Group III (Atmospheric
Transport and Modeling).
In addition to the profiles themselves, VOC and PM profile assignments
were made for all SCCs in the National Emissions Data System (NEDS),
applicable NEDS area source codes, and additional area source codes used in
6-25
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TABLE 6-6. PARTICULATE SPECIES CLASSES, 1985 MODELERS' INVENTORY
Size
class code
10a
30
50
10
30
50
10
30
50
10
30
50
10
20
40
Species
SAROAD code
12111b
12111
12111
12140
12140
12140
12180
12180
12180
12184
12184
12184
11101
11101
11101
Description
Reactive calcium (Ca), 0-2.5 micron size range
Reactive calcium, 2.5-10 micron size range
Reactive calcium, total
Reactive magnesium (Mg), 0-2.5 micron size range
Reactive magnesium, 2.5-10 micron size range
Reactive magnesium, total
Reactive potassium (K), 0-2.5 micron size range
Reactive potassium, 2.5-10 micron size range
Reactive potassium, total
Reactive sodium (Na), 0-2.5 micron size range
Reactive sodium, 2.5-10 micron size range
Reactive sodium, total
Total particulates , 0-2.5 micron size range
Total particulates, 2.5-6 micron size range
Total particulates, 6-10 micron size range
aSize class codes are defined as follows:
Code
10
20
30
40
50
Size range (microns)
0 - 2.5
2.5 - 6.0
2.5 - 10.0
6.0 - 10.0
Total
^Species SAROAD codes are defined as follows:
SAROAD Species
12111 Calcium (Ca)
12140 Magnesium (Mg)
12180 Potassium (K)
12184 Sodium (Na)
11101 Total particulates
6-26
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NAPAP. Ideally, a profile would be needed to represent each one of these
categories. However, the number of categories to be characterized is much
larger than the number of available profiles. Source categories without
original profile assignments were, therefore, assigned to existing profiles
using engineering judgment. Industry-specific average profiles were also
developed and applied to source categories for which a satisfactory assignment
based on engineering judgment was not possible. Finally, a "zero" profile (an
overall average based on all the profiles in the VOC or PM database) was
developed. This profile is intended for use only as a default profile to
represent those SCCs characterized by "zero" or "negligible" emission factors,
and for which the States or any other agency may have reported nonzero
emissions.
NOX speciation fractions represent NO and NC>2 fractions recommended by
AP-4232 and were adapted from the 1980 speciation data with little
modification. In previous applications, profiles that were applied to an SCC
that was not expected to have NOX emissions were assigned profile splits of
zero for both NO and N02« It was of concern that NOX emissions could be lost
during the FREDS speciation step if a source with NOX emissions were
misassigned to an SCC that does not usually report NOX emissions. To ensure
that no NOX emissions are lost, a default split of 95 percent NO and 5 percent
N02 was substituted for every profile which did not have an explicit NO/N02
split.
Speciation Factor Data Quality Ratings
A data quality rating was assigned for the application of each
hydrocarbon and particulate speciation profile to the point source SCCs .and
area source categories. The development of the data quality ratings was
subjective and was implemented similarly to the assignment of emission factor
data quality ratings included in AP-42. The data quality rating system uses
ratings of A through E, where A indicates the highest and E the lowest degree
of confidence in the profile assignment.
A summary of the resultant hydrocarbon class assignments for the total
point and area sources in the United States is presented in Table 6-7. The
summaries are represented as the percent of each class total emissions that
resulted from the application of profiles with the indicated profile
assignment quality rating.
Using the current hydrocarbon speciation methodology, approximately 50
percent of the total THC is speciated by D or E rated speciation profiles. No
hydrocarbon emissions in the modelers' inventory are speciated by A rated
profiles.
6-27
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TABLE 6-7. HYDROCARBON EMISSIONS IN PERCENT, BY CLASSES, BY SPECIATION
PROFILE QUALITY RATING
SPECIES CLASS
HC1
HC2
HC3
HC4
HC5
HC6
HC7
HC8
HC9
HC10
HC11
HC12
HC13
HC14
HC15
HC16
HC17
HC18
^HC19
HC20
^HC21
•HC22
HC23
VHC24
HC25
HC26
HC27
HC28
HC29
HC30
HC31
HC32
SPECIES CLASS NAME
methane
ethane
propane
atkanes (0.2S-0.5 react)
alkanes (0.5-1.0 react)
alkanes (1.0-2.0 react)
atkanes (>2.0 react)
alkanes/aro mix
ethene
propene
alkenes primary
alkenes internal
alkenes pri/int mix
benzene/ha I obenzenes
aromatics (<2.0 react)
aroma tics (>2.0 react)
phenols/cresols
styrenes
formaldehyde
higher aldehydes
acetone
higher ketones
organic acids
acetylene
haloalkenes
unreactive
others (<0.25 react)
others (0.25-0.5 react)
others (0.5-1.0 react)
others (>1.0 react)
unidentified
unass i gned
A
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
B
19.5
15.5
16.2
36.4
38.9
47.5
37.9
0.0
19.0
4.3
31.2
45.9
59.7
5.4
53.8
60.9
2.8
2.9
20.1
37.2
13.1
22.0
1.1
19.8
80.9
40.3
2.2
4.6
13.3
6.1
42.2
12.5
C
17.9
24.5
12.1
28.6
26.4
19.3
7.7
11.9
20.4
16.3
21.5
27.8
6.4
2.3
16.8
21.0
1.6
0.1
19.2
• 10.6
8.8
9.5
0.0
36.6
0.9
0.0
4.2
1.9
2.4
1.9
49.1
2.4
D
35.9
29.6
9.3
15.5
8.9
14.1
31.1
75.9
44.8
40.4
25.4
4.6
20.3
55.8
22.2
9.5
0.1
4.4
25.6
0.3
31.9
27.1
2.0
25.8
0.0
5.7
17.6
82.5
72.5
61.0
6.3
2.4
E
26.8
30.3
62.4
19.5
25.8
19.0
23.3
12.2
15.8
39.1
22.0
21.6
13.6
36.5
7.2
8.5
95.5
92.5
35.2
51.9 .
46.1
41.4.
96.9
17.9
18.2
54.0
76.0
11.1
11.9
31.0
2.4
82.7
TOTALS
0.0
32.4
17.9
26.4
23.3
6-28
-------
The result of the data quality ratings for the speciation of the more
chemically reactive species classes is of primary importance for the modeling
applications. Ten of the species classes presented in Table 6-7 have been
identified as the most reactive for RADM modeling applications. The 10 most
reactive species classes are:
HC7 alkanes (>2.0 reactivity)
HC8 alkanes/aromatic mix
HC10 propene
HC11 alkenes primary
HC12 alkenes internal
HC13 alkenes primary/internal mix
HC16 aromatics (>2.0 reactivity)
HC17 phenols and cresols
HC18 styrenes
HC20 higher aldehydes
More than 50 percent of the emissions for several of these most reactive
species classes have resulted from speciation profiles with quality ratings of
B or C.
The effect of speciatioh factor data quality on the 10 most reactive
species classes is .presented in Table 6-8. This table presents the area
source categories and the data quality ratings for the 30 largest contributors
to the 10 most reactive classes. Seventeen of the 30 entries in the table
result from D or E rated profiles, 1 from a C rated profile and 12 from B
rated profiles. The entries with B rated profiles are associated primarily
with the area source categories for highway gasoline vehicles, gasoline
marketed and architectural coatings. The entries with E rated profiles are
associated primarily with the subcategories for solvent purchased and the 10
additional NAPAP area source categories.
A summary of the resultant particulate class emissions for the total of
U.S. point and area sources by speciation factor quality rating is presented
in Table 6-9. The emissions in every particulate class are dominated by D and
E rated profiles. No particulate class emissions are speciated by either A or
B rated profiles.
Data Preparation—
PSPLIT file—Hydrocarbon speciation data consist of the following files:
• "PROFILE" file - contains, for each profile, a listing of weight
percentages for all species making up the profile (species
identified by SAROAD code).
• "SPECIES" file - contains the name, SAROAD, and molecular weight of
each of the approximately 600 species used in profile development.
6-29
-------
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6-30
-------
TABLE 6-9. PARTICULATE EMISSIONS IN PERCENT, BY CLASSES, BY SPECIAT10N
PROFILE QUALITY RATING
COMBINED POINT AND AREA SOURCES - CLASS QUALITY RATING
SPECIES CLASS
SPECIES CLASS NAME
B
Total Participate
Particulate 0.0-2.5 microns
Participate 2.5-6.0 microns
Particulate 6.0-10.0 microns
Calcium 0.0-2.5 microns
Calcium 2.5-6.0 microns
Calcium 6.0-10.0 microns
Potassium 0.0-2.5 microns
Potassium 2.5-6.0 microns
Potassium 6.0-10.0 microns
Magnesium 0.0-2.5 microns
Magnesium 2.5-6.0 microns
Magnesium 6.0-10.0 microns
Sodium 0.0-2.5 microns
Sodium 2.5-6.0 microns
Sodium 6.0-10.0 microns
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.7
9.1
10.9
9.2
0.8
1.1
1.0
0.0
O.A
0.1
0.0
0.0
0.0
29.6
21.3
24.8
58.3
61.9
53.0
52.7
53.5
70.8
58.9
63.6
23.4
56.2
46.4
4.4
28.7
29.8
44.4
32.5
33.1
29.0
36.1
38.1
45.7
28.1
40.1
36.4
76.2
43.7
53.6
95.6
71.3
40.5
34.3
42.8
6-31
-------
• "POINT" file - contains the point source SCCs with their respective
hydrocarbon profile assignments.
• "AREA" file - contains the area source SCCs with their respective
hydrocarbon profile assignments.
Following manual and automated quality control checking, these files served as
the basis for the PSPLIT input files.
The data processing requirements of the modelers' inventory made it
necessary to modify the original PSPLIT computer code. The modifications were
made and a new version of the PSPLIT code, version PSPLIT_2A, was created.
Version 2A differs from earlier versions of PSPLIT as follows:
• The maximum number of species per profile was increased from 75
to 325.
• The maximum number of species in all classes was increased from 500
to 640.
• The maximum number of SCC-profile records was increased from 1,800
to 4,000.
• The maximum number of hydrocarbon profiles was increased from 175
to 320. —
• The maximum number of hydrocarbon profile records (at the species
level) was increased from 2,000 to 4,500.
• The maximum number of species classes and output records was
increased from 30 to 32.
• The output file format was changed from 4 characters to 5 characters
to increase the precision of the speciation factors.
Speciation factor file—Particulate speciation data consist of the
following files:
• "PROFILE" file - contains the 161 PM profiles, including the
chemical species associated with each and the percent contribution
of each species within each size fraction of interest.
• "FRACTION" file - contains the mass fraction of particulate within
each size fraction for each of the PM profiles.
• "REACTIVITY" file - contains the reactive fraction associated with
Ca, Mg, Na, and K for each profile.
6-32
-------
• "POINT" file - contains the point source SCCs with their respective
PM profile assignments.
• "AREA" file - contains the area source SCCs with their respective PM
profile assignments.
• "CONTROL" file - contains the NEDS control device codes associated
with the control device used in determining each PM profile (not
used).
To convert the profile data into speciation factors suitable for NAPAP,
the composition, mass fraction, and reactivity are combined to obtain
the reactive fraction of a given species within a given size fraction. This
fraction can be expressed as
_
100
where: SF^ y Z ~ Speciation Factor representing the reactive fraction
of species X within size fraction Y for
speciation profile Z;
WP^ Y Z = wgight percent of size fraction Y occupied by
species X, profile Z;
MFy 2 = mass fraction of total particulate occurring within
size fraction Y, profile Z; and
RF^ 2 = reactive fraction of species X, profile Z.
Two computer programs were developed to derive speciation factors from
the data. The first program used the profile, fraction, and reactivity data
to create speciation fractions using the above equations. The pro'file" "
data were first reformatted and reduced to contain only the four species
relevant to NAPAP (Na, K, Mg, and Ca), at the size ranges desired. These were
multiplied by the fraction of total TSP occurring at each size range, and then
by the reactive fractions of the four species. The resulting "species-
profile" file was used as input to a second program, which matched the
speciation data to source categories and wrote the resulting data set in
FREDS-compatible EBCDIC format.
Addition of profile assignments for new area source SCCs—The Speciation
Module'requires that all point and area source SCCs be assigned to speciation
profiles, regardless of whether or not the SCC is likely to report emissions
of THC, TSP, or NOX. As a result, all new area source categories (100 through
109) were assigned to hydrocarbon and particulate speciation profiles. The
vehicular source ammonia categories (66 through 68) were assigned to the
6-33
-------
default TSP and hydrocarbon profiles (0), as no emissions of these pollutants
were included for these profiles. Similarly, TSP and hydrocarbon profiles for
the new livestock waste management categories (69 and 70) were copied from the
related categories 71 through 75.
File Formats
PSPLIT File Format—
The PSPLIT file is an EBCDIC file containing THC and NOX speciation data
by SCC. PSPLIT data are contained.on two records: Header records (type 1)
and Speciation records (type 2). Two-digit codes appear at the end of each
line which indicate both the record type and whether the record is the first
or second of a record type. As a result, the first two records of the PSPLIT
file constitute the Header (types 11 and 12); the remainder of the file is
comprised of Speciation records (alternating between types 21 and 22).
To illustrate this principle, the format of the PSPLIT file is provided
in Table 6-10. The header record contains a series of codes corresponding to
the 32 hydrocarbon species classes, with the first 16 codes on the first
header record (type 11) and the next 16 on the second (type 12). The
Speciation records contain the SCC code and the 32 mole factors corresponding
to each SCC. Again, 16 of these five-digit factors appear on the first
speciation record and the remaining 16 on the second (types 21 and 22,
respectively). Type 21 records also contain the weight fraction of
formaldehyde (not used in FREDS) and the fractional split of NO and N02- The
record type appears in the last two columns of each record.
Speciation Factor File Format—
An example of Speciation Factor File format is shown in Table 6-11. The
SFF can currently accommodate 20 speciation factors (10 per line), and is
formatted in a manner similar to the PSPLIT File. Each factor is expressed as
an eight-digit integer which is input to the Speciation Module such that all
eight digits appear to the right of the decimal. Seven digits are allowed for
each species class code in the header record, to allow room for particulate
size class/SAROAD combinations.
6-34
-------
TABLE 6-10. PSPLIT FILE FORMAT
Record position
First
Header
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
114
Header
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
114
Last width
Record No.
5
1.0
15
20
25
30
35
40
45
50
55
60
65
70
75
80
113
115
Record No.
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
113
115
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
33
2
2
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
33
2
Format
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
-
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
-
I
variaDie
name
CLASS 1
CLASS2
CLASS3
CLASS4
CLASS5
CLASS6
CLASS7
CLASS8
CLASS9
CLASSIC
CLASS11
CLASS 12
CLASS 13
CLASS14
CLASS15
CLASS16
—
TYPE
•
CLASS 17
CLASS 18
CLASS19
CLASS20
CLASS21
CLASS22
CLASS23
CLASS24
CLASS25
CLASS26
CLASS27
CLASS28
CLASS29
CLASS30
CLASS31
CLASS32
—
TYPE
Description
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Unused
code
code
code
code
code
code
code
code
code
code
code
code
code
code
code
code
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
No.
No.
No.
No.
No,
No.
No.
No.
No,
No.
No.
No.
No.
No.
No.
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Record T/pe (=11)
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Pseudo-SAROAD
Unused
code
code
code
code
code
code
code
code
code
code
code
code
code
code
code
code
-
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
17
18
19
20
21
22
23
24
25
26
27
28
29
.30
31
32
Record Type (=12)
(continued)
6-35
-------
TABLE 6-10. (continued)
Record
First
position
Last
Speciation Record
1
3
7
11
14
22
27
32
37
42
47
52
57
62
67
72
77
82
87
92
97
102
106
110
114
2
6
10
13
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
105
109
113
115
Column
width
No. 1
2
4
4
3
8
5
5
5
5
5
5
5
5
5
5 "
5
5
5
5
5
5
4
4
4
2
Format
I
I
A
A
I
F5V3b
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F4V4
F4V3
F4V3
I
Variable
name
STATE
COUNTY
PLANT_ID
POINT ID
sec
TSF1
TSF2
TSF3
TSF4
TSF5
TSF6
TSF7
TSF8
TSF9
.TSF10
TSF11
TSF12
TSF13
TSF14
TSF15
TSF16
HCHOWT
TSF33
TSF34
TYPE
Description
AEROS State Code
AEROS County Code
Plant Identification Code3
Point Identification Code3
SCC Code
Speciation Factor, THC Class lc
Speciation Factor, THC Class 2C
Speciation Factor, THC Class 3C
Speciation Factor, THC Class 4C
Speciation Factor, THC Class 5C
Speciation Factor, THC Class 6C
Speciation Factor, THC Class 7C
Speciation Factor, THC Class 8C
.Speciation Factor, THC Class 9C
Speeiation Factor, THC Class 10C
Speciation factor, 'THC Class llc
Speciation Factor, THC Class 12C '
Speciation Factor, THC Class 13C
Speciation Factor, THC Class 14C
Speciation Factor, THC Class 15C
Speciation Factor, THC Class 16C
Weight Fraction Formaldehyde in
Profile (dimensionless )
Speciation Factor for N02
(dimensionless weight fraction)
Speciation Factor for N02
(dimensionless weight fraction)
Record Type (=21)
(continued)
6-36
-------
TABLE 6-10. (continued)
Record position
First Last width
Format name
Description
Speciation Record No. 2
1
3
7
11
14
22
27
32
37
42
47
52
57
62
67
72
77
82
87
92
97
102
114
2
6
10
13
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
113
115
2
4
4
3
8
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
12
2
I
I
A
A
I
F5V3b
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
• F5V3
F5V3
F5V3
F5V3
F5V3
F5V3
-
I
STATE
COUNTY
PLANT ID
POINT ID
sec
TSF17
TSF18
TSF19
TSF20
TSF21
TSF22
TSF23
TSF24
TSF25
TSF26
TSF27
TSF28
TSF29
TSF30
TSF31
TSF32
—
TYPE
AEROS State Code
AEROS County Code
Plant Identification Code3
Point Identification Code3
SCC Code
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Unused
Record Type
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
Factor,
(=22)
THC
THC
THC
THC
THC
THC
THC
THC
THC
THC.
THC
THC
THC
THC
THC
THC
Class
Class
Class
Class
Class
Class
Class
Class
•Class
Class
Class
Class
Class
Class
Class
Class
17C
18C
19C
20C
21C
22C
23c
24C
25C
26C
27?
28C
29C
30C
31C
32c
aFor area source processing, these fields are left blank.
"Speciation factors are five-uigit numbers with an implied decimal point to
the right of the second digit.
cUnits are moles of the THC class per kilogram of total hydrocarbons.
6-37
-------
TABLE 6-11. SPECIATION FACTOR FILE FORMAT
Record
First
Header
1
8
15
22
29
36
A3
50
57
64
71
104
Header
1
8
15
22
29
36
104
position
Last v
Record No.
7
14
21
28
35
42
49
56
63
70
103
105
Record No.
7
14
21
28
35
103
105
Jo 1 umn
fidth
1
7
7
7
7
7
7
7
7
7
7
33
2
2
7 '
7
7
7
7
68
2
Variable
Format name
I
I
I
I
I
I
I
I
I
I
CLASS1
CLASS2
CLASS3
CLASS4
CLASS5
CLASS6
CLASS 7
CLASS8
CLASS9
CLASSIC
Size
Size
Size
Size
Size
Size
Size
Size
Size
Size
Description
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Code
Code
Code
Code
Code
Code
Code
Code
Code
Code
for
for
for
for
for
for
for
for
for
for
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
No.
No.
No.
No.
No.
No.
No.
No.
No.
No.
1
2
3
4
5
6
7
8
9
10
Unused
I .
I
I
I
I
I
TYPE
CLASS 11
CLASS12 '
CLASS13
CLASS14
CLASS 15
Record Type (=11)
Size
Size
Size
Size
Size
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Class/SAROAD
Code
Code
Code
Code
Code
for
for
for
for
for
Class
Class
Class
Class
Class
No-.
No.
No.
No.
No.
11
12
13
14
15
Unused
I
TYPE
Record Type (=12)
(continued)
6-38
-------
TABLE 6-11. (continued)
Record position
First Last
Column Variable
width Format name
Description
Speciation Record
1 2
3 6
7 10
11 13
14
22
30
38
46
54
62
70
78
86
95
103
104
Speciat
1
3
7
11
14
22
30
38
46
54
62
104
21
29
37
45
53
61
69
77
85
94
102
103
105
ion Record
2
6
10
13
21
29
37
45
53
61
103
105
No. 1
2
4
4
3
8
8
8
8
8
8
8
8
8
8
8
1
2
No. 2
2
4
4
3
8
8
8
8
8
» 8
• 41
2
I
I
A
A
I
F8V8b
F8V8
F8V8
F8V8
F8V8
F8V8
F8V8
F8V8
F8V8
F8V8 '
I
I
I
A
A
I
F8V8b
F8V8
F8V8
F8V8
F8V8
-
I
STATE
COUNTY
PLANT_ID
POINT ID
SCC
TSF1
TSF2
TSF3
TSF4
TSF5
TSF6
TSF7
TSF8
TSF9
TSF10
—
TYPE
STATE
COUNTY
PLANT ID
POINT ID
SCC
TSF11
TSF12
TSF13
TSF14
TSF15
—
TYPE
AEROS State Code
AEROS County Code
Plant Identification
Point Identification
SCC Code
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Speciation
Unused
Record Type
AEROS State
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
(=21)
Code
for
for
for
for
for
for
for
for
for
for
Code3
Code*
Class
Class
Class
Class
Class
Class
Class
Class
Class
Class
.
lc
2C
3C
4C
5C
6C
7C
8C
9C
10C
AEROS County Code
Plant Identification
Point Identification
SCC Code
Speciation
Speciation
Speciation
Speciation
Speciation
Unused
Record Type
Factor
Factor
Factor
Factor
Factor
(=22)
for
for
for
for
for
Codea
Codea
Class
Class
Class
Class
Class
11
12
13
c
c
c
14C
15C
For area sources, these fields are left blank.
