EMISSIONS INVENTORY
FOR THE NATIONAL
PARTICULATE MATTER STUDY
Final Draft
Prepared for:
Mr. Christopher Knopes
Office of Policy, Planning and Evaluation/Office of Policy Analysis
U.S. Environmental Protection Agency
401 M. Street, SW
Washington, DC 20460
Prepared by:
E.H. Pechan & Associates, Inc.
5537 Hempstead Way
Springfield, VA 22151
and
E.H. Pechan & Associates, Inc.
2880 Sunrise Blvd., Suite 220
Rancho Cordova, CA 95742
July 1994
EPA Contract No. 68-D3005
Work Assignment No. 0-10
Pechan Report No. 94.06.001/1710
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CONTENTS
Page
TABLES AND FIGURES v
CHAPTER I
INTRODUCTION 1
A. PURPOSE AND SCOPE 1
B. OVERVIEW OF METHODOLOGY 2
C. CONTENTS OF THIS REPORT 3
CHAPTER II
EMISSION INVENTORY DEVELOPMENT 5
A. ELECTRIC UTILITY EMISSIONS 5
1. 1985-1990 Steam Electric Utility Emission Inventory 5
a. Processing Computerized Data from Form EIA-767 5
b. Emissions Algorithms 6
B. NON-UTILITY POINT SOURCES 10
1. PM-10 Emission Calculations 10
2. PM-2.5 Emission Calculations 17
3. Calculation of NH3 Emissions 17
C. FUGITIVE DUST SOURCES 17
1. Agricultural Tilling 17
2. Construction Activities 18
3. Paved Road Resuspension 19
4. Unpaved Roads 20
5. Wind Erosion 22
6. Cattle Feed Lots 24
D. OTHER AREA AND MOBILE SOURCES 24
1. Growth Indicators 24
2. Residential Wood Combustion 28
3. Highway Vehicles 29
4. Nonroad Sources 29
a. Creation of National County-Level 1990 Nonroad Emission
Estimates 30
b. Distribution of Total Nonroad Emissions to SCCs 32
5. Other Combustion 32
E. BIOGENIC EMISSIONS 35
F. METHODS FOR ASSESSING SECONDARY ORGANIC AEROSOL
FORMATION 35
1. Anthropogenic VOC Sources 36
2. Biogenic Sources 38
G. CANADA AND MEXICO EMISSIONS 38
1. 1990 Emission Estimates for Canada 40
2. 1990 Emission Estimates for Mexico 40
a. The World Bank Report 41
b. Estimating 1990 Emissions 42
Page iii
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CONTENTS (continued)
c. Determining PM-10 and PM-2.5 Emissions 42
d. Determining Secondary Organic Aerosol Emissions 42
e. Compilation of Emissions 43
H. DEVELOPMENT OF AMBIENT MODELING INVENTORY . . 43
1. Temporal Allocation 43
2. Aggregation of Emissions 43
I. QUALITY OF EMISSIONS ESTIMATES AND RECOMMENDATIONS FOR
IMPROVEMENT 44
1. Relative Quality 44
2. Recommendations for Improvement 45
CHAPTER IH
RESULTS AND DISCUSSION 47
A. 1990 U.S. EMISSIONS 47
1. 1990 PM-10 and PM-2.5 Emissions 47
2. 1990 SO2 Emissions 54
3. 1990 NO, Emissions 54
4. 1990 NH3 Emissions 65
5. 1990 VOC and SOA Emissions 65
B. 1990 CANADA EMISSIONS 75
C. 1990 MEXICO EMISSIONS 75
ABBREVIATIONS AND ACRONYMS 77
REFERENCES 79
Page iv
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TABLES AND FIGURES
Tables Page
II-1 Steam Electric Utility Unit Source Classification Code Relationships 8
II-2 BEA Industry Earnings Data Classifications 11
II-3 Summary of BEA's SA-5: National Changes In Earnings By Industry 13
II-4 PM-10 Emission Estimates 16
II-5 Area Source Growth Indicators 25
II-6 SEDS National Fuel Consumption 27
II-7 Summary of Residential Wood Combustion Emission Factors 28
II-8 Calculation of Ammonia Emission Factors for Highway Vehicles 30
II-9 Ozone Nonattainment Areas with OMS-Prepared Nonroad Emission
Estimates 31
11-10 Source Categories Used for Nonroad Emission Estimates 34
11-11 Sample Determination of a Source-Specific FAC: Asphaltic Concrete 37
11-12 Sample Source-Specific FACs 39
11-13 Source-Specific FACs by Landcover Type 40
III-l United States 1990 PM-10 and PM-2.5 Emissions by EPA Region 49
III-2 United States 1990 SO2 Emissions by EPA Region 56
III-3 United States 1990 NO, Emissions by EPA Region 61