Speciation factors are 8-digit numbers with all 8 digits to the right of an
implied decimal point.
cSpeciation factors are dimensionless weight fractions.
6-39
-------
SECTION 6
REFERENCES
1. Sellars, F.M., M.J. Geraghty, A.M. Kiddie, and B.J. Bosy. Northeast
Corridor Regional Modeling Project Annual Emission Inventory Compilation
and Formatting. U.S. EPA, £PA-450/4-82-013a. Washington, DC, 1982.
2. U.S. Department of Commerce. State, Regional, and National Monthly and
Seasonal Heating Degree Days Weighted by Population (1980 Census)
Historical Climatology Series 5-1. National Oceanic and Atmospheric
Administration. Asheville, NC, 1983.
3. U.S. Department of Commerce. Local Climatological Data Published
Stations (1980 data), National Climatic Data Center, National Oceanic and
Atmospheric Administration, Asheville, NC, 1980.
4. U.S. Environmental Protection Agency. Procedures for the Preparation of
Emission Inventories for Volatile Organic Compounds; Volume II—Emission
Inventory Requirements for Photochemical Air Quality Simulation Models,
EPA-450/4-79-018 (NTIS PB80-202229), Research Triangle Park, NC, 1979.
5. U.S. Department of Labor. Supplement of Employment and Earnings:
Revised Establishment Data, Bureau of Labor Statistics, Washington, DC,
1981.
6. Klemm, H.A. and R.J. Brennan. .Emissions Inventory for the SURE Region.
GCA/Technology Division for the Electric Power Research Institute,
EA-1913. Palo Alto, CA, 1981.
7. U.S. Environmental Protection Agency. Documentation of the Regional Air
Pollution Study (RAPS) and Related Investigations in the St. Louis Air
Quality Control Region. EPA-600/4-79-076 (NTIS PB80-138241), Research
Triangle Park, NC 1979.
8. New Jersey Department of Environmental Protection. Emission Inventory—
State of New Jersey, Trenton, NJ, 1980.
9. New York Department of Environmental Conservation. Emission Inventory—
State of New York, Metropolitan New York Region, Albany, NY, 1980.
10. U.S. Environmental Protection Agency. Emission Inventories for Urban
Airshed Model Application in Tulsa, Oklahoma, EPA-450/4-80-021 (NTIS
PB81-156986), Research Triangle Park, NC, 1980.
11. U.S. Department of Transportation. Highway Statistics, 1980. Federal
Highway Administration, Washington, DC, 1980.
6-40
-------
12. U.S. Environmental Protection Agency. Regional Air Pollution Study;
Off-Highway Mobile Source Emission Inventory. EPA-600/4-77-041 (NTIS
PB273503), Research Triangle Park, NC, 1977.
13. Haupt, S.E., P.M. Sellars, M.J. Geraghty, A.M. Kiddie, and B.J. Bosy.
1982: Northeast Corridor Regional Modeling Project Annual Emission
Inventory Compilation and Formatting—Volume X—Ohio Emission Inventory,
U.S. EPA, EPA-450/4-82-013J. Washington, DC, 1981.
14. U.S. Civil Aeronautics Board. Seasonally Adjusted Traffic and Capacity,
Washington, DC, 1981.
15. U.S. Department of Energy. Petroleum Marketing Monthly, Energy
Information Service, Washington, DC, 1984.
16. Electric Power Research Institute. The EPRI Regional Systems, EPRI
P-1950-SR. Palo Alto, CA, 1981.
17. U.S. Environmental Protection Agency. Technical Tables to the National
Air Pollutant Emissions Estimates, 194Q-1984. EPA-450/4-85-014 (NTIS
PB86-121100). Office of Air Quality Planning and Standards, Research
Triangle Park, NC. 1986.
18. U.S. Department of Energy. Energy Data Reports, Washington, DC, 1979.
19. Beaulieu, T.A. and L.G. Modica. Documentation of Spatial Allocation
Factor Procedures for the 1980 NAPAP Emissions Inventory.
EPA-600/7-88-024a (NTIS PB89-159479), U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1988.
20. U.S. Bureau of the Census. Census of Population and Housing, 1980;
Summary Tape File 3 Technical Documentation. Prepared by the Data User
Services Division, Bureau of the Census. Washington; The Bureau. pp.
437. 1982.
21. Page, S.H. National Land Use and Land Cover Inventory. Lockheed
Engineering & Management Services Company, Inc. Remote Sensing
Laboratory, Las Vegas, Nevada. Prepared for Office of Research and
Development, U.S. Environmental Protection Agency, Las Vegas, Nevada.
pp. 7. April 1980.
22. Shareef, G., W. Butler, L. Bravo, and M. Stockton. Air Emissions Species
Manual Volume I - Volatile Organic Compound (VOC) Species Profiles.
EPA-450/2-88-003a (NTIS PB88-225792). U.S. Environmental Protection
Agency, Research Triangle Park, NC. April, 1988.
23. Shareef, G., and L. Bravo. Air Emissions Species Manual Volume II -
Particulate Matter (PM) Species Profiles. EPA-450/2-88-003b
(NTIS PB88-225800). U.S. Environmental Protection Agency, Research
Triangle Park, NC. April 1988.
6-41
-------
24. Compilation of Air Pollutant Emissions Factors. Volume It Stationary
Point and Area Sources. AP-42. Fourth Edition (GPO No. 055-000-00251-
7), U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina, September 1985.
6-42
-------
SECTION 7
DEVELOPMENT AND QUALITY ASSURANCE OF CANADIAN DATA
INTRODUCTION
Environment Canada, provincial environment ministries, and the U.S.
Environmental Protection Agency worked cooperatively to develop an Emissions
inventory for 721 plants and 129 area source categories for the 10 Provinces
to 60 degrees north latitude and from 50 to 125 degrees west longitude for use
in the 1985 NAPAP Modelers' Emissions Inventory Version 2. An additional 9
Canadian area source categories were used to develop the 1985 Canadian NAPAP
Natural Particulate Emissions Inventory.
Emissions data were collected by provincial environment ministries and
transmitted to Environment Canada for the compilation of the 1985 Canadian
National Emissions Inventory. The complete 1985 Canadian National Emissions
Inventory is presented in Table 7-1. Because the NAPAP modeling domain
extends only to the border between the 10 provinces and the northern Canadian
territories at 60 degrees north latitude, the emissions data from the Yukon
and Northwest territories which were reported to the U.S. EPA were not
processed for use by NAPAP.
This section describes the data collection and quality assurance measures
taken to develop the 1985 Canadian NAPAP Emissions Inventory. Environment
Canada has reviewed the emissions data included in the Canadian inventory with
the provincial environment agencies. A letter from Environment Canada
approving the use of these data in further NAPAP research efforts is included
in Appendix I.
The emissions data included in the Canadian inventory are presented as
national, provincial, and sector-level totals. The point and area source data
collection methodologies and differences between the Canadian point and area
source record formats and the data formats used for U.S. data are described.
An overview of the 1985 Canadian Natural Particulate Emissions Inventory is
also presented in this section. The quality assurance and data processing
measures applied by EPA to the 1985 Canadian NAPAP Emissions Inventory are
also described. These activities were performed to ensure that the processing
of the Canadian data by FREDS would be complete and that the resulting
inventory would be as compatible as possible with the 1985 United States NAPAP
Emissions Inventory Version 2.
1985 CANADIAN NAPAP EMISSIONS INVENTORY
Table 7-2 summarizes the 1985 Canadian NAPAP Emissions Inventory by
Province for S02, S04, NOX, VOC, THC, TSP, CO, HC1, HF, and NH3. The
7-1
-------
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7-2
-------
TABLE 7-2. 1985 CANADIAN NAPAP EMISSIONS INVENTORY BY PROVINCE
PROVINCE
NEWFOUNDLAND
PRINCE EDUARO ISLAND
NOVA SCOTIA
NEW BRUNSWICK
QUEBEC
ONTARIO
MAN I T08A
•
SASKATCHEWAN
ALBERTA
BRITISH COLUMBIA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
TOTAL
POINT
AREA
GRAND
so2
27,422
20,590
48,012
317
1,695
2,012
163,206
23,504
186,710
127,873
20,885
US, 758
632,688
143,186
775,874
1,457,096
136,475
1,593,571
509,949
6,213
516,162
85,883
7,198
93,081
582,656
7,317
589,973
71,526
33,045
104,571
3.658,616
400,108
4,058,724
S04
5,233
1,520
6,753
19
97
116
7,749
1,712
9,461
22,153
1,659
23,812
0
6,458
6,458
0
4,639
4,639
19,403
198
19,601
22,020
283
22,303
4,487
295
4,782
0
1,801
1,801
81,064
18,662
99,726
HO,
5,625
39,939
45,564
73
6,417
6,490
26,897
57,195
84,092
13,041
37,980
51,021
16,505
244,775
261,280
144,820
461,178
605,998
2,788
89,285
92,073
43,344
116,802
160,146
266,211
238,786
504,997
13,634
255,428
269,062
532,938
1,547,785
2,080,723
VOC
93
84,516
34,609
2
11,515
11,517
3,785
73,265
77,050
1,084
55,710
56,794
35,525
374,221
409,746
83,452
625,619
709,071
448
87,334
87,782
2,521
161,817
164,338
58,581
311,072
369,653
11,184
471 ,385
482,569
196,675
2,256,454
2,453,129
THC
124
95,462
95,586
2
14,020
14,022
5,595
82,343
87,938
1,472
62,203
63,675
42,830
417,494
460,324
93,828
683,314
777,142
492
95,116
95,608
2,900
181,858
184,758
74,045
352,414
426,459
16,126
541,158
557,284
237,414
2,525,382
2,762,796
TSP
108,516
61,845
170,361
10
8,689
8,699
24,439
53,620
78,059
48,757.
50,255
99,012
64,086
264,675
328,761
64,911
321,982
386,893
.15,753
42,526
58,279
74,332
84,730
159,062
66,498
169,497
235,995
39,242
457,637
496,879
506,544
1,515,456
2,022,000
CO
313
472,716
473,029
8
70,873
70,881
6,773
322,990
329,763
7,940
313,328
321,268
222,882
1,871,609
2,094,491
472,699
2,910,992
3,383,691
1.-46S"
491,348
492,813
63,704
827,173
890,877
107,936
1,310,905
1,418,841
72,817
2,370,291
2,443,108
956,537
10,962,225
11,918,762
HCL
0
0
0
0
0
0
0
0
0
0
0
0
1,717
0
1,717
10,009
0
10,009
0
0
0
0
0
0
0
0
0
0
0
0
11,726
0
11,726
HF
0
0
0
0
0
0
0
0
0
46
0
46
1.557
0
1,557
1,213
0
1,213
59
0
59
0
0
0
260
0
260
1,368
0
1,368
4,503
0
4,503
MH3
52
765
817
1
834
835
864
1,214
2,078
121
1,360
1,481
0
24,766
24,766
0
44,795
44,795
925
21,286 '
22,211
33
33,620
33,653
45,488
27,626
73,114
0
9,214
9,214
47,484
165,480
212,964
A complete breakdown of resolved emissions by Province and SCC for point and area sources can be found in Appendix A.
7-3
-------
provinces of Ontario and Quebec show the greatest emissions magnitudes for
many of the pollutants, reflecting Canada's concentration of population and
the corresponding concentration of transportation, electricity generation, and
industrial processes in those provinces.
Canadian emissions by category for SC^, NOX, VOC, and TSP are listed in
Table 7-3. The data in this table show that fuel combustion and industrial
process emissions are the dominant source of SO2 emissions, whereas the
transportation sector is clearly the major source of NOX emissions. VOC and
TSP emissions are emitted by a wide variety of sources, with industrial
process, transportation, and miscellaneous area source emissions contributing
large quantities of VOC and TSP emissions. The data in Tables 7-2 and 7-3
are organized similarly to the data presented in Section 3 for the 1985 United
States NAPAP Emissions Inventory Version 2. Emissions estimates for some
particular categories are represented in Table 7-3 as not applicable. The
emissions for these categories are included in other Canadian area source
categories because of differences between U.S. and Canadian reporting methods.
Although the agreement between the 1985 Canadian National Emissions
Inventory and the 1985 Canadian NAPAP Emissions Inventory is essentially
within 5%, there are minor differences between the 1985 Canadian National
Emissions Inventory (Table 7-1) and the 1985 Canadian NAPAP Emissions
Inventory summarized in Tables 7-2 and 7-3. The specific factors that
contribute to these differences are listed below:
1) The 1985 Canadian National Emissions Inventory was
adjusted in March 1989 based on additional comments
received from the provincial environment agencies. These
comments were received after the completion of the
processing'of the 1985 NAPAP Modelers' Emissions
Inventory. Specifically, N©2 and CO emissions from fuel
wood combustion in British Columbia and N0£ emissions in
Ontario from some additional industrial sources are
included in the 1985 Canadian National Emissions Inventory
but are absent from the 1985 Canadian NAPAP Emissions
Inventory.
2) Several emissions categories in the 1985 Canadian National
Emissions Inventory were not included in the 1985 Canadian
NAPAP Inventory so that consistency could be maintained
between the U.S. and Canadian databases. The removal of
selected Canadian area source categories from the 1985
NAPAP Modelers' Emissions Inventory Version 2 is discussed
later. In particular, TSP emissions resulting from
pesticide application in the Canadian prairie provinces
are not represented in the 1985 Canadian NAPAP Emissions
Inventory.
7-4
-------
TABLE 7-3. 1985 CANADIAN NAPAP EMISSIONS INVENTORY BY CATEGORY FOR SO2,
VOC AND TSP (103 TONS)a
NO,
EMISSIONS CATEGORIES
FUEL COMBUSTION
External Combustion
Resident i a I
Coal
Disti I late Oi I
Natural Gas
Wood
Electric Generation...
Coal
Lignite.. ...... ...
Residual Oil.. .
Distillate Oil
Process Gas
Other (Misc Diesel).
Industrial?
Coal
Lignite .
Residual Oil .
Disti I late Oi I .
Natural Gas. . .
Coke
Uood
LPG
Bagasse. ...........
Other
Commerc i a I / I nst i tut i on
Coal
L igni te ...
Res idua I Oil
Disti I late Oi I
Uood
LPG
Other..
AREA
S02 NOX VOC
296 168
296 168
42 45
3 0
' 34 15
0 0
0 26
4 4
1 11
NA NA
NA NA
0 0
0 0
0 4
NA NA
1 7
226 83
112 13
NA NA
3 2
110 22
0 46
NA . NA
NA NA
NA NA
0 1
NA NA
NA NA
26 28
1 0
NA NA
8 4
18 4
0 20
NA NA
0 1
NA NA
82
82
78
2
4
0
0
72
1
NA
NA
0
0
0
NA
1
2
0
NA
0
1
1
NA
• NA
NA
0
NA
NA
2
0
MA
0
0
1
NA
0
NA
NA signifies not applicable
Does not include emissions from Yukon
which reported the following emissions
S02
Yukon
Northwest Terr.
0
3
TSP S02
244
244
175
2
2
0
1
170
1
NA
NA
0
0
0
NA
1
65
55
NA
o
9
1
NA
• NA
NA
0
NA
NA
3
0
NA
0
1
1
NA
0
NA
931
931
NA
NA
NA
NA
NA
818
658
80
73
0
0
NA
7
114
1
NA
16
6
71
0
1
NA
0
NA
19
0
NA
NA
0
NA
NA
NA
NA
NA
and the Northwest
(106 tons):
NOX THC
3
14
7
7
POINT
NOX
449
288
NA
NA
NA
NA
NA
255
201
40
12
0
3
NA
0
32
0
NA
2
2
18
0
0
NA
0
NA
9
0
NA
NA
0
NA
NA
NA
NA
NA
VOC
7
5
NA
NA
NA
NA
NA
2
1
0
0
0
0
NA
0
.3
0
NA
0
1
5
0
0
NA
0
NA
2
0
NA
NA
0
NA
NA
NA
NA
NA
TSP
104.695
104.358
NA
NA
NA
NA
NA
99
62
31
5
0
0
NA
0
6
0
NA
0
0
3
0
0
NA
0
NA
2
0
NA
NA
0
NA
NA
NA
NA
NA
Territories,
All industrial fuel combustion is reported in the area source file except
for petroleum refineries and natural gas processing.
7-5
-------
TABLE 7-3. (continued)
EMISSIONS CATEGORIES
Internal Combustion*:...
Electric Generation...
Disti Hate Oi 1
Natural Gas
Other
Distillate Oil
Natural Gas
Gasol ine
Diesel Fuel
Other
Commerc i al / I nst i tut i on
Engine Testing
INDUSTRIAL PROCESS
Chemical Manufacturing..
Food/Agriculture.
Primary Metals
Secondary Metals
Mineral Products
Petroleum Industry
Wood Products
Organic Solvent Evap
Petroleum Storage/Trans.
Metal /Fabrication
Textile Manufacturing...
Other/Not Classified
SOLID WASTE DISPOSAL
Government
Municipal Incineration
Open Burning
Other Incineration
On-site Incineration..
Commercial/ Institutional
On-site Incineration..
Open Burning
Other
Industrial
On-site Incineration..
Open Burning
Other
so2
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
4
NA
0
NA
NA
0
NA
0
0
0
NA
NA
4
1
NA
NA
NA
NA
NA
NA
NA
1
1
NA
NA
NA
NA
NA
NA
AREA
NOX
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
NA
0
NA
NA
0
NA
0
0
0
NA
NA
0
7
NA
NA
NA
NA
NA
NA
NA
7
7
NA
NA
NA
NA
NA
NA
VOC
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
318
NA
0
NA
NA
0
NA
1
0 .
25
NA
NA
292
7
NA
NA
NA
NA
NA
NA
NA
7
7
NA
NA
NA
NA
NA
NA
TSP
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
684
NA
87
NA
NA
15
NA
112
0
0
NA
NA
470
39
NA
NA
NA
NA
NA
NA
NA
39
39
NA
NA
NA
NA
NA
NA
so2
0
0
o
o
NA
o
0
0
NA
NA
NA
NA
2727
66
NA
1839
209
38
187
69
0
o .
0
0
318
0
0
0
NA
0
NA
NA
0
NA
NA
NA
0
0
NA
NA
POINT
NOX
161
4
2
3
NA
157
0
157
NA
NA
NA
NA
84
12
NA
12
2
11
23
14
0
0
0
0
10
1
1
1
NA
0
NA
NA
0
NA
NA
NA
0
0
NA
NA
VOC
1
0
o
o
NA
1
0
1
NA
NA
NA
NA
189
130
NA
10
2
0
28
5
5
8
0
0
1
1
1
1
NA
0
NA
NA
0
NA
NA
NA
0
0
NA
NA
TSP
0
0
o
o
NA
o
0
o
NA
NA
NA
NA
401
9
NA
170
13
70
13
126
0
0
0
0
0
1
0
0
NA
0
NA
NA
0
NA
NA
NA
0
0
NA
NA
Internal combustion area sources are included in the external combustion
area source totals.
7-6
-------
TABLE 7-3. (continued)
EMISSIONS CATEGORIES
TRANSPORTATION
Land Vehicles
Light Duty Vehicles.
Light Duty Trucks...
Heavy Duty Vehicles.
Off -Highway
Diesel Fuel
Light Duty Vehicles.
Heavy Duty Vehicles.
Off-Highway
Rai 1
Aircraft f
Mi I i tary
Civil
Conmercial
Vesse Is
Coal, Oil, Diesel
MISCELLANEOUS AREA
Forest F i res
Forest Managed Burning..
Agricultural Burning^. ..
Structural Fires
Gasoline Stn. Evap. Loss
Solvent Evap. Loss
Concrete Batching
Other
ADDITIONAL AREA
POTWs
Cutback A
SOCHI Fugitives
Bulk Terminals/Plants...
Refinery Fugitives
Bakeries
Pharmaceutical Mfg
Synthetic Fiber Mfg
Oil/Natural Gas Fields..
TSDFs
GRAND TOTAl
so2
99
62
15
9
4
1
1
• 47
2
20
16
8
0
NA
NA
NA
37
37
0
0
0
NA
0
0
0
0
0
0
0
NA
0
NA
NA
NA
0
NA
NA
NA
NA
400
AREA
NOX
1323
1304
621
429
134
27
31
683
4
280
246
152
1
NA
NA
NA
18
18
0
51
29
NA
22
0
0
0
0
0
0
NA
0
NA
NA
NA
o
NA
NA
NA
NA
1548
VOC
1125
1084
1009
637
250
55
66
76
1
38
25
11
7
NA
NA
NA
33
4
29
722
156
NA
139 ,
6
120
300
0
0
3
NA
0
NA
NA
NA
3
NA
NA
NA
NA
2256
TSP
106
103
49
35
8
4
3
54
1
24
22
7
0
NA
NA
NA
4
4
0
385
123
NA
242
7
0
0
4
9
58
NA
58
NA
NA
NA
o
NA
NA
NA
NA
1515
so2
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
3659
POINT
NOX
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
533
VOC
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
197
TSP
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
507
Evaporative VOC emissions from gasoline motor vehicles are not included in
these emissions sums.
Includes emissions from area source category 23260, Piston Engine-Inflight.
Slash burning is reported as agricultural burning.
7-7
-------
3) The VOC emissions reported in the 1985 Canadian National
Emissions Inventory were augmented to account for the
emissions of aldehydes that were not properly represented
by the application of VOC emission factors.
4) Canadian mobile source VOC emissions were augmented to
account for excess evaporative losses that were not
included in the Version of MOBILES used by Environment
Canada to estimate mobile source emissions.
5) The use of application default VOC speciation profiles for
coal mines was inappropriate for sources in Canada which
resulted in higher estimates of VOC and lower estimates of
methane for these sources.
6) Emissions estimates for forest fires were included in the
1985 Canadian NAPAP Emissions Inventory. Emissions from
these sources were not included in the 19d5 Canadian
National Emissions Inventory.
7) Emissions data were developed and reported in the 1985
Canadian National Emissions Inventory by SIC. The
emissions data record format was also different for the
1985 Canadian National Emissions Inventory relative to the
format structure required by FREDS to process the 1985
NAPAP Emissions Inventory Version 2. Small differences
can be expected when a complex database such as the 1985
Canadian National Emissions Inventory'are subject to
significant manipulations and transformations.
POINT SOURCE DATA
Point Source Data Collection
Emissions and facility data for the 1985 Canadian NAPAP Emissions
Inventory were collected in a cooperative effort between Environment Canada
and the provincial governments. Provincial environment agencies collected
emissions and operating data from the sources in their jurisdictions by
sending out a questionnaire requesting specific information or from provincial
permits. Emissions data were obtained directly from the source or estimated
using facility operating data. Environment Canada regional and district
offices located in most provinces assisted in coordinating the data collection
effort. Some of the point source operating data were also obtained through
reporting channels established directly between specific industries and the
federal government. The amount of quality assurance (QA) performed on these
data at the provincial level varied from one province to another, depending on
resources and time.
7-8
-------
These data were then transmitted to Environment Canada, the federal air
pollution control agency. Environment Canada's Inventory Management Division
entered the emissions and operating data into a database covering all 12
provinces and territories. Delays in the delivery of provincial emissions
data and missing background information on many sources limited the amount of
preliminary QA Environment Canada was able to perform before transmitting the
Canadian emissions and operating data to the U.S. EPA. These emissions data
are representative of all point sources emitting 100 metric tons or greater of
SC-2, NOX, THC, CO or TSP. Environment Canada supplied TSP data speciated into
reactive alkaline fractions of calcium, magnesium, potassium, and sodium.
Because acid deposition modelers in the United States consider emissions of
hydrogen fluoride (HF) and hydrogen chloride (HC1) to be important in modeling
chemical processes leading to acid deposition, EPA calculated HF and HC1
emissions on an SCC specific basis for the Canadian point sources. A
discussion of HC1 and HF emission factors is presented in Section 4.
The methods, for reporting various data items differed from province to
province. Guidance provided to the provincial governments by Environment
Canada included requests for emissions data for all of the pollutants
identified above. Some of the provinces, however, had very little emissions
data on THC, CO and TSP. To provide consistency in VOC emissions estimates,
Environment Canada requested that EPA calculate VOC emissions for Canadian
point sources from reported throughput values and NEDS emission factors to
fill in the data gaps. Environment Canada calculated the emissions for the
data gaps on the other common pollutants. See Section A for a discussion of
VOC emission factors.
Fuel use data for industrial sources we're not available from all
provinces. Therefore, the emissions data for most of the industrial fuel
sources- for Canada were included in the Canadian area source file. Fuel use
data for combustion sources at electric utilities, petroleum refineries, and
natural gas plants were available to Environment Canada and were included in
the point source file.
The methodology for reporting location data also varied between
provinces. Most provinces supplied values for latitude and longitude in
degree/minute/second format. One province used its own location system,
another province reported fractional values for latitude and longitude data,
and some provinces had missing location data which had to be supplied by
Environment Canada. All location data were standardized to fractional values.
Stack parameter data as reported by the provinces were not complete. The
stack parameter data for Canadian point sources were often reported as missing
or zero as was the case for sources in the United States. Substitute stack
parameter data were developed for the largest Canadian points for use in
regional modeling applications following methodologies similar to those
described in Section 3 for U.S. point sources.
Provinces also had individual methods for reporting throughput data.
Under Environment Canada guidelines, fuel combustion processes should have
7-9
-------
indicated a fuel quantity (throughput) and a fuel quantity unit, and other
processes not related to combustion should have had a base quantity
(throughput) and corresponding base quantity units. Significant
inconsistencies occurred in the reporting of throughput data and the
associated units. In some cases, the fuel or base quantity units for a given
process varied widely. Many throughput values were Deported as zero or
missing even though positive emissions values were reported. A significant
effort was performed cooperatively between the U.S. EPA and Environment Canada
to establish as many throughput values as possible. The resolution of
Canadian throughput values was also important for the calculation of HC1, HF,
SOA, and NH3.
Canadian Point Source Record Format
Environment Canada used a substantially different point source record
format than that used for U.S. point sources. The quality assurance and data
processing software programs used by the U.S. EPA were modified to accommodate
these differences in record format.
The point source record format used by Environment Canada is based on the
Standard Industrial Classification Code (SIC) rather than a Source
Classification Code (SCC) as in the U.S. data. Environment Canada uses a
different SIC system than that used by the U.S. EPA. In addition, records
were aggregated by pollutant rather than by process, meaning that for every
process record in a Canadian plant, there may be as many as 7 individual
pollutant records. An additional step was required to process the Canadian
data to ensure that the data for each pollutant were aggregated correctly to
the process (SCC) level. The aggregation of contaminant based records to
process based records required additional QA to resolve apparent duplicate
records which appeared'when the contaminant level records were combined.
The assignment of U.S. SCCs to Canadian point source records was
completed to the extent possible by personnel at Environment Canada or the
provincial environment agencies familiar with the plants and processes in
their jurisdiction. In many cases, the existing SCC listings did not match
exactly the processes included in the Canadian data submittal. Two approaches
were taken at the provincial level to complete the forms in these cases. Some
provinces applied an unused SCC code for the process, and others entered
00000000 or 99999999. The resolution of the 0 and 99999999 SCCs involved
replacing these SCCs with SCC codes that closely matched the process
description available in the Canadian data base. Industrial processes in
Quebec which had French process descriptions further complicated the process
of matching valid and appropriate SCCs to point source records. All point
source records were eventually matched to representative SCCs.
The Canadian data submittals included several SCC process classifications
which had not been used in the U.S. data. The data processing for input into
acid deposition models requires that a TSP and VOC speciation profile be
associated with every SCC included in the file. Therefore, Environment Canada
requested that EPA make appropriate speciation profile assignments for those
7-10
-------
SCCs. In some cases, the process descriptions were not easily matched to
specific profiles. In these cases, EPA assigned industry average default
profiles or the overall average default profile. A list of the additional
Canadian SCCs and the profile assignments that will be applied to those SCCs
follows in Table 7-4. In some cases, the SCC-to-process matching exercise and
the assignments of speciation profiles to SCCs led to the application of
inappropriate speciation profiles as discussed earlier.
Temporal, Spatial, and Species Allocation of Canadian Point Source Data
The temporal allocation of Canadian point sources was based on plant
level temporal operating profiles supplied by Environment Canada with their
emissions data files. These files consisted of a year file with monthly
throughput percentages, a week file with throughput percentages for each day
of the week, and a day file with hourly throughput percentages. This temporal
allocation scheme differs from the U.S. methodology which is based on SCC-
level allocation factors. The Canadian temporal allocation data were
formatted to be consistent with the requirements of FREDS and FREDS was
modified to properly allocate Canadian emissions based on the Canadian
allocation factors.
The spatial allocation of point source data is based on locating points
using latitude and longitude coordinates. Environment Canada provided
latitude and longitude coordinates primarily in degree, minutes and seconds
format which had to be converted to decimal format for use in the Spatial
Allocation Module (SAM) of FREDS. The SAM locates each latitude/longitude
point in a NAPAP grid cell. Some problems encountered with variability
between provinces in reporting location data were discussed earlier.
The species allocation is accomplished in the FREDS speciation module and
results in emissions estimates for a total of 59 species classes. These
species are retained in the 1985 NAPAP Modelers' Emissions Inventory
Version 2. NOX is allocated into NC^ and NO, TSP and reactive alkaline
particulates into three particle size classes, and THC into 32 chemical
classes. Environment Canada supplied reactive alkaline fraction data for TSP
in the Canadian point source data files.
AREA SOURCE DATA
Data Collection
Canadian area source emissions estimates were developed much like the
U.S. area source calculation methodology discussed in Section 3. The
estimation of area source emissions in Canada was performed directly by
Environment Canada and provided to the provincial environment agencies for
their review. The Canadian area source calculation methodologies include
several categories for which the United States does not calculate emissions.
A primary concern for the NAPAP emissions inventory was to maintain
consistency of data content and format between the U.S. and Canadian databases
to the extent possible.
7-11
-------
TABLE 7-4. VOC PROFILES FOR EXTRA CANADIAN SCC's
CANADIAN SCC
10100104
10200692
10200693
10201499
10299999
20100291
20100292
20200292
3010229°
30102702
30102703
30103005
30300191
30300192
30300799
30300905
30501991
30600195
30600299
30610099
30700194
30700196
30700201
30700208
30700209
30700210
30900360
31000306
35001001
40300000
40301999
50100103
50100504
50300199
VOC PROFILE
1178
3
3
0
0
1001
1.001
1001
0
0
0
0
0
0
0
16
0
9012
9012
9012
9001
9001
'9001
9001
90Q]
9001
0
0
0
0
9024
122
122
9022
ASSIGNMENT CRITERIA
Based on SCC's 101001XX
Based on SCC's 102006XX
Based on SCC's 102006XX
Overall average profile
Overall average profile
Based on SCC 20100202
Based on SCC 20100202
Based on SCC 202002002/04
Based on SCC 30102201
Based on SCC's 301027XX
Based on SCC's 301027XX
Based on SCC's 301030XX
Based on SCC's 301001XX
Based on SCC's 301001XX
Overall average profile
Based on SCC 30300904
Based on SCC's 305019XX
Based on SCC 30600199
Based on SCC 30600199
Based on SCC 30610001
Based on SCC's 307001XX
Based on SCC's 307001XX
Based on SCC's 3070Q1XX
Based on-SCC's 307001XX
Based on SCC's 307001XX
Based on SCC's 307001XX
Based on SCC's 309003XX
Overall average profile
Overall average profile
Overall average profile
Based on SCC 40301299
Based on SCC's 501001XX
Based on SCC's 50100505/06
Based on SCC 50300108
The 1985 Canadian National Emissions Inventory contains 156 area source
categories compared to the U.S. total of 97. Some of these additional
categories represent fuel burning sources or industrial processes and were
retained in the Canadian area source file to provide consistency with similar
sources in the U.S. point source file. Eighteen Canadian area source
categories had no corresponding category in either the U.S. point or area
source file and were extracted from the database prior to the development of
7-12
-------
the 1985 Canadian NAPAP Emissions Inventory. A list of the Canadian area
source categories that were extracted from the file before processing through
FREDS is presented in Table 7-5. An additional 9 Canadian area source
categories were included in the 1985 Canadian Natural Particulate Emissions
Inventory. A complete list of the 129 Canadian area source categories that
were processed through FREDS to produce the 1985 NAPAP Modelers' Emissions
Inventory Version 2 can be found in Appendix C.
Temporal, Spatial, and Species Allocation of Canadian Area Source Data
Canadian area source data files were accompanied by SCC-specific temporal
profiles. Temporal data were reported in the form of throughput percentages
for each month of the year, day of the week, and hour of the day. The monthly
percentages were converted to four seasonal fractions. Throughputs for
weekdays were averaged to produce daily profiles consisting of factors for a
typical weekday, Saturday, and Sunday. Forest fires, the only category which
had not been assigned specific temporal allocation factors by Environment
Canada, was assigned the U.S. temporal factors for forest fires.
Spatial allocation surrogates provided by Environment Canada included
population, housing units, gas dwellings, oil dwellings, agricultural labor
force, industrial labor force, commercial labor force, and mining labor force.
These data were obtained from Statistics Canada surveys and were reformatted
to the NAPAP grid system by Environment Canada. The statistics were for
calendar year 1981 except for population statistics, which were based on 1986
Canadian census data. The census data were summed for each province to obtain
provincial totals, and a spatial fraction was calculated for each grid cell.
Each of the Canadian area source categories was matched to the most
appropriate surrogate based on an analysis of the 1980 allocation factors,
creating the surrogate selection file. The Spatial Allocation Factor
Preprocessor (SAFP) used for processing U.S. area source data was modified and
run on the Canadian area source data. The population surrogate was used as a
default category for Canadian area source categories which either lacked a
specific allocation surrogate or for which the assigned surrogate equaled zero
when summed at the province level. SAFP matches a spatial fraction for each
grid cell to each area source category based on the appropriate surrogate
distribution.
Species allocation for Canadian data also used the PSPLIT program,
resulting in the split of NOX into N02 and NO, TSP and reactive alkaline
particulate fractions into three particle size classes and VOC into thirty-
two chemical classes. Environment Canada provided the reactive alkaline
fraction data.
1985 CANADIAN NATURAL PARTICULATE EMISSIONS INVENTORY
Environment Canada supplied the natural source data developed for the
1980 NAPAP Inventory based on methodologies developed in the early 1980s.
7-13
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TABLE 7-5. CANADIAN AREA SOURCE CATEGORIES THAT WERE MOT PROCESSED BY FREDS
CANADIAN
AREA SOURCE CODE
PROCESS DESCRIPTION
81000
21150
23210
23230
23240
23250
26100
26200
52120
65000
43100
46100
44000
45100
45200
45300
45400
45500
Cigarette Smoking
Motorcycles
Jet Aircraft - Inflight
Piston Transport - Inflight
Helicopter - Inflight
Local Aircraft - Inflight
4-Wheeled Vehicles - Tire Wear
8-Wheeled Vehicles - Tire Wear
Crude Oil Production - Evaporation
From Ships
Pesticide Application
Wind Erosion From Land Tilling
Erosion From Tailing Piles - Fugitive
Landfill Sites - Fugitive
Construction Sites - Residential
Building
Construction Sites - Other Building
Construction Sites - Utilities, Water,
Sewer
Construction Sites - Roads, Bridges,
Tunnels
Construction Sites - Ojther Heavy
Construction
4
Environment Canada personnel felt that natural source emissions had not
changed appreciably from 1980 to 1985, and resource constraints prevented
any further research to refine natural source emissions estimates. Because
the data were the same as those reported in 1980, the tape sent by Canada
contained annual emissions data and supporting temporal data. EPA
recalculated biogenic hydrocarbon emissions for both the United States and
Canada using land use and land cover data and a canopy biogenic emissions
model developed by NAPAP for modeling applications.
QUALITY ASSURANCE AND DATA PROCESSING OF POINT AND AREA SOURCE DATA
Quality Assurance of Point Source Data
Extensive quality assurance (QA) was performed on Canadian point source
data. An adaptation of the NE061 program used on U.S. data was initially
performed. The results of the execution of the QA program were used to rank
QA measures on the data. Rejected records contained errors which would result
7-14
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in incomplete processing of the data by FREDS. These errors included missing
or invalid province codes, zero or missing SCCs, missing or invalid plant IDs.
The original edit checking messages for rejected records were broken down
into the following categories:
Plant ID is missing or invalid 201
S02 emissions > 100,000 Metric tons 2
TSP emissions > 100,000 Metric tons 1
CO emissions > 100,000 Metric tons 1
Latitude is missing 53A
Longitude is missing 534
Missing or invalid day code 141
Missing or invalid week code 141
Missing or invalid year code 141
SCC code not on file 241
Plant IDs and emissions over 100,000 metric tons per year were verified with
Environment Canada and ranges for these reject messages or field definitions
for particular variables were adjusted. Missing latitude and longitude data
were obtained by consulting with Environment Canada personnel, as were missing
temporal codes. Temporal codes with zero or missing values were given a
continuous operating profile reflecting continuous operation. For SCC codes
with zero or invalid values, consultation with Environment Canada personnel
provided either valid SCC codes or process descriptions from which valid SCC
codes could be assigned.
Whenever- Environment Canada's independent QA resulted in modifications to
the data files, the information to be deleted, added, or changed was
incorporated. Each time changes were made or files were added, a QA check for
records which appeared to be duplicates was carried out, and any problems with
apparent "duplicate" records were resolved. Environment Canada's point source
records are based on a sector SIC code which is not used in the processing of
the 1985 NAPAP Modelers' Emissions Inventory Version 2. At times records may
appear to be duplicates in the modelers' inventory which are in fact unique
records when the SIC code is used. Other apparent duplicate records resulted
from the presence of 3-digit point ID codes for Canadian points, so the field
definition for point ID was modified to accommodate the potential for these
Canadian point IDs. As a final check, emissions totals for all pollutants by
province and by SIC were verified by Environment Canada and compared against
the 1985 Canadian National Emissions Inventory.
Point Source Data Processing and Modification
Certain data items in the original Canadian data files sent to the United
States EPA were modified to make them consistent with U.S. data. Latitude and
longitude values were converted from degree to decimal values. Throughput
values were converted to English units to match corresponding NAPAP emission
factor units. For throughputs which could not be converted to English units
because of the unavailability of specific density values, the throughputs were
7-15
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reported as missing. This measure was taken to avoid confusion on the part of
users of the data who might make calculations using throughput values without
looking at the corresponding units.
Monthly temporal data were summed to the seasonal level, daily throughput
percentages were aggregated to the typical weekday, Saturday, and Sunday
scenarios, and hourly throughput data were retained and applied to the
Canadian emissions data. These aggregations were used to form a temporal
factor file which was linked with a matching variable during FREDS processing
to temporally allocate emissions data. Temporal processing resulted in a
resolved point source file with hourly emissions values for the twelve
temporal scenarios (weekday, Saturday, and Sunday in each of the four
seasons). Where temporal profiles were not available, emissions received
uniform temporal allocation.
Quality Assurance of Canadian Area Source Data
Quality assurance of Canadian area source data was much simpler than QA
of point source data because there were fewer area source records. The major
problems encountered were duplicate records, miscoded SCCs, emissions
magnitudes larger than maximum values set by Environment Canada, missing or
invalid temporal codes, and throughput values not equal to 100 percent. The
majority of these problems were quickly resolved through consultation with
Environment Canada personnel. As a final QA check, emissions totals by
province and by area source category for each of the major pollutants were
compared with emissions totals from data files at Environment Canada.
While reviewing Canadian data which had been processed by FREDS,
Environment Canada personnel noted that area source coal processing SCCs had
incorrect PSPLIT profiles. These sources should reflect essentially 100
percent methane. The impact of this change on acid deposition modeling
studies is minimal since nearly 90 percent of these emissions are 'in the
Province of Alberta, which is outside the modeling domain.
In addition, emissions from area source category 23260, Piston Engine -
Inflight, were processed through FREDS but should have been included in the
list of Canadian area source categories not processed by FREDS for the 1985
NAPAP Modelers' Emissions Inventory Version 2. The emissions from this area
source contribute 52 tons SC>2, 78 tons NOX, 6790 tons VOC, 100 tons TSP, and
55,044 tons CO. Again, the impact on acid deposition modeling studies is
small because the emissions contribution from the category is small.
Canadian Area Source Data Processing and Modification
The Canadian highway gasoline mobile source categories were adjusted to
account for the inclusion of the excess evaporative emissions following the
same methodology used to adjust the U.S. mobile source file. The adjustment
for Canadian mobile sources did not consider running losses since the MOBILE3
assumptions for Canada included the average annual temperature for all of
Canada of 46 degrees Fahrenheit, well below the temperature range of
7-16
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importance for running losses. A separate mobile source file for the summer
scenarios was created specifically for model evaluation studies. The separate
mobile source file can be adjusted for temperature specific cases and includes
the effects of running losses.
Canadian area source hydrocarbon data was supplied as THC. The FREDS
hydrocarbon preprocessor was initialized to calculate VOC from the THC
estimate and the profile methane concentration. In contrast, all U.S.
hydrocarbon data were supplied as VOC by the States.
7-17
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SECTION 8
NATURAL SOURCES
INTRODUCTION
A significant amount of research on natural source emissions in the
United States has been conducted by the National Oceanic and Atmospheric
Administration (NOAA) for NAPAP's Task Group on Atmospheric Chemistry (Task
Group II) and EPA's Atmospheric Research and Exposure Assessment Laboratory
(AREAL). Estimates of emission factors, flux estimates and emissions
inventories have been developed by NOAA and AREAL for biogenic hydrocarbons,
soil NOX and alkaline particulate matter. For the 1985 NAPAP Emission
Inventory Version 2 only emissions of alkaline particulate matter were
available in a format readily adaptable to the NAPAP inventory.
The results of preliminary emissions estimates for U.S. natural
particulate sources were included in the 1985 NAPAP Modelers' Emissions
Inventory Version 2 to meet delivery deadlines. Research conducted by NAPAP
Task Group II since the delivery of the modelers' inventory has resulted in
modifications to the national and state level emissions estimates. Further
research conducted by NAPAP Task Group I has also resulted in modifications to
the county allocation methodologies. Therefore, the emissions totals
discussed in this report do not agree with other published NAPAP research
results.
Methodologies have been developed by EPA to apply the results of research
on biogenic hydrocarbon emissions to the RADM model input data base.