III-4 United States 1990 NH3 Emissions by EPA Region 67
III-5 United States 1990 VOC and SOA Emissions by EPA Region 71
III-6 1990 Canada Emissions 75
III-7 1990 Mexico Emissions 76
Figures
II-l Assignment of Surrogate Nonattainment Areas . 33
III-l Map of EPA Regions 48
III-2 United States 1990 PM-10 Emissions by Source Category 51
III-3 United States 1990 PM-2.5 Emissions by Source Category 52
III-4 Summary of 1990 PM-10 Emissions by EPA Region 53
III-5 Summary of 1990 PM-2.5 Emissions by EPA Region 55
III-6 United States 1990 SO2 Emissions by Source Category 59
III-7 Summary of 1990 SO2 Emissions by EPA Region 60
III-8 United States 1990 NO, Emissions by Source Category 64
III-9 Summary of 1990 NOE Emissions by EPA Region 66
III-10 United States 1990 NH3 Emissions by Source Category 69
III-ll Summary of 1990 NH3 Emissions by EPA Region 70
III-12 United States 1990 Anthropogenic SOA Emissions by Source Category 73
III-13 Summary of 1990 Anthropogenic SOA Emissions by EPA Region 74
Page v
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Page vi
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CHAPTER I
INTRODUCTION
Prior to 1987, research on particulate matter (PM) focussed on Total Suspended
Particulate (TSP), usually defined as particulate matter sized 30 micrometers (um) and
smaller in aerodynamic diameter. After an assessment of the TSP national ambient air
quality standard (NAAQS) in 1987, it was determined that particles with aerodynamic
diameters less than 10 um (PM-10) were responsible for more serious health effects, and a
PM-10 NAAQS was promulgated. Recent epidemiological research shows that morbidity
and mortality effects occur at levels well below the current NAAQS. Other research
suggests that smaller size fractions may contribute significantly to the most serious
health effects due to the ability of the fine particulate to penetrate deeper into the lungs.
For this reason, the U.S. Environmental Protection Agency (EPA), Office of Policy,
Planning and Evaluation (OPPE), is assessing the impacts of lower ambient air standards
for PM-10 and of new standards based on particulate matter with aerodynamic size 2.5
um or less (PM-2.5).
The purpose of OPPE's "National Particulate Matter Study" is to explore alternative
methods of reducing ambient levels of, and human exposure to, particulate matter. This
is being accomplished through regional analyses designed to identify cost-effective ways of
reducing ambient concentrations. Specific tasks to be performed include the following: (1)
develop a national emission inventory of primary particulate and precursors to secondary
particulate formation for the base year of 1990; (2) perform emissions projections to the
year 2005 based on industrial and population growth and the impact of controls as
required by the 1990 Clean Air Act Amendments; (3) determine the costs of various
control scenarios, (4) conduct regional ambient modeling to predict base year (1990) and
projections year (2005) concentrations, (5) compile data available (e.g., source
apportionment, actual control strategy performance) from other studies conducted as part
of State Implementation Plan (SIP) development, visibility studies, or other studies; and,
(6) compile information from the Aerometric Information Retrieval System/Air Quality
Subsystem (AIRS/AQS) in order to examine current PM-10 concentrations at the county-
level, and provide a basis for comparing the ambient modeling results to measured data.
A. PURPOSE AND SCOPE
This report documents the development and results of the base year emission
inventory: the National Particulates Inventory.