Specific temperature and solar intensity data are used by EPA'to calculate
grid level hydrocarbon emissions for each hour of specific RADM simulations.
Soil NOX emissions are also dependent on temperature and have been calculated
by EPA following methodologies similar to those applied for biogenic
hydrocarbons.
An inventory of alkaline particulate emissions from natural sources was
developed by NAPAP for use as input to atmospheric transport models. The
contributions from natural sources can affect the chemical composition of
atmospheric deposition.
Emissions inventories for alkaline particulate matter from U.S. natural
sources have been developed for three categories: unpaved roads, wind erosion
and dust devils. Data were provided by Task Group II for total particulate
mass and the mass of total calcium (Ca), magnesium (Mg), sodium (Na) and
potassium (K). Data sources for calculation of annual and speciated natural
particulate emissions include field measurements, AP-42 , and particulate
matter species profiles . Unpaved road emissions were calculated at the
county level by NAPAP Task Group I. Wind erosion and dust devil emissions
estimates were provided by Task Group II for major land resource areas
(MLRAs). These emissions estimates were resolved to the county level by Task
Group I. Temporal, spatial and species resolution of the natural particulate
emissions were derived from information provided by Task Group II and EPA.
8-1
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Environment Canada supplied natural source particulate emissions
estimates from paved roads, unpaved roads and wind erosion at the provincial
level. Temporal, spatial and species resolution were supplied by Environment
Canada. A summary of U.S. and Canadian natural particulate categories
included in the 1985 NAPAP Modelers' Emissions Inventory Version 2 is provided
in Table 8-1. A summary of the particulate emissions data for natural sources
for the United States and Canada is presented in Table 8-2. The natural
particulate emissions are speciated into the same reactivity and size
fractions as the anthropogenic TSP (see Table 6-9).
TABLE 8-1. NATURAL ALKALINE PARTICULATE CATEGORIES
INCLUDED IN 1985 NAPAP VERSION 2
U.S. SCC
CATEGORY DESCRIPTION
901
902
903
Unpaved Road Travel
Wind Erosion Natural and Agricultural Lands
Dust Devils
CANADIAN SCC
CATEGORY DESCRIPTION
41110
41120
42110
42120
42210
42220
42310
42320
43200
Dust - Paved Roads
Dust - Paved Roads
bust Unpaved Roads
Dust Unpaved Roads
Dust Unpaved Roads
Dust Unpaved Roads
Dust Unpaved Roads
Dust Unpaved Roads
- Vehicles
- Trucks
- Vehicles - Treated Gravel
- Trucks - Treated Gravel
- Vehicles - Untreated Gravel
- Trucks - Untreated Gravel
- Vehicles - Earth Roads
- Trucks - Earth Roads
Agricultural - Erosion From Crops
DEVELOPMENT OF COUNTY LEVEL ALKALINE PARTICULATE EMISSIONS
Unpaved Roads
Particulate emissions from unpaved roads are based on the flux of
particulate matter to the atmosphere due to the disturbance of the road
•surface by passing vehicles. The magnitude of the emissions depends on
vehicle speeds and road surface composition.
8-2
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State-level emission factors (Ib/VMT (vehicle miles traveled)) for
unpaved roads in the United States were provided by Task Group II for
particulates of 10 microns or less. The emission factor was multiplied by the
activity level (VMT for unpaved roads) and the weight percent of the alkaline
components to determine state level annual emissions for unpaved roads. The
emissions from the unpaved road category developed by EPA as part of NEDS were
removed from the anthropogenic area source inventory to avoid double counting
emissions from this source.
The source extent of local and nonlocal unpaved roads was obtained from
the U.S. DOT. County level information was not readily available. State-
level, information was used in conjunction with population data and the
methodology developed by Barnard-* to arrive at county level unpaved road VMT.
This methodology used state level VMT in rural, small urban and urban
locations to represent local unpaved roads, and a mean of rural and urban
areas for nonlocal unpaved roads. The state-level VMT was apportioned to
counties based on rural and urban population distributions.
Prior to the allocation of state-level emissions to counties, the VMT
(urban and rural) for various surface types was determined for application of
the appropriate surface chemistry data. State level road surface chemistry
data provided by for NAPAP were used to represent the chemistry of urban and
rural local, and all nonlocal unpaved roads. Local surface soil chemistry
data were used for rural and urban local unimproved unpaved roads. County
level alkaline particulate emission estimates were calculated from the state-
level totals. The details of the procedures for calculating county-level
unpaved road emissions are presented elsewhere .
The county-level emissions estimates developed by EPA were processed
through a series of quality contr&l checks. The data were reformatted and a
category code (SCC) of 901 was assigned to all unpaved road emissions records
to ensure compatibility with the NAPAP annual emissions files. Emissions of
total particulates were calculated by multiplying the PM-10 mass by the
inverse of the size multiplier for the 10 micron fraction. In addition, the
reactive components of Ca, Mg, Na, and K were calculated from data included in
the Air Emissions Species Manual, Volume II .
Wind Erosion
Wind erosion emissions estimates for total and alkaline particulates (Ca,
Mg, Na, K) for the United States have also been developed by Task Group II.
Wind erosion emissions include particulate emissions from soils produced by
the frictional forces of the wind. Total mass for wind erosion is defined as
particles 20 microns or less in diameter. The spatial resolution of the
annual emissions is based on MLRAs. The methodology for calculating annual
wind erosion emissions is based on the 30-year climatological record, and
therefore, is not specific to the year 1985.
8-4
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A U.S. Department of Agriculture Soil Conservation Service map was used
to assign MLRA characteristics to county boundaries. Each county was assigned
to a corresponding MLRA, and the flux for that MLRA was multiplied by the area
of the county to yield annual county-level wind erosion emissions. The
county- level emissions were subjected to a series of quality control checks.
The data were reformatted to make them compatible with the NAPAP annual
emissions files. In addition, the reactive components of Ca, Mg, Na, and K
were calculated from data included in the Air Emissions Species Manual,
Volume II.
Dust Devils
Dust devil emissions estimates for the United States were developed by
Task Group II for the total mass of particles with diameters less than or
equal to 25 microns. Dust devils result from intense local convective
circulation in arid locations and can cause dust to rise up to high levels in
the atmosphere. Flux estimates were based on observations made near Tucson,
Arizona during the Summer of 1987. The field measurement program was
completed to determine the distribution of dust devils in four size
categories - small, medium, large, and extra large. Measurements were made of
the dust concentrations and the vertical velocities within active dust devils.
Vegetation classes and climatic categories were also analyzed to determine the
potential for dust devil development in other regions of the United States.
Flux estimates were developed for each of the four points defining a grid cell
used by the RADM model.
To arrive at annual county-level dust devil emissions, the latitude and
longitude of each grid point were matched to a corresponding county and the
emissions flux for that point was assigned to that county. If more than one
grid point was located in a county, the county level emissions were determined
by averaging the values corresponding to those grid points. For small
counties that lie between grid points, contour maps were used to determine if
a county should have an emissions value assigned to it. If a positive
emissions value was indicated, the average value associated with adjacent or
nearby grid points was assigned to that county. To assure a representative
distribution, county-level maps were compared with contour maps developed from
the gridded data.
The county-level data were subjected to several quality control checks.
The data were then reformatted and a category code (SCC) of 903 was assigned
to all dust devil emissions records. In addition, the reactive components of
Ca, Mg, Na, and K were estimated from values obtained in the Air Emissions
Species Manual Volume II2. The resultant file was merged with the annual
county-level unpaved road and wind erosion particulate emissions to create the
1985 United States NAPAP Natural Particulate Emissions Inventory for use as
input to FREDS.
8-5
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Canadian Natural Particuiate Emissions
Canadian particuiate emissions for paved and unpaved roads and wind
erosion from crop lands were supplied by Environment Canada. The categories
and their corresponding SCCs are presented in Table 8-1. Environment Canada
supplied total particulates, Ca, Mg, Na, K, reactive Ca, reactive Mg, reactive
Na, and reactive K for paved and unpaved roads. Only total particuiate
emissions were supplied for wind erosion. To determine the reactive alkaline
components, several data sources were consulted. The average alkaline
composition of Canadian soils" and the reactivity fractions in the Air
Emissions Species Manual were used to arrive at a best estimate of the
reactive alkaline components. The modified Canadian particuiate data were
used to create the 1985 Canadian NAPAP Natural Particuiate Emissions Inventory
for use as input to FREDS.
Quality Assurance of Annual Emissions Data
A series of checks was performed on the data prior to creation of the
annual inventory to assure the quality of the natural source emissions data.
Quality control checks on county-level annual alkaline particuiate emissions
focused on assuring data reasonableness and compatibility with NAPAP county
codes and allocation files.
Quality control checks on the raw data included matching the emissions
data to the Spatial Allocation Factor File (SAFF) and the Timezone File (TZF)
to identify any missing or miscoded counties. The alkaline components were
summed and compared to total particulates emissions to ensure internal
consistency. Although in most cases the sum of the alkaline components does
not equal the_total particuiate mass they should never exceed the total mass.
Additional checks included manual reviews of the file to identify erroneous
data.
The counties in the SAFF and TZF files were compared with the counties
present in the unpaved road, wind erosion, and dust devil emissions.
Discrepancies were discovered in Massachusetts, Montana, South Dakota, and
Virginia. In Massachusetts, differences occurred because of the
correspondence of the SAFF and TZF to Air Pollution Control Districts (APCDs)
rather than to counties. To adjust for APCDs, emissions data for the
Massachusetts counties were recalculated relative to APCDs based on land area
fractions. In Montana and South Dakota, differences occurred due to
differences in NEDS and FIPS county codes. Information for two missing
counties was calculated and inserted into the emissions files. In Virginia,
discrepancies occurred due to the definition of independent cities which are
treated as county equivalents. The Virginia counties and independent cities
were adjusted for compatibility with the NAPAP files following methodologies
developed for the anthropogenic area source data.
Additional manual examination of the data files for the United States
indicated no apparent problems with the file. Visual examination of the
Canadian data indicated some invalid SAROAD codes assigned to the alkaline
components. These codes were subsequently corrected.
8-6
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Additional quality control checks were performed on the U.S. and Canadian
emissions data during the creation of the masterfiles and the annual FREDS
inventory. These checks included calculation of emissions sums before, during
and after creation of the annual inventory, checks for missing values, and
visual checks for discrepancies and erroneous data. These analyses turned up
missing data in the annual inventory for one county in Montana which had been
manually inserted into the file. Once this error was corrected the annual
inventory was rechecked to assure that no other problems were present.
NATURAL SOURCES ALLOCATION FACTORS
Temporal Allocation Factors
Temporal allocation factors were developed individually for each of the
natural source categories in the United States and Canada. Temporal factors
derived for each source category at varying levels of specificity were
concatenated to form a natural source Temporal Allocation Factor File (TAFF)
for U.S. natural source particulates. Canadian natural source temporal
profiles were supplied by Environment Canada and are included in the Canadian
area source TAFF. The appropriate linker variable was created in the Canadian
annual natural source file for correspondence between the emissions file and
the TAFF.
Unpaved road temporal profiles available in the U.S. area source TAFF
were used to temporally allocate U.S. unpaved road emissions. These factors
are SCC-specific and are based on U.S. DOT data for light duty vehicles on
rural roads.
County specific temporal -profiles for wind erosion emissions in the
United States are based on temporal information supplied by Task Group II.
Seasonal fractions were calculated for each of the alkaline species and total
particulate for each county by summing the monthly emissions for a season and
dividing the seasonal total by the annual emissions. The seasonal values for
total particulates and the alkaline components were combined to calculate
average seasonal values for the U.S. particulate emissions from wind erosion.
The diurnal profile for wind erosion, as recommended by Task Group II, was
derived from a figure by Wigner and Peterson showing the occurrence of
blowing dust by time of day. Daily factors were assumed to be uniform.
Quality control checks were performed on the county specific seasonal
profiles to assure the quality and compatibility of the factors. County codes
were matched to those in the U.S. annual natural source emissions file. Any
nonmatches were adjusted to assure complete temporal allocation. In addition,
all seasonal and diurnal fractions were checked to ensure that they summed to
unity.
Category specific seasonal factors for U.S. dust devil emissions were
based on data recommended by Task Group II. The seasonal distribution of dust
devils are at a maximum between June 1 and September 1, decrease linearly to
zero from September 1 to October 15 and remain at zero between October 15 and
8-7
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April 15. The activity of dust devils begins a linear rise from April 15 to
reach the maximum again on June 1. The diurnal profile recommended by Task
Group II shows maximum activity from 11 AM to 3 PM, a linear decrease from 3
to 4 PM, zero activity from 4 PM to 10 AM, and a linear increase from 10 to
11 AM.
The temporal profiles for each category for each scenario were
concatenated to form the natural source TAFF. As a final check on the
temporal data, seasonal and hourly fractions were totaled to verify that they
summed to unity.
Spatial Allocation Factors
The land area spatial distribution surrogate file was used for spatial
allocation of U.S. natural source emissions. The gridded spatial fractions
were matched to the natural sources Surrogate Selection File to generate a
natural source Spatial Allocation Factor File (SAFF).
Spatial allocation factors for Canadian natural particulates were
provided by Environment Canada. Paved road particulate emissions were
allocated by the population surrogate and the unpaved road and wind erosion
categories were allocated by the agricultural labor force surrogates.
Speciation Factors
The NAPAP Task Group II research results related to natural alkaline
particulate included estimates of total mass and the total mass of alkaline
species for limited size ranges as discussed earlier. Th.ese estimates were
divided into the appropriate size and reactivity fractions to provide
estimates that were consistent with the anthropogenic particulate data.
Reactivity fractions were obtained from published data. The measured data for
unpaved road dust indicated that 24 percent of the total mass was included in
the less than 10 micron size fraction. The total TSP mass was estimated by
multiplying the less than 10 micron size fraction by 4.17, the inverse of the
24 percent fraction.
Size fraction data available in AP-42 suggest that the less than 10
micron size fraction is 362. The measured data for unpaved road dust,
however, indicated 24% of the particulate mass to be less than 10 microns.
Estimates of size fractions for unpaved road dust in size ranges of less than
2.5 micron; less than 5.0 micron; less than 10 micron; less than 15 micron and
less than 30 micron are also included in AP-42. The size fractions for the
2.5 to 6.0 and 6.0 to 10.0 micron ranges were obtained from a linear
regression of the plotted AP-42 data. The AP-42 size fractions were scaled by
the ratio of 0.24/0.36 to develop estimates of the size fractions required for
the NAPAP application.
The same size fractions were applied to both TSP and the individual
alkaline components. These size fractions were used in the speciation of all
three natural source categories. The speciation factors applied to the NAPAP
natural source particulate data are listed in Table 8-3.
8-8
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Size fractions for Canadian natural particulates were available in the
Canadian area source Speciation Factor File (SFF) and the alkaline fractions
were obtained from profiles for the natural source categories that were
included in the Air Emissions Species Manual. The reactive alkaline fractions
for the Canadian data were supplied by Environment Canada.
TABLE 8-3. SPECIATION FACTORS FOR U.S.
NATURAL ALKALINE PARTICULATES
SPECIES
TSP
SODIUM (Na)
POTASSIUM (K)
CALCIUM (Ca)
MAGNESIUM (Mg)
REACTIVE
FRACTION
N.A.
0.0
0.0
0.5
0.5
0-2.5 urn
FRACTION
0.0207
0.0207
0.0207
0.0207
0.0207
2.5-6.0 urn
FRACTION
0.090
N.A.
N.A.
N.A.
N.A.
6.0-10.0 urn
FRACTION
0.129
N.A.
N.A.
N.A.
N.A.
2.5-10.0 urn
N.A.
0.2194
0.2194
0.2194
0.2194
.The annual emissions files and allocation factors described above were
used in conjunction with FREDS to generate a modelers' emissions inventory.
The application and processing of FREDS are discussed in the following
section.
8-9
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SECTION 8
REFERENCES
1. U.S. Environmental Protection Agency. Compilation of Air Pollutant
Emission Factors," Fourth Edition, AP-42 (GPO No. 055-000-00251-7),
Volume 1, Research Triangle Park, NC, September 1985.
2. Shareef, G. and L. Bravo. "Air Emissions Species Manual (NTIS PB88-
225800). Volume II - Particulate Matter (PM) Species Profiles."
EPA-450/2-88-003b. U.S. Environmental Protection Agency, Research
Triangle Park, NC. April 1988.
3. Barnard, W.R., D.F. Gatz, and G.J. Stensland. Evaluation of Potential
Improvements in the Estimation of Unpaved Road Fugitive Emissions
Inventories. Paper 87-58.1, Proceedings of the 80th Annual Meeting of
the Air Pollution Control Association, New York, NY. 1987.
4 Boerngen, J.C. and H.T. Shacklette. Chemical Analyses of Soils and Other
Surficial Materials in the Coterminus United States. USGS Report 81-197,
U.S. Geological Survey. 1987.
5. The Environmental Applications Group Limited. National Inventory of
Natural Sources and Emissions of Alkaline Particulates. DSS Contract
No. 05E81-00164, Environment Canada, Downsview, Ontario, 1982.
6. Wigner, K. and R. Peterson. Synoptic Climatology of Blowing Dust on the
Texas South Plains, 1947-84. Journal of Arid Environments, 13, 199-209.
1987.
8-10
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SECTION 9
EMISSIONS INVENTORY DATA PROCESSING
INTRODUCTION
One of the primary functions of the 1985 NAPAP Modelers' Emissions
Inventory Version 2 is to support regional- and national-scale acid deposition
modeling efforts using the Regional Acid Deposition Model (RADM). As
described in Section 6, the annual data must be resolved temporally (to the
hourly level for a typical weekday, Saturday, or Sunday in each season),
spatially (into grid cells 1/6 degree latitude by 1/4 degree longitude), and
by pollutant species (for hydrocarbons, oxides of nitrogen, and particulate
matter). This additional resolution is provided by applying the NAPAP
allocation factors to annual emissions records and creating a series of data
tapes suitable as model input. The computer software which has been developed
to perform the required resolution is called the Flexible Regional Emissions
Data System (FREDS).1
FREDS consists of seven primary modules written in SAS* and Fortran.
FREDS extracts desired data from the annual inventories, performs temporal and
spatial allocation, speciates selected pollutants, converts data to modeling
format, and performs quality control checking at various stages of processing.
The user can specify the parameters of execution using Control Options Files
associated with each module and can vary the allocation methodology or
speciation chemistry by changing or replacing one or more of the allocation
factor files. This design flexibility allows FREDS to be used for processing
emission inventories for a wide variety of applications.
FREDS was originally developed for processing the 1980 NAPAP Emissions
Inventory. A number of enhancements arid modifications to FREDS were
undertaken to meet the requirements of the 1985 modelers' inventory. Some of
these changes were necessary to accommodate new expanded data requirements,
while others were identified as desirable based on past experience with the
FREDS software. These changes facilitate the timely and cost effective
processing of the 1985 NAPAP Emissions Inventory and ensure high level data
quality while maintaining maximum flexibility for future applications.
Following modifications and comprehensive system testing, FREDS was
executed on the 1985 NAPAP Emissions Inventory Version 1 to produce the first
1985 NAPAP Modelers' Emissions Inventory Version 1. Subsequent modifications
to the annual inventory and the addition of emissions estimates for Canadian
and natural sources required a second FREDS series involving six individual
sets of module runs. The resulting modelers' inventory, including point, area
and natural sources for the United States and Canada, has been designated The
1985 NAPAP Modelers' Emissions Inventory Version 2.
*SAS is a registered trademark of the SAS Institute, Gary, NC, 27511-8000.
9-1
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The remainder of this section describes the FREDS system and its
application to create the NAPAP Modelers' Emissions Inventory Version 2. It
includes an overview of the FREDS modules and their operation, a description
of enhancements made to FREDS for the 1985 inventory development effort, a
summary of modelers' tape processing, and information on the format and
specifications of the modelers' tapes. Prospective system users should also
refer to the report, "Flexible Regional Emissions Data System (FREDS)
Documentation for the 1985 NAPAP Emissions Inventory,"^ for a more thorough
treatment of FREDS system operation.
FREDS SYSTEM OVERVIEW
FREDS is a modular software system designed to process the annual
inventory and derive an emissions database suitable for input to atmospheric
pollution simulation models, such as the RADM. The primary functions of FREDS
are to resolve annual emissions to typical hourly values, to apportion county-
level area source emissions to grid cells, and to perform speciation of
selected pollutants into user-defined species classes.
Most of the FREDS modules are written in the SAS language. SAS was
chosen due to its flexible file structure, its powerful "macro" commands, and
its diverse statistical and graphics capabilities which facilitate data
analysis and quality control. Fortran is employed in some segments of FREDS
which require extensive arithmetic computations to minimize processing time
and cost. The complete system is currently installed on the IBM 3090 at the
EPA's National Computer Center in Research Triangle Park, NC.
A simplified diagram of FREDS processing, showing the interrelationship
of the major system components, is shown in Figure 9-1. A brief description
of each module is provided in the following pages.
Hydrocarbon Preprocessor
The FREDS Hydrocarbon Preprocessor (HCPREP) is run on the preliminary
annual data to ensure a consistent basis for estimating THC and non-methane
VOC emissions. HCPREP can interconvert VOC and THC, deriving one given the
other, and can also compensate for the missing mass of aldehydes in emissions
estimates calculated using factors based on flame ionization detection.