This emissions inventory serves three purposes in the National PM study. As a
source of baseline emission estimates, it provides a complete inventory of all relevant
pollutants. Also, these base year estimates are the basis for the emission and cost
projections that will be output from the cost modeling. Finally, these emission estimates
will be used as input to the ambient modeling to develop source-receptor relationships.
Page 1
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The National Particulates Inventory is a 1990 air emissions inventory for the U.S.
(excluding Alaska and Hawaii), Canada and Mexico. The inventory includes the following
pollutants:
PM-10
PM-2.5
Sulfur dioxide (S02)
Oxides of nitrogen (NOE)
Ammonia (NH3)
Volatile organic compounds (VOC)
Secondary organic aerosols (SOA)
Primary PM emissions include PM-10 and PM-2.5. Emissions of SO2 and NOX, assisted by
NH3 that acts as a neutralizing agent, form secondary PM in the atmosphere. Also,
certain VOC species, based on reactivity of the organic compound with atmosphere
oxidants, form SOA. Thus, it is necessary to develop a complete inventory of all primarily
and secondarily emitted PM emissions in order to provide the basis for comprehensive
ambient modeling.
For the most part, emissions of SO2, NOr, and VOC were estimated using previously
established methods, while estimates of PM-10, PM-2.5, NH3, and SOA relied on new
methods or data obtained just for the National PM Study.
One product of this emission inventory effort is a data base to be used in air quality
modeling. This data base contains county/Source Classification Code (SCO level
emissions for the U.S. and State-level emissions for Canada and Mexico. Emissions are
provided on an annual and seasonal basis. Also, due to limitations of the modeling
software some point source emissions were aggregated, in order to reduce the number of
data points to be modeled.
The results contained in this report are reported at the EPA region level (for the
U.S.), and at the Province level for Canada and the State level for Mexico. Emissions
from anthropogenic sources of all pollutants (emitting from all three countries) are
included in the National Particulates Inventory. Biogenic emissions of VOC (and SOA)
from the U.S. are also included.
B. OVERVIEW OF METHODOLOGY
The development of the National Particulates Inventory relied heavily on methods
previously developed for other EPA emission inventories. These other inventories include
the 1990 Interim Inventory (EPA, 1993a), the National Air Pollution Emission Trends
Inventory (EPA, 1993b), and the 1985 National Acid Precipitation Assessment Program
(NAPAP) Inventory (EPA, 1989).
The 1990 Interim Inventory was developed to facilitate regional oxidant modeling, in
lieu of States' submittal of their own 1990 emission inventories. The 1990 Interim
Inventory methodology consists of estimating emissions of six major source categories: (1)
electric utilities, (2) non-utility point sources, (3) highway vehicles, (4) nonroad engines
and equipment, (5) area source solvent use, and (6) other area sources.
Page 2
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The Trends Inventory is produced every year to provide the most current emissions
estimates of criteria air pollutants, and show emissions trends. Recent (since 1985)
Trends Inventory methodology is based on a modification of the methods used for the
Interim Inventory.
The National Particulates Inventory uses VOC, SO2, and NO, emissions and/or
estimation methodology developed for some source categories from the 1990 Interim
Inventory. Also, the National Particulates Inventory uses emissions/methods developed
for fugitive dust sources from the Trends Inventory. Biogenic emissions were based on
the method used for the Trends Inventory (which uses EPA's biogenic emissions model).
New or revised emissions/methods were developed for utility, highway, and nonroad
sources. The following chapters provide details on the actual methods used.
C. CONTENTS OF THIS REPORT
This report is organized into three chapters. Following the Introduction (Chapter I),
Chapter II discusses the inventory development including details of the methods used to
estimate emissions for the major source categories. Chapter II also includes a discussion
of how emissions were estimated for Canada and Mexico. Chapter HI contains the
inventory results, by EPA region, and includes discussion related to the relative
significance of emissions sources.