Hydrocarbon adjustment for a source is determined using the weight percentages
of formaldehyde and methane from the source's speciation profile. The
decision of whether or not to perform an adjustment depends on the settings of
aldehyde and methane augmentation flags determined for each point and area
source category (SCC). The original NEDS hydrocarbon emission estimates for
each source are retained for later reference. The annual inventory that
results from the execution of the Hydrocarbon Preprocessor is considered the
1985 NAPAP Emissions Inventory Version 2.
9-2
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1985 NEDS Annuc: Point Sourca
Emissions
1985 NEDS Annual Area Sourca
Emissions
• Hydrocarbon
Adjustment Factors"
Hydrocarbon Preprocessor
(HCPREP)
1985 NAPAP Annual
Emissions Inventory
Model Data
Extraction
Module (MDEM)
• Spatial Allocation
Factors
Spatial
Allocation
Module (SAM)
• Temporal Allocation
Factors
Temporal
Allocation
Module (TAM)
!
1
• Speciation Factors
for TSP. NOX.
Hydrocarbons
Speciation
Module
(SM)
1
Module Input
Preprocessor
(MIP)
1985 NAPAP Modelers'
Emissions Inventory
Quality Control
Module
(QCM)
Diagnostic / QC
Report. FREDS
Intermediate
Res
Figure 9-1. Simplified Diagram of FREDS Processing
9-3
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Model Data Extraction Module
The Model Data Extraction Module (MDEM) functions to reduce the volume of
emissions data to be processed and to create industry-specific emissions data
subfiles for subsequent processing. MDEM reduces the annual emissions file
size by creating condensed point and area source records at the source
category level, extracting only the data needed to identify the emissions
source and characterize its emissions.
For point sources, the following data are retained:
• Identification data (State, county, plant, point, AQCR,
SCC and SIC codes)
• Location data (latitude, longitude and UTM data)
• Operating schedule (seasonal throughput percentages;
hours/day, days/week and weeks/year of operation)
• Stack parameters (temperature, height, diameter, flow
rate, exit velocity, and plume height)
• Pollutant data (SAROAD codes, emissions, estimation
methods, control equipment codes and control efficiencies)
MDEM also removes all process (SCC) level records with zero emissions
remaining in the file (point level data with no emissions are removed prior to
FREDS). MDEM must be executed prior to the allocation and speciation modules
to ensure compatibility throughout the remainder of FREDS.
Speciation Module
The Speciation Module (SM) will accept an output file from any of the
FREDS modules and merge the emissions data with speciation factors from the
Pollutant Splits Program (PSPLIT) and/or fractions from a user-supplied
Speciation Factor File (SFF). PSPLIT is a Fortran program which disaggregates
total VOC into approximately 650 chemical species according to source category
specific profiles, and then reaggregates the species into classes based on the
requirements of the model chemistry. The PSPLIT output file contains mole
fractions for hydrocarbon species and percentage splits for NOX, while the SFF
file contains fractions for additional pollutants not resolved by PSPLIT
(e.g., alkaline particulates). Hydrocarbons may be apportioned into as many
as 32 species or groups of species, and as many as 20 additional species can
be accommodated through the SFF. Speciation is accomplished by merging each
emissions record with a speciation profile specific to each point or area
source category.
9-4
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Spatial Allocation Module
The Spatial Allocation Module (SAM) allocates emissions data into grid
cells measuring 1/6 degree latitude by 1/4 degree longitude. The NAPAP grid
system is comprised of 63,000 such grid cells (210 rows x 300 columns)
extending from 50° to 125° west longitude and 25° to 60° north latitude. For
area sources, emissions are merged with spatial allocation factors to
disaggregate county level emissions to individual grid cells. The spatial
allocation factors are based on Landsat and census data and take the form of
fractional multipliers used to apportion county total emissions into specific
grid cells. Point source emissions are assigned to grid cells based on
latitude and longitude or UTM coordinates.
Temporal Allocation Module
Point and area source emissions data are temporally resolved by the
Temporal Allocation Module (TAM). This module merges emissions with seasonal,
daily, and hourly temporal fractions from the Temporal Allocation Factor File.
Allocation factors are provided for up to 12 types of days (a weekday,
Saturday, and Sunday for each season). SCC specific allocation factors exist
for only a subset of point source categories. If an allocation factor match
cannot be made for a particular point source SCC, TAM will generate a default
pattern based on operating data found in the point source record. As an
exception, seasonal factors for point sources are primarily derived frcm
point-specific operating data, these being preferred over more general SCC
specific factors from the temporal factor file. For point source records with
inadequate operating data, or for area source records with no allocation
factor match, a uniform temporal emissions distribution is assigned. A time
zone offset is applied to each emissions record by merging the data by State
and county with records in a separate time zone file. Finally, TAM adjusts
all temporal data to GMT.
Model Input Preprocessor
The Model Input Preprocessor (MIP) accepts fully resolved, area source
files or point source files which include the appropriate spatial, temporal,
and species factors. MIP concatenates input files if necessary and performs
the appropriate sorts to yield a complete point or area source modelers file
in SAS format. These SAS formatted modelers' tapes are then converted to
standard ASCII or EBCDIC format as an additional job step.
Quality Control Module
In addition to the diagnostic checks performed within each of the FREDS
modules, data sets at any stage of FREDS processing may be examined using the
Quality Control Module (QCM). QCM accepts any SAS formatted emissions file
and compares emissions totals with values calculated from an earlier stage of
FREDS to ensure that pollutant data are not unexpectedly altered during the
apportioning process. QCM checks national and state level sums for ten major
pollutants, as well as national emissions totals for up to 20 user selected
source category codes. Temporal allocation factors are also checked, when
present, to ensure that they sum to unity within a user specified tolerance.
9-5
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FREDS ENHANCEMENTS FOR THE 1985 NAPAP EMISSIONS INVENTORY
Version 4 of the 1980 NAPAP inventory was the first to be converted into
a format suitable for modeling applications. Processing was accomplished
using an already existing system, the Regional Model Data Handling System
(RMDHS) . RMDHS was originally developed for the Northeast Corridor Regional
Modeling Project (NECRMP) emissions inventory and was subsequently modified to
accommodate preliminary NAPAP requirements. RMDHS was judged unsuitable for
use in resolving the final version (5.0) of the 1980 inventory, primarily
because of limitations on the number of pollutants which could be processed.
Instead, the FREDS was developed2 and used to create the final 1980 NAPAP
modeling inventories (Versions 5.2 and 5.3).
Following the completion of the 1980 inventory development effort,
necessary and desirable refinements to FREDS were identified for processing
the 1985 NAPAP Modelers' Emissions Inventory Version 2. Recommendations for
improvements were based on anticipated changes to the 1985 annual and resolved
inventories and those which, while not essential in terms of basic
functioning, would provide tangible operational benefits (ease of data
handling, reduction in processing time, etc.). This analysis formed the basis
for the FREDS enhancement effort. As the annual inventory file contents and
modeling inventory needs became better defined, additional modifications were
implemented. In addition, FREDS was subjected to several system tests.
Results of these tests revealed additional minor problems, which were
subsequently corrected to yield a modified FREDS system capable of handling
the requirements of the 1985 NAPAP inventories.
New FREDS Modules
»
One of the major enhancements to FREDS for the 1985 modelers.1 inventory
development effort was the design, development, and testing of two new
modules, the Hydrocarbon Preprocessor (HCPREP) and the Quality Control Module
(QCM) . HCPREP is intended to ensure a consistent basis for comparison of
hydrocarbon emissions data, while QCM serves as a consolidated error checking
routine capable of inspecting emissions totals at every stage of FREDS
execution. These modules perform a vital function in achieving and
maintaining the high level data quality essential for modeling applications.
1985 NAPAP Annual File Structure and Contents
Changes to the structure and contents of the point and area source NAPAP
annual files necessitated modifications affecting all the FREDS modules. Use
of the Emissions Inventory System (EIS) as an annual data handling system was
discontinued and, as a result, several variables referencing EIS were renamed;
other EIS specific variables were removed entirely.
Area source data for the 1980 inventory were included in a single file,
which contained both the county level header information and emissions data at
the source category level. Although convenient, repetition of the
9-6
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county level information on each SCC level record increased storage
requirements unnecessarily. For the 1985 inventory development effort, the
area source data have been split into two linked files: a file containing the
county level data and an SCC level file containing the annual emissions
totals.
1985 NAPAP Modelers* Emissions Inventory Version 2 Requirements
In addition to annual emissions file changes, modeling requirements also
evolved, requiring code changes in FREDS. In particular, hydrocarbon and
particulate speciation factors and class assignments were significantly
changed for the 1985 inventory. The number of hydrocarbon classes increased
from 28 to 32, and the individual species makeup of these classes changed
considerably. The number of discrete hydrocarbon species on which the classes
are based also increased from approximately 150 to over 500. The number of
TSP classes increased fourfold due to resolution of the data by both chemical
species and particle size fraction. The total number of speciated components
required for the 1985 NAPAP Modelers' Emissions Inventory Version 2 is 49, as
compared to 34 for the 1980 inventory. The 49 speciated components developed
by FREDS are in addition to the 10 species represented in the annual emissions
database.
Temporal and spatial allocation factor treatment was also reviewed and
improved to provide greater precision for resolved emission estimates. For
example, the Temporal Factor File was converted from EBCDIC to SAS 8-byte
format to permit numbers to be stored with greater precision and sum more
precisely to unity within the prescribed algorithms.
- The increased size of the new modeling inventories (resulting from the .
increase in speciated components) and the hardware difficulties inherent in
processing large files led to the decision to process the area source modeling
inventory in two segments, with one file containing THC, VOC, NOX, NO, N02 and
the 32 hydrocarbon classes and the other containing the remaining pollutants,
S02, SO^, CO, NH3, HC1, HF, TSP and the 15 particulate classes. The file
split reduces the processing burden posed by the large file and also allows
alternate runs to proceed independently (e.g., differing S02 or VOC/NOX
control strategies or differing speciation methodologies) without rerunning
the entire inventory. Significant updates to the FREDS source code,
particularly the Model Data Extraction Module and Speciation Module, were made
to accommodate this split file.
Point-specific data on temporal operating patterns were available for
several utility sources (e.g., hourly generation data) and judged to be of
better quality than NEDS operating schedule data. The Temporal Allocation
Module was accordingly updated to allow point specific profile matches where
data were available at this level of specificity.
9-7
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Improvements to Data Processing
Data processing improvements undertaken as part of the FREDS
modifications fall into two categories: efforts to minimize hardware related
job failures, and attempts to streamline program code to reduce processing
costs.
Some of the intermediate data sets generated by FREDS are so large that
they require work space not normally available to system users. Several
dedicated disk packs were reserved for NAPAP data processing and
specifications for the use of these disk packs were incorporated into the
FREDS code.
The area source Temporal Allocation Module (TAM) and Model Input
Preprocessor (MIP) constitute the vast majority of FREDS processing time and
cost. These modules were the focus of cost reduction analyses. TAM includes
programs written in Fortran and SAS, with the Fortran portion (MTPREP)
accounting for 2/3 of the execution time. Changes to the MTPREP program code
resulted in a 50 percent reduction in CPU time and reduced the overall FREDS
execution time by approximately 30 percent. Code modifications in MIP further
decreased processing costs by eliminating unnecessary sorting routines when
only one input file is required. Finally, the removal of SCC level records
with no emissions by the Model Data Extraction Module has shortened processing
times in all of the FREDS modules and has also reduced space requirements for
NAPAP data sets throughout processing.
Changes Resulting From System Testing
•
As part of the enhancement and validation process, FREDS was subjected to
several complete system tests. The first of these exercises was a preliminary
review using data from the 1980 NAPAP inventory, as the 1985 inventory was not
yet available. A second series of tests were conducted using preliminary 1985
point and area source data. Because of the time and cost associated with area
source processing, only one area source temporal scenario was processed (a
summer weekday). An initial FREDS run was used to identify problems and
implement corrections; a followup run was then performed as a final audit of
the FREDS code. Additional minor changes to FREDS were made as a result of
the test series. In particular, diagnostic report output was augmented to
provide additional information necessary to verify successful execution.
Observation limits were placed on output file printouts to ensure that they
would be valuable analytical tools during actual operation, rather than only
during tests with small input data sets.
FREDS MODIFICATIOMS FOR CANADIAN DATA PROCESSING
Slight differences in file structures and allocation methodologies for
Canadian data required code changes in the FREDS modules to accommodate
Canadian data processing. A separate version of FREDS was created to handle
Canadian point and area source data processing. Significant differences
between this version and the version used to process the United States data
are described in the following paragraphs.
9-8
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Canadian area source hydrocarbon emission estimates were provided as THC.
The HCPREP input files were modified to prevent methane augmentation.
Canadian area source hydrocarbons were augmented, as necessary, for aldehydes
only; point source preprocessing was identical to the United States.
The TSP area source emissions were already subdivided by reactive
alkaline particulate fractions, but not by size fractions. At the request of
Environment Canada, particulate speciation methodology was changed so that
FREDS would disaggregate the alkaline species into size fractions. A new
Speciation Factor File was created, and modifications were made to SM and its
Control Options File to allow this disaggregation.
Spatial allocation for Canadian area sources occurs at the province-level
rather than the county-level. Array sizes for spatial factor processing were
therefore increased from 160 to 900 to account for the significantly greater
number of grids possible within a province (as opposed to a U.S. county).
TAM was changed to accommodate a Temporal Allocation Factor File with a
different format than that used to process U.S. emissions records. The U.S.
factors are merged with emissions records at various levels of resolution
(e.g., SCC-, State-, county-, plant-, or point-level). Canadian factors are
merged with emission records using temporal profile code numbers. Each
profile may be applied to a number of different emissions records. TAM was
modified to use the profile number as a means to link emissions records with
temporal profiles. Instead of several routines which merge emissions records
with factors at different levels of resolution, the Canadian point and area
source TAM contain a single merge based on profile number.
FREDS MODIFICATIONS FOR NATURAL SOURCE DATA .PROCESSING
Minor modifications to some of FREDS modules were required for processing
the natural source emissions data. Changes to JCL and control options files
comprised the majority of differences between natural and anthropogenic area
source emissions processing by FREDS. For Canadian natural source processing,
the FREDS code used for anthropogenic area source emissions was applied with
no modifications. However, changes were made to control file options and JCL
files to reflect differences in the data being processed. A summary of FREDS
code modifications for U.S. natural source processing follows.
MDEM Modifications
For natural sources processing, MDEM was modified to eliminate the SAS
code for the THC/TSP file split. All corresponding variables were removed
from the remainder of the program. This change was implemented for
two reasons: (1) only particulates were present in the natural source file
and therefore there was no need for a file split; and (2) the segment of the
code was not applicable to the natural source file since the alkaline
components were not coded into the variable lists.
9-9
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SM Modifications
The SM was modified to accept a SFF with a format of 8.7; the U.S.
anthropogenic area source FREDS code input the data with a format of 8.8. No
other modifications were required.
TAM Modifications
Modifications to TAM were necessary to temporally allocate natural source
emissions using the county specific temporal allocation factors for wind
erosion. This involved an additional data step and modifications throughout
the code and diagnostic report to reflect the addition of new variables and
counters. Additional changes to the control and JCL files were also required.
DATA PROCESSING RESULTS
The NAPAP Modelers' Emissions Inventory Version 1 was produced in
September 1988 by processing the 1985 U.S. NAPAP Emissions Inventory Version 1
point and area source emissions sequentially through the revised FREDS
modules. Revised U.S. point and area source annual emissions data (as
described in Section 3) were made available for use in the 1985 NAPAP
Modelers' Emission Inventory Version 2. In addition, point and area source
annual data were supplied for Canada, and preliminary estimates of particulate
emissions from U.S. and Canadian natural sources were derived. The six
resulting annual emissions data sets(were each processed through FREDS to
create the Version 2 NAPAP modelers' inventory. The results of FREDS
processing for each of these six data types are discussed in the following
pages.
U.S. Point Sources
The NAPAP point source data, adapted from the 1985 NEDS Emissions
Inventory, contain annual emission data for 72,261 observations at the process
(SCC) level, encompassing the 48 contiguous United States and the District of
Columbia. The point source data were processed through FREDS as indicated in
Table 9-1 to create the U.S. point source component of the modelers'
inventory. Note that for point sources, allocation factors are appended to
the emission records, causing the number of variables to increase while the
number of data records remains virtually constant.
Annual data were provided as input to the HCPREP for adjustment of VOC
and derivation of THC. The annual inventory, containing emissions of 10 major
pollutants, was processed through the MDEM, where 29,496 process-level records
with zero emissions were removed from the file.
SAM was executed on the MDEM output file. Seven records whose
coordinates fell slightly outside the southern boundary of the NAPAP grid
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TABLE 9-1. FREDS PROCESSING - U.S. POINT SOURCES
Processing Summary
FREDS Input Output Comments
Module Records Records
HCPREP
MDEM
SAM
TAM
SM
MIP
10A,103
10A,103
7A,607
7A,607
7A,607
7A,607
10A,103
7A,607
7A,607
7A,607
7A,607
7A,607
THC/VOC Adjusted
Records with zero emissions deleted
Location data validated; 7
in FL south of grid system
records
Temporal pattern basis:
Point-specific factors: 56
State/SCC-specific factors: 1,089
SCC-specific factors: 2,6A7
Operating schedule: 66,819
Uniform (default): 3,996
Hydrocarbon & participate
factors merged
speciation
SAS and ASCII formatted modelers
tapes generated
4
Execution Statistics
FREDS Output Output CPU Time Percent of EXCPS
Module Records Variables (minrsec) total CPU
HCPREP 104,103 177 0:56 A. A AA,725
MDEM 7A,607 86 0:25 2.0 15,A76
SAM 7A,607 86 0:06 0.0 5,A13
TAM 7A,607 396 A:28 33.8 61,017
SM 7A,607 5A5 0:31 2. A 3A,092
MIP 7A,607 5A5 7:22 56.9 98,26A
9-11
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system were identified. All of these emitters are located in the southern
portion of Monroe County, Florida. Because these sources are correctly
located, the records were retained in the inventory but assigned grid row
numbers of 0 or -1.
The next processing step for point sources was TAM. Temporal profiles
based on operating schedule were assigned to 89 percent of point source
records. Temporal factor matches were found for an additional 6 percent of
the point source data, including 58 TVA utility source records for which
point-specific hourly generation data were used. A uniform default profile
was employed for the remaining 5 percent of the emissions records.
The SM for point sources was executed following TAM. The resulting file,
containing a complete compliment of allocation factors, was passed to the MIP,
where FREDS output files were generated both in SAS and standard ASCII format.
Output files from each of the point source modules were processed through
the Quality Control Module to identify emissions discrepancies. A relative
tolerance limit of 1 x 10 of the baseline input emissions was selected; this
number is small enough to ensure that all errors are detected, yet of
sufficient magnitude to prevent the flagging of records whose emissions differ
within normal machine precision. With the exception of the expected changes
to VOC and THC totals in the HCPREP, all emissions totals and temporal factor
sums checked to within QCM tolerance.
A summary of statistics relating to point source FREDS processing is also
presented in Table 9-1. The table includes information on file sizes,
processing times, "excps" (input/output operations) and hardware work, space
requirements. The Model Input Preprocessor accounts fgr over half of the
point source FREDS CPU time and is the'most input/output intensive of the
modules, primarily due to the SAS-to-ASCII conversion step at the conclusion
of the program.
Canada Point Sources
Point source data were supplied by Environment Canada and converted to
the same format as the U.S. point source NEDS data, as described in Section 7.
A summary of Canadian point source processing is presented in Table 9-2. The
execution of FREDS on Canadian point sources followed the same module sequence
as the U.S. point source runs. The file input to the Hydrocarbon Preprocessor
contained 3,245 SCC level observations from 10 Canadian provinces; therefore,
execution times for Canadian modules are significantly shorter than the
corresponding U.S. runs.
The Canadian data were processed successfully through the HCPREP and
MDEM. SAM detected 73 observations outside of the NAPAP study area,
corresponding to points in British Columbia with longitudes greater than
125° west. In the 1980 NAPAP Emissions Inventory, these sources were dropped,
leading to differences in annual and modelers' inventory emissions totals. As
in the case of sources in southern Florida, it was decided to retain the 73
observations in the file, since their locations were correct. Column numbers
assigned to these emitters range from 0 to -33.
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TABLE 9-2. FREDS PROCESSING - CANADA POINT SOURCES
Processing Summary
FREDS Input Output Comments
Hodule Records Records
HCPREP
MDEM
SAM
TAM
SM
MIP
3,245
3,245
3,245
3,245
3,245
3,245
3,245
3,245
3,245
3,245
3,245
3,245
THC/VOC Adjusted
Records with zero emissions deleted
Location data validated;
in BC west of grid system
Temporal pattern basis:
Canada season/day/hour
Canada day /hour code3:
Uniform (default):
Hydrocarbon & particulate
factors merged
73 records
code: 3,103
10
132
speciati on
SAS and ASCII formatted modelers
tapes generated
a _
Sources contained invalid year codes; seasonal profiles
defaulted to uniform.
Execution Statistics
FREDS
Module
HCPREP
MDEM
SAM
TAM
SM
MIP
Output
Records
3,245
3,245
3,245
3,245
3,245
3,245
Output
Variables
117
75
78
393
542
542
CPU Time
(min:sec)
0:04
0:02
0:01
0:11
0:11
0:24
Percent of
total CPU
7.5
3.8
1.9
20.8
20.8
45.3
EXCPS
1,334
770
519
1,758
5,021
4,131
9-13
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TAM, which was revised to accommodate the Canadian temporal coding, was
run on the SAM output file. Approximately 130 sources contained temporal
codes of zero and were assigned uniform temporal distributions. An additional
10 sources contained an invalid seasonal code, but daily and hourly codes for
these records were present and valid. In these cases, seasonal patterns were
set as uniform, but daily and hourly distributions were derived from the
remaining valid codes in the record.
SM and MIP were executed to complete the Canadian FREDS processing.
Quality Control Module results indicated no emissions gains or losses within
the 1 x 10 specified tolerance through the six processing modules.
U.S. Area Sources
Point and area source processing are conducted independently, and while
FREDS performs the same basic functions on both, the specific procedures
differ considerably. For point sources, allocation factors are appended to
emission records, while for area sources the emissions are multiplied by the
factors and the resolved totals are retained as output. As a result, point
source files exhibit an increase in record size; area sources show a net
increase in the number of records while record size remains fairly constant.
The large increase in number of records during SAM and TAM makes it more
efficient to execute SM as the first of the area source allocation modules.
In addition, the area source TAM must be executed once for each temporal
scenario. Because the TAM output files for the United States and Canadian
area sources are of identical format, they are combined in the MIP to reduce
the number of modelers' tapes.
A summary of U.S. area source FREDS processing, illustrating the
progression of the data through the six processing vnodules, is presented.in
Table 9-3. FREDS execution is significantly more complicated and
resource-intensive for area sources than for point sources; over 100 module
runs were required to process the U.S. area source files.
The area source FREDS input file contains data for 97 area source
categories encompassing 3,073 counties in the contiguous United States,
yielding a data set of over 260,000 observations. This file was processed
through the HCPREP, which derived total hydrocarbon emissions from VOC and
augmented selected source types for missing aldehydes. The resulting annual
NAPAP emissions file contained data for eight major pollutants (estimates of
HC1 and HF are not available for area sources). SCC level quality control
checks were performed to confirm that the augmentation proceeded correctly.
Due to the large number of species classes required for the RADM model,
the area source file was divided into two segments as described earlier. The
file split reduces the data processing burden posed by the large file and
allows runs to proceed independently for the different files. MDEM was used
to create these split files, referred to as the THC/NOX and TSP/S02 files,
respectively. MDEM also removed over 120,000 area source records containing
zero emissions from the inventory. The two MDEM output files differ in the
number of records, because different numbers of zero records were removed from
each.
9-14
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9-15
-------
The MDEM output files were processed through the SM. Hydrocarbon and NOX
speciation factors from the PSPLIT output file were merged with the THC/NOX
emissions data, while the particulate species fractions from the Speciation
Factor File were merged with the TSP/S(>2 file. As with previous modules, no
emissions discrepancies were noted in the SM output upon examination of the
QCM diagnostic reports.
SAM apportioned the county level emissions estimates into over 15,000
grid cells resulting in an output file that is over ten times the size of the
input file. The SAM output file contains one observation for each grid cell
that overlaps w.ith a county. When only a small portion of a grid falls within
a county the record corresponding to that grid cell may have zero emissions.
To minimize file size and processing cost, an optional postprocessor was
executed on the SAM output files. In addition to removing records with no
emissions, the postprocessor separated the mobile source categories (light,
medium and heavy duty gasoline vehicles, and heavy duty diesel vehicles) from
the THC/NOX and TSP/S02 files for independent processing. The resulting files
are referred to as mobile (containing area source categories 27 through 39 and
40 through A3) and nonmobile (containing all other categories).
The four spatially resolved area source SAM output files were each
processed 12 times through the Temporal Allocation Module (once for each
temporal scenario) to create a total of 48 temporally resolved files. The TAM
aggregates the emissions data by grid cell by summing all of the SCC
contributions from each county overlapping the grid. The result of TAM
execution is 24 hourly observations for each grid-level record for each of the
12 temporal scenarios. QCM review of the TAM output files revealed emission
discrepancies above standard tolerance for all eight major pollutants, which
are attributable to an intermediate TAM processing step which writes emission
data to an EBCDIC file using four significant figures. Upon subsequent
reconversion to SAS, rounding errors occurred beyond four significant figures.
While these differences exceed QCM tolerance, they do not significantly affect
the modelers' tape emissions because the tapes are written using no more than
four significant figures.
Finally, the 48 TAM output files were processed through the Model Input
Preprocessor, where they were each combined with the respective Canadian TAM
output files to create SAS-formatted FREDS output as well as ASCII-formatted
modelers' tapes.
Table 9-3 contains FREDS execution statistics for U.S. area source
modelers' tape processing. Clearly, TAM and MIP execution represent the most
intensive data processing demands; TAM alone accounts for over 60 percent of
FREDS CPU time requirements. Because of differences in the level of
aggregation of the data after each FREDS module, the number of records do not
sum to the total number of module input records.
Canada Area Sources
The Canadian area source input file contained data at the province level
for 129 source categories, yielding a data set of 1,019 observations. These
data were input to the HCPREP. Unlike U.S. data, Canadian area source
hydrocarbon estimates were expressed as THC (i.e., including methane).
9-16
-------
Methane augmentation was therefore bypassed in HCPREP. The resulting file was
processed through the MDEM to separate THC/NOX and TSP/S02 data. While file
size is not a critical consideration for Canada, the file split was performed
to maintain uniformity with the U.S. area source file.
Unlike U.S. area sources, in which TSP is disaggregated by both species
and size class, Canadian data contain estimates of alkaline species emissions
which need only to be broken down by size fraction. A modified SM was used to
process MDEM output and create speciated totals consistent with the other
NAPAP data. Area source spatial allocation was then performed using the
allocation files and spatial surrogates developed for this purpose.
The gridding of province level data creates SAM output files which are
larger than the input files by a factor of over 400. The SAM postprocessor
was run to remove grid level observations with no emissions, and separate
mobile sources (categories 21000 through 21400) from the remainder of the
files. This resulted in four Canadian area source files, each of which was
processed 12 times through TAM to create 48 fully resolved files. Some area
source emissions in the British Columbia that were west of the NAPAP domain
were retained with negative column numbers. There were also a small number of
grids from Quebec that are above the 60°N NAPAP boundary. These emissions
were dropped for consistency with the remaining inventory.
The Canadian files were combined at the grid level with their U.S.
analogs in a series of 48 MIP runs. The complete area source modeling
inventory output, spanning 48 files, contains in excess of 26 million
observations, a 100-fold increase over the FREDS input annual files.
A summary of Canadian area source FREDS execution is shown in Table 9~4.
As with all anthropogenic FREDS runs, the Quality Control Module was used to
verify input and output emissions data. A loss of precision beyond the fourth
significant figure was noted upon analysis of the TAM output file, as in the
U.S. area source file. Again, no significant error is expected to occur in
the modelers' tapes as a result of this minor discrepancy.
U.S. Natural Sources
U.S. natural source FREDS processing did not require any hydrocarbon
adjustments, and therefore, the first module executed was MDEM. For U.S.
natural sources, 9,216 county level emissions records for three SCCs were
input to MDEM. The MDEM output file contained 5,017 records; 4,199 records
contained zero emissions and were therefore dropped by MDEM. The FREDS
processing statistics for U.S. natural sources are summarized in Table 9-5.
This table shows that spatial allocation causes an expansion of the emissions
file by more than an order of magnitude and temporal allocation further
increases the number of records by another order of magnitude. Quality
control checks were performed on the output from each module to determine any
emissions discrepancies due to allocation processes. These checks entailed
calculating sums and manually simulating checks performed by the QCM.
9-17
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TABLE 9-5. FREDS PROCESSING - NATURAL SOURCES
Module
MDEM
SM
SAM
TAM
MDEM
SM
SAM
TAM
Number Output
of Runs Records
(per run)
1 5,017
1 5,017
1 68,437
12 485,784
1 104
1 88
1 44,294
12 117,480
Output CPU Time Approx.
Variables per run Pet of
(per run) (minrsec) Total CPU
U.S.
8 0:04 *
25 0:05 *
27 0:43 0.3
23 6:13 30.2
Canada
14 0:02 *
25 0:03 *
27 3:20 1.3
23 2:15 10.9 •
EXCPS
(per run)
454
1,136
3,264
64,435
425
865
17,497
21,975
M1P
12
U.S. & Canada Combined
601,248 23 11:46
57.2
47,561
* - These runs combine to contribute approximately 0.12 of total CPU time
for natural source FREDS execution.
9-19
-------
Canadian Natural Sources
Canadian natural source FREDS processing did not require the execution of
HCPREP. One hundred and six province level emissions for nine SCCs were input
to MDEM; 104 records were output for subsequent processing (two records
contained zero emissions values). In the SM, 16 additional records located in
the Yukon and Northwest territories were eliminated when no matches were found
in the Timezone File. This represents an emissions loss of less than one
percent for all natural particulates. These areas are not considered part of
the NAPAP domain and so the records for these two territories were not
processed by FREDS. Spatial allocation increased the size of the natural
source emissions file by a factor of almost 400; temporal allocation further
increased the number of records threefold. Processing statistics for Canadian
FREDS processing are summarized in Table 9-5. Data for the MIP execution to
combine the U.S. and Canadian natural source data are also provided in this
table.
1985 NAPAP VERSION 2 INVENTORY MODELERS1 TAPES
The final step of the FREDS MIP converts the completed modeling
inventories to standard ASCII format and writes the data onto magnetic tapes
for release to the user community. The complete 1985 NAPAP Modelers'
Emissions Inventory Version 2 consists of 62 individual files spanning 87
physical tapes. The U.S. and Canadian annual point, area and natural source
data sets contribute an additional 6 tapes to make a total of 93 tapes that
contain the entire annual and modelers' inventories. As shown in Table 9-6,
the tapes,include U.S. anthropogenic point and area source emissions, Canadian
anthropogenic point and area source emissions, and U.S. and Canadian natural
source particulate emissions. Data are 'normally produced on unlabeled, 9-
track, 6250 bpi tapes written in standard ASCII format. However, SAS
formatted files are also produced as part of FREDS processing, 'and EBCDIC
formatted files can easily be produced.
File Formats and Contents
A complete list of the emissions data products that make up the annual
data and modelers' tapes of the 1985 NAPAP Emissions Inventory Version 2 is
presented in Table 9-7. A key to the individual files that can be used to
identify products to be ordered is also included in Table 9-7. Table 9-7 also
includes the number of records associated with each of the files and file
emissions summaries for S02, NOX and TSP. File format tables are also listed
in Tables 9-8 through 9-18. The appropriate file format table for each file
is listed in Table 9-7.
Point Sources—
The point source files for the United States and Canada are similarly
organized and contain one record, for each SCC level point source observation.
The data records contain general identifying information, stack parameters,
and emissions of the ten major annual pollutants, as well as a set of 288
hourly temporal factors and 49 speciation factors. This is all the data
required to derive gridded, hourly emissions values of the 59 pollutant
species and subspecies for any of 12 temporal scenarios.
9-20
-------
TABLE 9-6. MAPAP VERSION 2 MODELERS' TAPES
Source Type
Point Sources, Annual U.S.
Point Sources, Annual Canada
Area Sources, Annual U.S.
Area Sources, Annual Canada
Natural Sources, Annual U.S.
Natural Sources, Annual Canada
Point Sources, Modelers' U.S.
Point Sources, Modelers' Canada
Number of
Data Files
1
1
1
1
1
1
1
1
Number of
Physical Tapes
1
1
1
1
1
1
2
1
Area Sources, Modelers'
U.S./Canada Combined:
THC/NOX, Mobile3 12 24
THC/NOX, Non-mobile3 12 24
TSP/S02, Mobile3 12 12
TSP/S02, Non-mobile3 12 12
Natural Sources,
U.S./Canada Combined3 12_ 12
Totals 68 93
a - One file exists for each of the 12 NAPAP temporal scenarios
(see Section 6).
"THC/NOX" files contain NOX, VOC, THC, and N0x/hydrocarbon species;
"TSP/S02" files contain S02, SO^, CO, NH3, TSP and particulate species.
"Mobile" source files contain U.S. area source categories 27 through 39
and 40 through 43, and Canadian area source categories 21000 through
21400. All other source categories are stored in the "nonmobile" files,
9-21
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9-24
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9-25
-------
TABLE 9-8. U.S. POINT SOURCE ANNUAL INVENTORY FORMAT
Record
First3
1
3
7
10
14
18
20
21
33
73
127
129
133
135
140
146
154
163
165
167
169
171
173
174
176
181
185
189
193
197
204
213
217
221
275
283
290
position
Last*
2
6
9
13
17
19
20
32
72
126
128
132
134
139
145
153
162
164
166
168
170
172
173
175
180
184
188
192
196
203
212
216
220
274
282
289
297
Column
width
2
4
3
4
4
2
1
12
40
54
2
4
2
5.1
6.1
8.4
9.4
2
2
2
2
2
1
2
5
4.1
4
4.1
4
7
9.2
4
4
54
8
7
8.3
Format
Z
Z
Z
A
I
I
A
A
A
A
A
I
I
F
F
F
F
I
I
I
I
I
I
I
I
F
I
F
I
I
F
I
A
A
Z '
I
F
Variable
name
STATE
COUNTY
AQCR
PLANT ID
CITY
UTM_ZONE
OWNER
CONTACT
NAMEADD
NEDPLCOM
POINT_ID
SIC
IPP
UTMX
UTMY
LAT
LON
WINTHRU
SPRTHRU
SUMTHRU
FALTHRU
HOURS
DAYS
WEEKS
BOILCAP
SPHEAT
STACK HT
STACK_DI
STACK_TP
FLOW
VELOCITY
PLUMEJiT
PTSCOMST
NEDPTCOM
sec
THRUPUT
MAXRATE
Description
NEDS State Code
NEDS County Code
Air Quality Control Region
Plant ID Code
City Code
UTM Zone
Ownership Type for Plant
Plant Contact
Plant Name and Address
NEDS Plant Comment
Point ID Code
Standard Industrial
Classification Code
IPP Code
UTM Easting, km
UTM Northing, km
Latitude, Degrees
Longitude, Degrees
Winter Thruput, 2
Spring Thruput, %
Sutnmer Thruput, 2
Fall Thruput, 2
Hours/Day in Operation
Days/Week in Operation
Weeks/Year in Operation
Boiler Design Capacity,
MMBTU/hr
Space Heat, 2
Stack Height, feet
Stack Diameter, feet
Stack Temperature, F
Flow Rate, cubic feet/min
Stack Gas Velocity, ft/sec
Plume Height, feet
Range of Points with a
Common Stack, AAZZ
NEDS Point Comment
Source Classification Code
Operating Rate, SCC units/yr
Maximum Design Rate,
SCC units/hour
(continued)
9-26
-------
TABLE 9-8 (continued)
Record
First*
298
302
306
311
312
313
367
369
372
375
379
380
390
397
400
403
407
408
418
425
428
431
435
436 -
446
453
456
459
463
464
474
481
484
487
491
492
502
509
512
515
519
520
530
position
Last3
301
305
310
311
312
366
363
371
374
378
379
389
396
399
402
406
407
417
424
427
430
434
435
445
452
455
458
462
463
473
480
483
486
490
491
501
508
511
514
518
519
529
536
Column
width
4.2
4.1
5
1
1
54
2
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
7
3,
3
4.1 '
1
10.5
7
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
7
Format
F
F
I
I
A
A
I
I
I
F
I
F
I
I
I
F
I
F
I
I
I •
F
I
F
I
I
I
F
I
F
I
I
I
F
I
F
I
I
'I
F
I
F
I
Variable
nameb
SCON
ASHCON
HEATCON
CONFID
SCECODE
NEDSCCOM
NUMPOLL
S02PRI
S02SEC
S02EFF
S02MET
S02EMF
S02EMISS
NOXPRI
NOXSEC
NOXEFF
NOXMET
NOXEMF
NOXEMISS
VOCPRI
•VOCSEC '
VOCEFF
VOCMET
VOCEMF
VOCEMISS
TSPPRI
TSPSEC
TSPEFF
TSPMET
TSPEMF
TSPEMISS
COPRI
COSEC
COEFF
COMET
COEMF
COEMISS
S04PRI
S04SEC
S04EFF
S04MET
S04EMF
S04EMISS
Description
Sulfur Concent, I
Ash Content, I
Heat Content, MMBTU/SCC unit
Confidentiality Code
Source Code, B, S, P, 0
NEDS SCC Comment
Number of Pollutants
S02 Primary Control Eq. Code
S02 Second. Control Eq. Code
S02 Control Efficiency
SC>2 Emis. Estimation Method
S02 Emission 'Factor
S02 Emissions, tons/yr
NOX Primary Control Eq. Code
NOX Second. Control Eq . Code
NOX Control Efficiency
NOX Emis. Estimation Method
NOX Emission Factor
NOX Emissions, tons/yr
VOC Primary Control Eq . Code
VOC Second. Control Eq . Code
VOC Control Efficiency
VOC Emis. Estimation Method
VOC Emission Factor
VOC Emissions, tons/yr
TSP Primary Control Eq . Code
TSP Second. Control Eq . Code
TSP Control Efficiency
TSP Emis. Estimation Method
TSP Emission Factor
TSP Emissions, tons/yr
CO Primary Control Eq. Code
CO Second. Control Eq . Code
CO Control Efficiency
CO Emis. Estimation Method
CO Emission Factor
CO Emissions, tons/yr
SO/j Primary Control Eq. Code
SO^ Second. Control Eq. Code
SO^ Control Efficiency
S04 Emis. Estimation Method
SO^ Emission Factor
SO^ Emissions, tons/yr
(continued)
9-27
-------
TABLE 9-8 (continued)
Record
First3
537
540
543
547
548
558
565
568
571
575
576
586
593
596
599
603
604
614
621
624
627
631
632
642
649
651
653
655
657
659
661
668
669
670
position
Last*
539
542
546
547
557
564
567
570
574
575
585
592
595
598
602
603
613
620
623
626
630
631
641
648
650
652
654
656
658
660
667
668
669
689
Column
width
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
7
3
3
4.1
1
10.5
•7
2
2
2
2
2
2
7
1
1
20
Format
I
I
F
I
F
I
I
I
F
I
F
I
I
I
F
I
F
I
I
I
F
I
F
I
I
I
I
I
I
I
I
A
I
A
Variable
nameb
HCLPRI
HCLSEC
HCLEFF
HCLMET
HCLEMF
HCLEMISS
HFPRI
HFSEC
HFEFF
HFMET
HFEMF
HFEMISS
NH3PRI
NH3SEC
NH3EFF
NH3MET
NH3EMF
NH3EMISS
THCPRI
THCSEC
'. THCEFF
THCMET
THCEMF
THCEMISS
YRPLANT
YRPOINT
YRCONTR
YREMISS
YRPROD
YRREC
ORIG_HC
UPDNOX
UPDLOC
UPDOP
Description
HC1 Primary Control Eq. Code
HC1 Second. Control Eq. Code
HC1 Control Efficiency
HC1 Emis. Estimation Method
HC1 Emission Factor
HC1 Emissions, tons/yr
HF Primary Control Eq . Code
HF Second. Control Eq. Code
HF Control Efficiency
HF Emis. Estimation Method
HF Emission Factor
HF Emissions, tons/yr
NH-j Primary Control Eq. Code
NHj Second. Control Eq. Code
NH3 Control Efficiency
NH} Erais. Estimation Method
NH^ Emission Factor
NH-j Emissions, tons/yr
THC Primary Control Eq. Code
THC Second. Control Eq. Code
THC Control Efficiency
THC Emi.s. Estimation Method
THC Emission Factor
THC Emissions, tons/yr
Year Plant Info Last Updated
Year Point Info Last Updated
Year Control Info Updated
Year Emissions Info Updated
Year Production Info Updated
Year Regulatory Info Updated
Original NEDS Hydrocarbons
NOX Emission Factor Update Code
(not used)
(not used)
aApplies only Co ASCII or EBCDIC formatted files,
Applies only to SAS formatted files.
9-28
-------
TABLE 9-9. CANADIAN POINT SOURCE ANNUAL INVENTORY FORMAT
Record
First3
1
3
7
13
17
25
34
38
42
46
53
62
66
74
81
96
100
104
109
111
120
123
126
130
131
138
141
144
148
149
156
159
162
166
167
174
177
180
184
185
position
Last3
2
6
12
16
24
33
37
41
45
52
61
65
73
80
95
99
103
108
110
119
122
125
129
130
137
140
143
147
148
155
158
161
165
166
173
176
179
183
184
191
Column
width
2
4
6
4
8.4
9.4
4
4.1
4
7
9.2
4
8
7
15
4.2
4.1
5
2
9
3
3
4.1
1
7
3
3
4.1
1
7
3
3
4.1
1
7
3
3
4.1
1
7
Format
Z
A
A
I
F
F
I
F
I
I
F
I
Z
I
A
F
F
I
I
A
I
I
F
I
I
I
I
F
I
I
I
I
F
I
I
.1
I
F
I
I
Variable
name
STATE
PLANT_ID
POINT~ID
SIC
LAT
LON
STACK HT
STACK_DI
STACK TP
FLOW
VELOCITY
PLUME HT
sec
THRUPUT
THRUPUTU
SCON
ASHCON
HEATCON
NUMPOLL
YWD
S02PRI
S02SEC
S02EFF
S02MET
S02EMISS
NOXPRI
NOXSEC
NOXEFF
NOXMET
NOXEMISS
VOCPRI
VOCSEC
VOCEFF
VOCMET
VOCEMISS
TSPPRI
TSPSEC
TSPEFF
TSPMET
TSPEMISS
Description
NEDS Province Code
Plane ID Code
Point ID Code
Standard Industrial
Classification Code
Latitude, degrees
Longitude, degrees
Stack Height, feec
Stack. Diameter, feet
Stack Temperature, F
Flow Rate, cubic feet/min
Stack Gas Velocity, feet/sec
Plume Height, feet
Source Classification Code
Operating Race, SCC units/yr
Thruput units
Sulfur Content, %
Ash Content, "L
Heat Content, MMBTU/SCC unit
Number of Pollutancs
Canadian Temporal Profile
Code
30^ Primary Control Eq. Code
S02 Second. Control Eq. Code
S02 Control Efficiency
S02 Emiss. Estimation Method
S02 Emissions, tons/yr
NOX Primary Control Eq . Code
NOX Second. Control Eq. Code
NOX Control Efficiency
NOX Emiss. Estimation Method
NOX Emissions, tons/yr
VOC Primary Control Eq. Code
VOC Second. Control Eq. Code
VOC Control Efficiency
VOC Emiss. Estimation Method
VOC Emissions, tons/yr
TSP Primary Control Eq. Code
TSP Second. Control Eq. Code
TSP Control Efficiency
TSP Emiss. Estimation Method
TSP Emissions, tons/yr
(continued)
9-29
-------
TABLE 9-9 (continued)
Record
First3
192
195
198
202
203
210
213
216
220
221
228
231
234
238
239
246
249
252
256
257
264
267
270
274
275
282
285
288
292
293
300
position
Lasta
194
197
201
202
209
212
215
219
220
227
230
233
237
238
245
248
251
255
256
263
266
269
273
274.
281
284
287
291
292
299
306
Column
width
3
3
4.1
1
^
7
3
3
4.1
1
7
3
3
4.1
1
7
3
3
4.1
1
7
3
3
4.1
1
7
3
3
4.1
1
7
7
Format
I
I
F
I
I
• I
I
F
I
I
I
I
F
I
I
I
I
F
I
I
I
I
F
I
I
I
I
F
I
I
I
Variable
name
COPRI
COSEC
COEFF
COMET
COEMISS
S04PRI
S04SEC
S04EFF
S04MET
S04EMISS
HCLPRI
HCLSEC
HCLEFF
HCLMET
HCLEMISS
HFPRI
HFSEC
HFEFF
HFMET
HFEMISS
NH3PRI
NH3SEC
NH3EFF
NH3MET
NH3EMISS
THCPRI
THCSEC
THCEFF
THCMET
THCEMISS
ORIG_HC
Description
CO Primary Control Eq. Code
CO Second. Control Eq. Code
CO Control Efficiency
CO Emiss. Estimation Method
CO Emissions, tons/yr
304 Primary Control Eq. Code
304 Second. Control Eq. Code
304 Control Efficiency
304 Emiss. Estimation Method
304 Emissions, tons/yr
HCl Primary Control Eq. Code
HCl Second. Control Eq. Code
HCl Control Efficiency
HCl Emiss. Estimation Method
HCl Emissions, tons/yr
HF Primary Control Eq. Code
HF Second. Control Eq. Code
HF Control Efficiency
HF Emiss. Estimation Method
HF Emissions, tons/yr
NHj Primary Control Eq. Code
NH3 Second. Control Eq. Code
NHj Control Efficiency
NH^ Emiss. Estimation Method
NH^ Emissions, tons/yr
THC Primary Control Eq. Code
THC Second. Control Eq. Code
THC Control Efficiency
THC Emiss. Estimation Method
THC Emissions, tons/yr
Original Hydrocarbons
aApplies only to ASCII or EBCDIC formatted files.
"Applies only to SAS formatted files.
9-30
-------
TABLE 9-10. U.S. AREA SOURCE ANNUAL INVENTORY FORMAT
Record
First3
1
3
7
15
17
27
34
44
51
61
68
78
85
95
102
112
119
129
136
146
153
position
Lasta
2
6
14
16
26
33
43
50
60
67
77
84
94
101
111
118
128
135
145
152
159
Column
width
2
4
8
2
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7 ,
7
Format
Z
Z
Z
I
F
I
F
I
F
I
F
I
F
I
F
I
F
I
F
I
I
Variable
name
STATE
COUNTY
sec
NUMPOLL
S02EMF
S02EMISS
NOXEMF
NOXEMISS
VOCEMF
VOCEMISS
TSPEMF
TSPEMISS
COEMF
COEMISS
S04EMF
S04EMISS
NH3EMF
NH3EMISS
THCEMF
THCEMISS
ORIGJiC
Description
NEDS State Code
NEDS County Code
Source Classification Code
Number of Pollutants
862 Emission Factor
S02 Emissions, tons/yr
NOX Emission Factor
NOX Emissions, tons/yr
VOC Emission Factor
VOC Emissions, tons/yr
TSP Emission Factor
TSP Emissions-, tons/yr
CO Emission Factor
CO Emissions, tons/yr
SO^ Emission Factor
SO^ Emissions, tons/yr
NH^ Emission Factor
NH2 Emissions, tons/yr
THC Emission Factor
THC Emissions, tons/yr
Original Hydrocarbons
aApplies only to ASCII or EBCDIC formatted files.
"Applies only to SAS formatted files.
9-31
-------
TABLE 9-11. CANADIAN AREA SOURCE ANNUAL INVENTORY FORMAT
Record
First3
1
3
11
13
22
32
39
49
56
66
73
83
90
100
107
117
124
134
141
151
158
168
175
185
192
202
209
219
226
position
Last*
2
10
12
21
31
38
48
55
65
72
82
89
99
106
116
123
133
140
150
157 .
167
174
184
191
201
208
218
225
232
Column
width
2
8
2
9
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
10.5
7
7
Format
Z
Z
I
A
F
I
F
I
F
I
F
I
F
I
F
r
F
i
F
I
F
'
I
F
I
F
I
F
I
I
Variable
name
STATE
sec
NUMPOLL
YWD
S02EMF
S02EMISS
NOXEMF
NOXEMISS
VOCEMF
VOCEMISS
TSPEMF
TSPEMISS
COEMF
COEMISS
S04EMF
S04EMISS
NH3EMF
NH3EMISS
THCEMF
THCEMISS
ARCAEMF
ARCAEMISS
ARMGEMF
ARMCEMISS
ARKEMF
ARKEMISS
ARNAEMF
ARNAEMISS
ORIC_HC
Description
NEDS Province Code
Source Classification Code
Number of Pollutants
Canadian Temporal Profile
Code
S02 Emission Factor
SC>2 Emissions, tons/yr
NOX Emission Factor
NOX Emissions, tons/yr
VOC Emission Factor
VOC Emissions, tons/yr
TSp Emission Factor
TSP Emissions, tons/yr
CO Emission Factor
CO Emissions, tons/yr
SO^ Emission Factor
SO^ Emissions, tons/yr
NH-j Emission Factor
NH^ Emissions, tons/yr
THC Emission Factor
THC Emissions, tons/yr
Reactive Calcium Emission
Factor
Reactive Calcium
Emissions, tons/yr
Reactive Magnesium Emission
Factor
Reactive Magnesium
Emissions, tons/yr
Reactive Potassium Emission
Factor
Reactive Potassium
Emissions, tons/yr
Reactive Sodium Emission
Factor
Reactive Sodium
Emissions, tons/yr
Original Hydrocarbons
aApplies only to ASCII or EBCDIC formatted files.
bApplies only to SAS formatted files.
9-32
-------
TABLE 9-12. U.S. NATURAL SOURCE ANNUAL INVENTORY FORMAT
Record
First3
1
11
21
31
41
51
61
71
81
91
101
111
121
131
position
Last3
8
18
28
38
48
58
68
78
88
98
108
118
128
138
Column
width
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Format
I
I
I
I
I
I
I
I
I
I
I
I
I
I
Variable
name
sec
STATE
COUNTY
NUMPOLL
SAROAD1
SAROAD2
SAROAD3
SAROAD4
SAROAD5
EMISS1
EMISS2
EMISS3
EMISS4
EMISS5
Description
Source Classification Code
NEDS State Code
NEDS County Code
Number of Pollutants
SAROAD Code for Pollutant #1
SAROAD Code for Pollutant #2
SAROAD Code for Pollutant #3
SAROAD Code for Pollutant #4
SAROAD Code for Pollutant #5
Emissions for Pollutant #1,
tons/yr
Emissions for Pollutant #2,
tons/yr -
Emissions for Pollutant #3,
tons/yr
Emissions for Pollutant #4,
tons/yr
Emissions for Pollutant #5,
tons/yr
aApplies only to ASCII or EBCDIC formatted files.
Applies only to SAS formatted files.
9-33
-------
TABLE 9-13. CANADIAN NATURAL SOURCE ANNUAL INVENTORY FORMAT
Record
First*
1
11
21
31
41
51
61
71
81
91
101
111
121
131
position
Last*
8
18
28
38
48
58
68
78
88
98
108
118
128
140
Column
width
8
8
8
8
8
8
8
8
8
8
8
8
8
9
Format
I
I
I
I
I
I
I
I
I
I
I
I
I
A
Variable
nameb
sec
STATE
NUMPOLL
SAROAD1
SAROAD2
SAROAD3
SAROAD4
SAROAD5
EMI SSI
EMISS2
EMISS3
EMISS4
EMISS5
YWD
Description
Source Classification Code
NEDS Province Code
Number of Pollutants
SAROAD Code for Pollutant #1
SAROAD Code for Pollutant #2
SAROAD Code for Pollutant #3
SAROAD Code for Pollutant #4
SAROAD Code for Pollutant #5
Emissions for Pollutant #1
Emissions for Pollutant #2
Emissions for Pollutant #3
Emissions for Pollutant #4
Emissions for Pollutant #5
Can. Temporal Profile Code
aApplies only to ASCII or EBCDIC formatted files.
bApplies only to SAS formatted files.
9-34
-------
TABLE 9-14. U.S. POINT SOURCE MODELING INVENTORY FORMAT
Record
First3
1
9
11
15
19
21
24
28
36
45
49
53
54
55
62
66
70
74
7.8
87
93
95
positioi
Last3
8
10
14
18
20
23
27
35
44
48
52
53
54
61
65
69
73
77
86
92
94
i
- Column
width
8
2
4
4
2
3
4
8
9
4
4
1
1
7
4
4
. 4
4
9
6
2
5
8
2
5
6
6
6
6
6
6
6
6
6
6
Format
I
I
I
A
A
I
I
F 8.5
F 9.5
I
I
I
I
F 7.0
F 4.0
F 4.1
F 4.0
F 4.0
F 9.2
F 6.4
I
I
F 8.1
I
F 5.3
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
Variable
name
sec
STATE
COUNTY
PLANT ID
POINT ID
AQCR
SIC
LAT
LON
COL
ROW
ZONE
DAYLITE
FLOW
PLUME HT
STACK_DI
STACK HT
STACK TP
VELOCITY'
HCWOWT
NUMPOLL
SAROADl-15d
EMISS1-15
NUM_DAY
SEA1-126
DAY1-12
HFGMTlf
HFGMT2
HFGMT3
HFGMT4
HFGMT5
HFGMT6
HFGMT7
HFGMT8
HFGMT9
Description
SCC Code
AEROS State Code
AEROS County Code
Plant Identification Code
Point Identification Code
Air Quality Control Region
SIC Code
Fractional Latitude (degrees)
Fractional Longitude (degrees)
Grid Column
Grid Row
Time Zone Flag
.Daylight Savings Flagc
Exhaust Gas Flow Rate
(ft3/min)
Plume Height (ft)
Stack. Diameter (ft)
Stack Height (ft)
• Stack -Temperature (°F)
Stack Gas Velocity (ft/sec)
Aldehyde Weight Fraction
Number of Pollutants
(maximum=15)
Pollutant SAROAD Code
Pollutant Emissions (tons/yr)
Number of Temporal Factor Sets
in Record (12 for modelers'
inventory)
Seasonal Temporal Allocation
Factor
Daily Temporal Allocation
Factor
Temporal Factor for Hour 1
Temporal Factor for Hour 2
Temporal Factor for Hour 3
Temporal Factor for Hour 4
Temporal Factor for Hour 5
Temporal Factor for Hour 6
Temporal Factor for Hour 7
Temporal Factor for Hour 8
Temporal Factor for Hour 9
(continued)
9-35
-------
TABLE 9-14 (continued)
Record position
First* Last*
Col
width
Format
Variable
name6
Description
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
5
11
4
1
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.
F 6.
.4
.4
F 6.4
I
I
E 11.4
F 4.1
I
I
HFGMT10
HFGMT11
HFGMT12
HFGMT13
HFGMT14
HFGMT15
HFGMT16
HFGMT17
HFGMT18
HFGMT19
HFGMT20
HFGMT21
HFGMT22
HFGMT23
HFGMT24
NUMSP
CAT1-498
CLASS1-49
SF1-49
CONEFF1-15J
ESTMET1-15
SAFk
Temporal Factor for Hour 10
Temporal Factor for Hour 11
Temporal Factor for Hour 12
Temporal Factor for Hour 13
Temporal Factor for Hour 14
Temporal Factor for Hour 15
Temporal Factor for Hour 16
Temporal Factor for Hour 17
Temporal Factor for Hour 18
Temporal Factor for Hour 19
Temporal Factor for Hour 20
Temporal Factor for Hour 21
Temporal Factor for Hour 22
Temporal Factor for Hour 23
Temporal Factor for Hour 24
Number of Speciation Factors
Major Pollutant SAROAD of
Species
SAROAD Code of Pollutant
Species*1
Speciation Factor of Species1
Control Efficiency of Point
Pollutant (Z)
Estimated Method Used for
Point Pollutant
Spatial Allocation Fraction
(=1 for point sources)
aApplies only to ASCII or EBCDIC formatted files.
bApplies only to SAS formatted files.
C0 - Daylight Savings Time is not adhered to in this area.
1 - Daylight Savings Time is adhered to in this area.
dSAROAD and EMISS are repeated NUMPOLL (up to 15) times, once for each
pollutant.
9-36
-------
TABLE 9-14 (continued)
eSEA, DAY, and HFGMT1 to HFGMT24 are repeated 12 times in sequence, once for
each temporal scenario. The variable HFGMT ranges from HFGMT1 to HFGMT288,
where HFGMT are hourly factors expressed in GMT.
fHFGMTl is defined as 0000-0100 GMT.
8CAT, CLASS, and SF are repeated once for each species (49 times for
the 1985 NAPAP inventory).
For TSP classes, the size range code occupies the first two columns;
otherwise, the first two columns are blank.
1Speciation factors:
SF1-32 - Hydrocarbon species classes (moles/kg)
SF33-34 - NO and N02 (dimensionless weight fraction)
SF35-49 - TSP species (dimensionless weight fraction)
JCONEFF and ESTMET will be repeated NUMPOLL (up to 15) times, once
for each pollutant.
These variables included in SAS formatted output tapes only.
9-37
-------
TABLE 9-15. CANADA POINT SOURCE MODELING INVENTORY FORMAT
Record
First"
1
9
11
15
19
23
31
40
44
48
49
50
51
61
65
69
73
82
88 .
90
positioi
Last*
8
10
14
18
22
30
39
43
47
48
49
56
60
64
68
72
81
87
89
i
- Column
width
8
2
4
4
4
8
9
4
4
1
1
7
4
4
4
4
9
6
2
5
8
2
5
6
6
6
6
6
6
6
6
6
6
Format
I
I
A
A
I
F 8.5
F 9.5
I
I
I
I
F 7.0
F 4.0
F 4.1
_F 4.0
F 4.0
F 9.2
F 6.4
I
I -
F 8.1
I
F 5.3
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
Variable
nameb
sec
STATE
PLANT_ID
POINT ID
SIC
LAT
LON
COL
ROW
ZONE
DAYLITE
FLOW
PLUME_HT
STACK DI
STACK HT
STACK TP
VELOCITY
HCWOWT
NUMPOLL
SAROADl-15d
EMISS1-15
NUM_DAY
SEA1-126
DAY1-12
HFGMTlf
HFGMT2
HFGMT3
HFGMT4
HFGMT5
HFGMT6
HFGMT7
HFGMT8
HFGMT9
(continued
9-38
Description
SCC Code
Province Code
Plant Identification Code
Point Identification Code
SIC Code
Fractional Latitude (degrees)
Fractional Longitude (degrees)
Grid Column
Grid Row
Time Zone Flag
Daylight Savings Flag0
Exhaust Gas Flow Rate
(ft3/min)
Plume Height (ft)
Stack Diameter (ft)
Stack Height (ft)
Stack Temperature (°F)
Stack Gas Velocity (ft/sec)
Aldehyde Weight Fraction
Number of Pollutants
( max i mum= 15)
Pollutant SAROAD Code
Pollutant Emissions (tons/yr)
Number of Temporal Factor Sets
in Record (12 for modelers'
inventory)
Seasonal Temporal Allocation
Factor
Daily Temporal Allocation
Factor
Temporal Factor for Hour 1
Temporal Factor for Hour 2
Temporal Factor for Hour 3
Temporal Factor for Hour 4
Temporal Factor for Hour 5
Temporal Factor for Hour 6
Temporal Factor for Hour 7
Temporal Factor for Hour 8
Temporal Factor for Hour 9
)
-------
TABLE 9-15 (continued)
Record position
First*
Last*
Column
width Format
Variable
nameb
Description
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
5
11
4
1
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
F 6.4
I
I
I
E 11.4
F 4.1.
I
I
HFGMT10
HFGMT11
HFGMT12
HFGMT13
HFGMT14
HFGMT15
HFGMT16
HFGMT17
HFGMT18
HFGMT19
HFGMT20
HFGMT21
HFGMT22
HFGMT23
HFGMT24
NUMSP
CAT1-498
CLASS1-49
SF1-49
CONEFF1-15J
ESTMET1-15
SAFk
Temporal Factor for Hour 10
Temporal Factor for Hour 11
Temporal Factor for Hour 12
Temporal Factor for Hour 13
Temporal Factor for Hour 14
Temporal Factor for Hour 15
Temporal Factor for Hour 16
Temporal Factor for Hour 17
Temporal Factor for Hour 18
Temporal Factor for Hour 19
Temporal Factor for Hour 20
Temporal Factor for Hour 21
Temporal Factor for Hour 22
Temporal Factor for Hour 23
Temporal Factor for Hour 24
Number of Speciation Factors
Major Pollutant SAROAD of
Species
SAROAD Code of Pollutant
Speciesh
Speciation Fac'tor of Species1
Control Efficiency of Point
Pollutant (Z)
Estimated Method Used for
Point Pollutant
Spatial Allocation Fraction
(=1 for point sources)
aApplies only to ASCII or EBCDIC formatted files.
Applies only to SAS formatted files.
C0 - Daylight Savings Time is not adhered to in this area.
1 - Daylight Savings Time is adhered to in this area.
dSAROAD and EMISS are repeated NUMPOLL (up to 15) times, once for each
pollutant.
9-39
-------
TABLE 9-15 (continued)
eSEA, DAY, and HFGMT1 to HFGMT24 are repeated 12 times in sequence, once for
each temporal scenario. The variable HFGMT ranges from HFGMT1 to HFGMT288,
where HFGMT are hourly factors expressed in GMT.
fHFGMTl is defined as 0000-0100 GMT.
gCAT, CLASS, and SF are repeated once for each species (49 times for
the 1985 NAPAP inventory).
"For TSP classes, the size range code occupies the first two columns;
otherwise, the first two columns are blank.
1Speciation factors:
SF1-32 - Hydrocarbon species classes (moles/kg)
SF33-34 - NO and N02 (dimensionless weight fraction)
SF35-49 - TSP species (dimensionless weight fraction)
JCONEFF and ESTMET will be repeated NUMPOLL (up to 15) times, once
for each pollutant.
^These variables included in SAS formatted output tapes only.
9-40
-------
TABLE 9-16. AREA SOURCE MODELING INVENTORY FORMAT (THC/NOZ FILE)
Record position
First*
1
5
9
11
21
31
Al
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
Last*
4
8
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
Column
width*
4
4
2
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10 .
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Format
I
I
I
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10.
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
Variable
nameb
COL
ROW
GMT
TOT1
TOT2
TOT3
TOT4
TOTS
TOT6
TOT 7
TOTS
TOT9
TOT 10
TOT 11
TOT 12
TOT 13
TOT 14
TOT 15
TOT16 '
TOT 17
TOT 18
TOT 19
TOT20
TOT21
TOT22
TOT23
TOT24
TOT25
TOT26
TOT27
TOT28
TOT29
TOT30
TOT 31
TOT32
Description
Grid Column
Grid Row
Hour (Greenwich Mean Time)
NOX Emissions
VOC Emissions
THC Emissions
Methane Emissions
Ethane Emissions
Propane Emissions
Alkane (0.25-0.5 react) Emissions
Alkane (0.5-1.0 react) Emissions
Alkane (1.0-2.0 react) Emissions
Alkane (>2.0 react) Emissions
Alkane/Aromatic Mix Emissions
Ethene Emissions
Propene Emissions
Primary Alkene Emissions
Internal Alkene Emissions
Primary/Internal Mix Alkene
Emissions
Benzene/Halobenzene Emissions
Aromatic (<2.0 react) Emissions
Aromatic (>2.0 react) Emissions
Phenol/Cresols Emissions
Styrenes Emissions
Formaldehyde Emissions
Higher Aldehydes Emissions
Acetone Emissions
Higher Ketones Emissions
Organic Acids Emissions
Acetylene Emissions
Haloalkenes Emissions
Unreactive Hydrocarbon Emissions
Other Hydrocarbon (<0.25 react)
Emissions
Other Hydrocarbon (0.25-0.5
react) Emissions
Other Hydrocarbon (0.5-1.0
react) Emissions
(continued)
9-41
-------
TABLE 9-16 (continued)
Record position
First*
331
341
351
361
371
Last*
340
350
360
370
380
Column
width*
10
10
10
10
10
Format
E10
£10
E10
E10
E10
Variable
nameb
TOT33
TOT34
TOT35
TOT36
TOT37
Description
Other Hydrocarbon (>1.0 react)
Emissions
Unidentified Hydrocarbon Emissions
Unassigned Hydrocarbon Emissions
NO Emissions
N02 Emissions
aApplies only to ASCII or EBCDIC formatted files.
^Applies only to SAS formatted files.
Note:
Emissions of hydrocarbon species classes (TOT4 through TOT35) are
expressed as gram moles per hour; all other pollutants are
expressed as tons per hour.
9-42
-------
TABLE 9-17. AREA SOURCE MODELING INVENTORY FORMAT (TSP/SO2 FILE)
Record position
First3 Lasta
Column
width8
Variable
Format n«"">')
Description
1441 COL Grid Column
5 8 4 I ROW Grid Row
9 10 2 I GMT Hour (Greenwich Mean Time)
11 20 10 E10 TOT1 S02 Emissions0
21 30 10 E10 TOT2 SOA Emissions
31 40 10 E10 TOT3 TSP Emissions
41 50 10 E10 TOT4 CO Emissions
51 60 10 E10 TOTS NH3 Emissions
61 70 10 E10 TOT6 Calcium Emissions, 0-2.5 urn
71 80 10 E10 TOT7 Calcium Emissions, 2.5-10 urn
81 90 10 E10 TOT8 Calcium Emissions, Total
91 100 10 E10 TOT9 Magnesium Emissions, 0-2.5 urn
101 110 10 E10 TOT10 Magnesium Emissions, 2.5-10 urn
111 120 10 E10 TOT11 Magnesium Emissions, Total
121 130 10 E10 TOT12 Potassium Emissions, 0-2.5 urn
131 140 10 E10 TOT13 Potassium Emissions, 2.5-10 urn
141 . 150 10 £10 TOT14 Potassium Emissions, Total
151 160 10 E10 TOT15 Sodium Emissions, 0-2.5 urn
161 170 10 E10 TOT16 Sodium Emissions, 2.5-10 ^um
171 180 10 E10 TOT17 Sodium Emissions, Total
181 190 10 E10 TOT18 Total Particulate Emissions,
0-2.5 urn
191 200 10 E10 TOT19 Total Particulate Emissions,
2.5-6 urn
201 210 10 £10 TOT20 Total Particulate Emissions,
6-10 urn
aApplies only to ASCII or EBCDIC formatted files.
bApplies only to SAS formatted files.
'-'Note: Emissions of all pollutants in the TSP/S02 file are
expressed as tons per hour.
9-43
-------
TABLE 9-18. NATURAL SOURCE MODELING INVENTORY FORMAT
Record position
First*
1
5
9
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
Last3
4
8
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150 .
160
170
180
190
200
210
ituiuinii
width3
4
4
2
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Format
I
I
I
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
E10
' E10 '
E10
E10
E10
E10
E10
variant, e
name6
COL
ROW
GMT
TOT1
TOT2
TOT3
TOT4
TOTS
TOT6
TOT 7
TOTS
TOT9
TOT 10
TOT 11
TOT 12
TOT 13
TOT 14
TOT 15
TOT 16
TOT 17
TOT 18
TOT 19
TOT20
Description
Grid Column
Grid Row
Hour (Greenwich Mean Time)
TSP Emissions0
Reactive Calcium Emissions
Reactive Magnesium Emissions
Reactive Sodium Emissions
Reactive Potassium Emissions
Calcium Emissions, 0-2.5 urn
Calcium Emissions, 2.5-10 urn
Calcium Emissions, Total
Magnesium Emissions, 0-2.5 um
Magnesium Emissions, 2.5-10 um
Magnesium Emissions, Total
Potassium Emissions, 0-2.5 um
Potassium Emissions, 2.5-10 um
Potassium Emissions, Total
Sodium Emissions, 0-2.5 um
Sodium Emissions, 2.5-10 um
Sodium Emissions, Total
Total Particulate Emissions,
0-2.5 um
Total Particulate Emissions,
2.5-6 um
Total Particulate Emissions,
6-10 um
aApplies only to ASCII or EBCDIC formatted files.
Applies only to SAS formatted files.
cNote: Emissions of all pollutants in the natural source file are
expressed as tons per hour.
9-44
-------
The information provided in the format tables can be used to read point
source modelers' tapes in SAS, EBCDIC, or ASCII format. However, not all
information is applicable to all formats. For example, the starting and
ending positions of data elements are essential for reading EBCDIC and ASCII
tapes, but are not needed to read SAS files, which are generally referenced by
variable name.
Area Sources—
Due to the large volume of area source data generated during FREDS
processing, the annual inventory data are subdivided three times during the
resolution process: once by pollutant; once by source category; and finally by
temporal scenario as represented in Table 9-7. U.S. and Canadian area source
data are then combined during MIP to reduce the number of independent output
files. As a result, a complete set of area source data for a single temporal
scenario requires four data sets, and the entire area source modeling
inventory (12 scenarios) consists of A8 data sets.
Each area source grid level record contains emissions values for a single
hour for the selected temporal scenario. The THC/NOX file contains 37
pollutants; the TSP/S02 file contains 20 pollutants. Since HC1 and HF
emissions are not reported in the area source inventory, the total number of
pollutants (57) is two fewer than in the point source modelers' tapes.
Natural Sources—
The natural particulate source data is formatted similarly to the
anthropogenic area sources. There is one file for each of the 12 temporal
scenarios, containing hourly emissions at the grid cell level. Since TSP is
the only major pollutant, there is no file division by pollutant, nor is there
a "mobile/nonmobile" file split. A total of 20 pollutant species and
subclasses are reported.
Point Source Resolution
Point source modelers' tape format differs considerably from other source
types. It is often convenient to express all emissions on a common basis,
which requires a method for creating gridded, hourly point source files. An
example of a SAS program to perform such a transformation is illustrated in
Figure 9-2; however, similar programs can be developed in virtually any
scientific programming language. The point source resolution program
speciates and temporally allocates data from the FREDS output file; spatial
allocation is unnecessary since all sources have already been assigned to
grids.
Speciation is accomplished by multiplying the appropriate major pollutant
emissions with the speciation factors appended in the SM. The speciation
factor array consists of the 32 hydrocarbon class factors (SF1 through SF32),
two NOX species factors (SF33 and SF34), and 15 particulate class factors
(SF35 through SF49). These factors are multiplied by the THC, NOX, or TSP
annual emissions as appropriate to yield speciated annual emissions estimates.
Factors are expressed in moles/kilogram for hydrocarbon species, therefore, a
kilogram-to-ton conversion factor is also applied to generate speciated
9-45
-------
--PROGRAM RESOLVE;
*;
OPTIONS MPRINT
*'- *
»
***** THIS PROGRAM WILL PRODUCE GRIDDED, ALLOCATED EMISSIONS FOR
NAPAP POINT SOURCE FILES SUITABLE FOR A MODELERS' TAPE;
*»
***** TO EXECUTE PROVIDE THE NUMBER OF THE TEMPORAL SCENARIO TO BE
ALLOCATED(1-12) TO THE MACRO ZRSOLVE (LAST PROGRAM LINE);
*;
***** THE OUTPUT FILE CONTAINS THE GRID COLUMN AND ROW (COL,ROW),
THE GMT HOUR (GMT) AND THE FULLY RESOLVED EMISSIONS (GPOLL1-
GPOLL59) IN TONS/HOUR FOR ALL POLLUTANTS EXCEPT THE 32 THC
SPECIES WHICH HAVE UNITS OF GM-MOLES/HOUR.;
*;
***** NOTE THAT THE OUTFILE CONTAINS 24 TIMES AS MANY RECORDS
AS THE INFILE, ALTHOUGH THE RECORDS HAVE FEWER VARIABLES;
*;
ZMACRO RSOLVE(TAFNO);
v**- •
*• »
%* CALCULATE POSITION OF HOURLY TEMPORAL FRACTIONS TO BE APPLIED;
%* BASED ON SCENARIO NUMBER (TAFNO);
%*;
%LET A=%EVAL((&TAFNO-1)*24+1);
ZLET B=%EVAL(&A+23);
»*«• •
" 9
* OPEN DATASETS AND DEFINE SAS ARRAYS;
DATA OUTFILE.DATA(KEEP=COL ROW GMT GPOLL1-GPOLL59);
'SET INFILE.DATA;
ARRAY SAROAD(K) SAROAD1-SAROAD15;
ARRAY EMISS(K) EMISS1-EMISS15;
ARRAY SF SF1-SF49;
ARRAY POLL(N) POLL1-POLL59;
ARRAY GPOLL(N) GPOLL1-GPOLL59;
ARRAY HFGMT(A) HFGMT&A-HFGMT&B;
ARRAY GHOUR(A) GHOUR1-GHOUR2A;
*;
* EXTRACT POLLUTANTS AND SPECIATE;
*;
DO K=l TO NUMPOLL;
SELECT (SAROAD);
WHEN (42401) POLL1=EMISS /* S02 */;
WHEN (12403) POLL2=EMISS /* S04 */;
WHEN (11101) DO;
POLL3=EMISS /* TSP */;
DO _I_=35 TO 49;
POLL=POLL3*SF /* ALKALINE DUST SPECIES */;
Figure 9~2. Sample Point Source Resolution Program.
9-46
-------
END;
END;
WHEN (42101) POLL4=EMISS /* CO */;
WHEN (42302) POLL5=EMISS /* HCL */;
WHEN (42303) POLL6=EMISS /* HF */;
WHEN (42603) DO;
POLL7=EMISS /* NOX */;
POLL43=POLL7*SF33 /* NO */;
POLL44=POLL7*SF34 /* N02 */;
END;
WHEN (42604) POLL8=EMISS /* NH3 */;
WHEN (43104) POLL9=EMISS /* VOC */;
WHEN (43101) DO;
POLL10=EMISS /* TOT HC */;
DO _I =1 TO 32;
N=~I_-HO;
POLL=POLL10*SF*907.18474 /* SPECIES(GM-MOL PER YR) */;
END;
END;
OTHERWISE;
END;
END;
»
* ALLOCATE TEMPORALLY AND OUTPUT 1 OBSERVATION PER HOUR;
-'- •
1
DO A = 1 TO 24;
CHOUR=HFGMT*SEA*TAFNO*DAY&TAFNO;
END;
DO A=l TO 24; . .
GMT=A;
DO N=l TO 59;
GPOLL=POLL*GHOUR;
END;
OUTPUT;
END;
%MEND RSOLVE;
"' 5
* INVOKE THE MACRO;
*;
%RSOLVE(7);
Figure 9-2. (continued)
9-47
-------
emissions estimates in gram moles of species class. NOX and particulate
factors are dimensionless mass fractions which, when multiplied by the major
pollutant, yield emission estimates in tons.
Annual emissions are temporally allocated using the seasonal, daily and
hourly factors present in each point source record. There are 12 seasonal, 12
daily, and 288 (12 sets of 24) hourly factors. The example in Figure 9-2 will
produce hourly emissions for scenario 7 (a summer weekday). The seventh set
of hourly factors is, therefore, selected from the array (HFGMT 145 through
168). These 24 factors are each multiplied by the appropriate seasonal and
daily factors (SEA 7 and DAY 7) to produce the temporal profile for the source
(GHOUR 1 through GHOUR 24). Each of the 59 annual pollutants is multiplied by
each value of GHOUR and written to the output file in turn, creating 24 hourly
output records for each input record.
Tape Totals
Tables 9-19 and 9-20 contain emissions information for each of the 48
area source tape data sets, which can be used to verify that a given area
source modelers' tape is being read correctly. The total emissions in tons in
each file are illustrated. Emissions of NOX, VOC, THC, NO, and N02 are
provided for THC/NOX files; S02, TSP, NH3, total calcium, and PM (6-10 urn)
totals are given for TSP/S02 files. To verify tape totals, the user should
sum the values for the indicated pollutants over all hours for all grid cells.
The resulting totals in tons should match the totals indicated in the tables.
Table 9-21 contains tape totals for five representative pollutants from
each of the 12 natural source tapes; .these can be. used to verify th'e co'rrect
reading of natural source data using the procedure described above. Since the
point source modelers' tape emissions are unresolved, totals for major
pollutants can be verified by comparison with annual totals provided in the
tables in Section 2, as well as in Appendix A, of this report.
9-48
-------
TABLE 9-19. TAPE TOTALS FOR COMBINED U.S. AND CANADIAN
AREA SOURCES, TUC/NOX FILES
MB/NM
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Scenario
1
2
3
4
5
6
7
8
9
10
11
12
1
'2
3
4
5
6
7
8
9
10
11
12
TOT1
21,116.72
18,098.44
16,031.41
22,004.99
19,894.91
17,858.08
23,270.56
20,826.03
18,841.64
22,081.41
19,947.77
17,352.10
15,325.39
11,161.87
9,215.85
15,160.86
10,911.62
9,375.86
14,639.98
10,499.87
8,848.25
14,780.22
10,780.61
9,074.16
TOT2
22,962.69
18,948.13
16,370.47
24,175.76
21,399.24
18,875.39
25,694.80
22,455.67
20,114.34
24,052.72
21,289.85
17,978.86
44,175.52
31,208.41
27,669.35
42,529.63
29,959.60
26,660.70
40,497.86
27,909.25
23,965.86
41,492.02
29,497.71
25,508.65
TOT3
25,338.45
20,876.68
18,022.09
26,686.17
23,601.23
20,807.11
28,372.83
24,773.78
22,183.95
26,547.79
23,478.27
19,809.68
52,855.98
39,211.74
35,143.53
47,990.20
34,744.88
30,918.68
43,742.25
30,467.22
25,985.77
46,468.31
33,789.38
29,259.91
TOT36
20,060.89
17,193.51
15,229.83
20,904.73
18,900.15
16,965.17
22,107.04
19,784.72
17,899.54
20,977.33
18,950.39
16,484.48
14,521.61
10,571.34
9,009.81
14,298.82
10,273.69
8,817.00
13,829.35
9,920.88
8,354.26
13,967.10
10,174.85
8,556.06
TOT37
1,055.83
904.92
801.57
1,100.25
994.74
892.90
1,163.53
1,041.30
942.08
1,104.06
997.39
867.60
803.77
590.54
506.03
862.02
637.96
558.87
810.61
578.99
493.99
813.12
605.77
518.10
9-49
-------
TABLE 9-20. TAPE TOTALS FOR COMBINED U.S. AMD CANADIAN
AREA SOURCES, TSP/S02 FILES
MB/NM
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Non-Mobile
Scenario
1
2
3
4
5
6
7
8
9
10
11
12
1
o
4.
3
4
5
6
7
8
9
10
11
12
TOT1
1,504.93
1,346.61
1,222.69
1,549.38
1,440.91
1,318.87
1,623.78
1,495.55
1,375.41
1,562.40
1,450.79
1,297.30
6,683.67
4,451.04
3,711.65
5,601.60
3,395.65
2,764.11
4,914.14
2,831.81
2,206.97
5,641.13
3,496.16
2,799.15
TOT3
10,956.34
9,170.76
7,732.71
11,535.71
10,352.92
8,918.14
12,287.22
10,812.07
9,512.48
11,515.50
10,325.79
8,458.54
13,541.75
11,710.85
11,150.00 -
13,069.34
' 10,471.60
9,897.82
9,716.51
5,600.76
5,012.11
10,722.49
8,464.55
7,869.83
TOTS
8.27
6.67
5.98
8.73
7.04
6.29
8.69
7.01
6.08
8.35
' 6.74
5.83
3,751.87
3,639.32 .
1,150.64
6,836.82
6,728.43
1,278.84
3,380.66
3,269.97
1,227.08
5,729.26
5,622.34
1,207.91
TOTS
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
17.13
14.73
14.01
33.32
30.73
30.11
14.68
11.99
11.41
21.06
18.52
17.85
TOT20
537.58
448.42
376.75
566.50
507.50
436.01
603.98
530.38
465.69
565.41
506.08
413.00
560.98
342.74
293.52
672.99
431.22
380.84
676.91
377.93
327.11
644.73
396.11
344.06
9-50
-------
TABLE 9-21. TAPE TOTALS FOR COMBINED U.S. AND CANADIAN NATURAL SOURCES
SCENARIO
Scenario 01
Scenario 02
Scenario 03
Scenario 04
Scenario 05
Scenario 06
Scenario 07
Scenario 08
Scenario 09
Scenario 10
Scenario 11
Scenario 12
TOTAL 01
(TSP)
184456.75
175308.63
164692.39
239446.03
229246.01
212705.96
265478.63
254006.33
238274.80
207294.64
197013.63
182379.48
TOTAL 06
(CAD
136,3825
134.2778
132.0196
161.6149
159.2244
155.9613
152.9899
150.3061
147.0712
141.2171
138.8246
135.8567
TOTAL 10
(MG2)
222.9759
222.0965
220.7315
275.0447
274.0504
271.3589
256.6016
255.4857
253.1667
225.4996
224.5034
222.2970
TOTAL 18
(PM1)
21347.96
18626.03
16105.13
24495.34
21460.54
18556.03
27470.74
24058.13
20850.16
23975.08
20916.68
18030.82
TOTAL 20
(PM3)
23773.85
22524.50
21296.63
29627.55
28234.82
26677.85
33591.71
32026.28
30391.26
25978.02
24575.23
23088.55
Total
2550303.27
1745.7461 2923.8119
255892.63
321786.26
NOTE: 1 Metric Ton = 1.10231707 Tons
9-51
-------
SECTION 9
REFERENCES
1. Modica, L.G., D.R. Dulleba, R.A. Walters, and J.E. Langstaff. "Flexible
Regional Emissions Data System (FREDS) Documentation for the 1985 NAPAP
Emissions Inventory." EPA Report No. EPA-600/9-89-047
(NTIS PB89-198816). May 1989.
2. Lebowitz, L.G., and A.S. Ackerman. "Flexible Regional Emissions Data
System (FREDS) Documentation for the 1980 NAPAP Emissions Inventory."
EPA-600/7-87-025a (NTIS PB88-129499). U.S. Environmental Protection
Agency, Research Triangle Park, NC. November 1987.
3. Sellars, P.M., T.E. Fitzgerald, Jr., J.M. Lennon, L.J. Maiocco, N.M.
Monzione, and D.R. Neal, Jr. "National Acid Precipitation Assessment
Program Emission Inventory Allocation Factors." EPA-600/7-85-035
(NTIS PB86-10A247). U.S. Environmental Protection Agency, Research
Triangle Park, NC. September 1985.
4. Wagner, J.K., R.A. Walters, L.J. Maiocco, and D. R. Neal, Jr.
"Development of the 1980 NAPAP Emissions Inventory." EPA-600/7-86-057a
(NTIS PB88-132121), U.S. Environmental Protection Agency, Research
Triangle Park, NC. December 1986.
9-52
-------
APPENDIX A
DETAILED EMISSIONS SUMMARIES
This appendix lists Che emissions totals for the 59 species that are
included in the 1985 NAPAP Modelers' Emissions Inventory Version 2. The tables
list emissions totals at various levels of aggregation including State and
Provincial totals, U.S. EPA Regions and SCC levels for both United States and
Canadian point and area sources. The headings of columns in each of these
tables refer to individual species as listed below:
S02
NO
CO
HC1
NH3
PM1
PM3
CA2
Kl
K3
MC2
NA1
NA3
VOC
HC1
HC2
HC4
HC6
HC8
HC10
HC12
HC1A
HC16
HC18
HC20
HC22
HC24
HC26
HC28
HC29
HC30
HC32
NO,
NO 2
S04
HF
TSP
PM2
CA1
CA3
K2
MG1
MG3
NA2
THC
sulfur dioxide
nitric oxide
carbon monoxide
hydrogen chloride gas
ammonia
particulate 0.0 - 2.5 urn
particulate 6.0 - 10.0 urn
reactive calcium 2.5 - 10.0 urn
reactive potasium 0.0 - 2.5 urn
total reactive potasium
reactive magnesium 2.5 - 10.0 urn
reactive sodium 0.0 - 2.5 urn
total reactive sodium
nonmethane volatile organic compounds
hydrocarbon species class 1 - methane
species class 2 - ethane HC3
alkanes - reactivity 0.25 - 0.50 HC5
alkanes - reactivity 1.0 - 2
alkane/aromatic mix
propene
alkenes (internal)
benzene, halobenzenes
aromatics reactivity > 2.0
styrenes
higher aldehydes
higher ketones
acetylene
unreactive hydrocarbons
other species - reactivity 0
other species - reactivity 0
other species - reactivity >
Unassigned hydrocarbons
0 HC7
HC9
HC11
HC13
HC15
HC17
HC19
HC21
HC23
HC25
HC27
25 - 0.50
5 - 1.00
1.00 HC31
total/oxides of nitrogen
nitrogen dioxide
primary particulate sulfate
hydrogen fluoride gas
total suspended particulate
particulate 2.5 - 6.0 urn
reactive calcium 0.0 - 2.5 urn
total reactive calcium
reactive potasium 2.5 ~ 10.0 urn
reactive magnesium 0.01 - 2.5 urn
total reactive magnesium
reactive sodium 2.5 - 10.0 urn
total hydrocarbon
species class 3 - propane
alkanes - reactivity 0.5 - 1.00
alkanes - reactivity > 2.0 *
ethene .
alkenes (primary)
alkenes (primary/internal mix)
aromatics reactivity < 2.0
phenols and cresols
formaldehyde
acetone
organic acids
haloalkenes
other species
- reactivity < 0.25
unidentified hydrocarbons
NOTE: the symbol urn represents micrometers diameter for particulate size
fractions
the term reactivity refers to the reaction rate constant of the
species with HO in units of 10 ppm min
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