Page 3
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Page 4
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CHAPTER II
EMISSION INVENTORY DEVELOPMENT
A. ELECTRIC UTILITY EMISSIONS
With the potential contribution of utilities to nonattainment problems, it was crucial
to use the most source-specific and detailed information available to develop the steam
electric utility portion of the National Particulates Inventory. Data sources and methods
used in computing steam-electric utility emissions are described below. Using these data
and methods, PM-10, PM-2.5, SO2, NO^, and VOC (and later, secondary organic aerosol)
emissions for 1990 were estimated. Ammonia emissions were not considered for electric
utility sources.
1. 1985-1990 Steam Electric Utility Emission Inventory
The Department of Energy (DOE), Energy Information Administration (EIA) collects
monthly boiler-level data on a yearly basis using Form EIA-767 (Steam-Electric Plant
Operation and Design Report) (DOE, 1990). The steam inventory only includes boiler-
level data and does not include data for gas turbines (GT) or internal combustion (1C)
engines. (The latter account for a very small share of electric utility fuel use and
corresponding emissions and are included in the non-utility point source inventory.)
The steam emission inventory data for 1990 are based on the aggregated monthly
electric utility steam boiler-level data from Form EIA-767. All plants with a nameplate
rating of at least 10 megawatts (MW) that have at least 1 operating boiler are required to
provide this information to EIA. For plants with a nameplate rating from 10 MW to less
than 100 MW, only selected pages of the Form EIA-767 must be completed. Stack and
flue information is not required for these smaller plants.
a. Processing Computerized Data from Form EIA-767
The basis for the fossil-fuel-fired steam electric utility component of the National
Particulates Inventory was the reported primary utility data collected by EIA. The data
from these EIA forms are transferred to data tapes that are not initially serviceable to the
public. Customized computer code was developed to process these data and to account for
the various characteristics of the data tapes.
Form EIA-767 data are reported by the operating utility for each plant with fossil-fuel
steam boilers of 10 MW or greater. The written form is designed so that information for
each plant is reported on separate pages that relate to different levels of data. The
relevant data levels are as follows:
Plant-level One page for delineating the plant configuration, which establishes
the number of boilers and the identification numbers for each boiler, as well as
-------
the associated generators), flue gas desulfurization (FGD) unit(s), flue(s) and
stack(s). These do not necessarily have a one-to-one correspondence.
Boiler-level One page per boiler for monthly fuel consumption and quality data
(for coal, oil, gas, and other), one page for regulatory data, and one page for
design parameters.
Generator-level One page for data relating to up to five generators.
FGD-level One page for up to five FGD units for annual operating data and
one page for each FGD unit for design parameter data.
Flue- and stack-level One page per flue-stack for design parameter data.
Processing Form EIA-767 was accomplished in a series of steps aimed at converting
the computerized data into data base form. Each "page" format was reproduced on the
computer file exactly as it appeared on the written page of the form. The data from each
page was extracted from the computer file, associated with the correct boiler, and
combined with all corresponding data from the other pages for that boiler.
For example, fuel-related boiler data monthly values for each fiiel burned, along
with the fuel's associated sulfur, ash, and heat content are reported on page six. These
data must be aggregated for each fuel type in order to produce annual estimates for each
boiler before they are combined with the other data (such as control devices and
efficiencies, plant location data, associated generator data, and associated stack
parameters).
After SCCs were assigned to each reported fuel for each boiler within a plant, the
SCC-specific data were separated so that each data base observation was on the plant-
boiler-SCC level. These data were subject to a review that resulted in some changes to
boiler firing configuration and bottom type. In some cases, the utilities had just
submitted updated Forms EIA-767. These changes subsequently changed some SCO
assignments, NO, control efficiencies, and emissions estimates.
b. Emissions Algorithms
Data that were not obtained directly from the computerized data files (or converted to
other measurement units) were developed by using algorithms that have been utilized
since the 1980s. These variables include heat input, pollutant emissions, NO, control
efficiencies, and SCO. Emission factors from EPA's Compilation of Air Pollutant Emission
Factors (AP-42) were used in calculating emissions (EPA, 199 la). The emission factor
used depends upon the SCO and pollutant, as explained below.
The appropriate SCO was assigned to each fuel based on its characteristics. For
coal, the SCO is based on the American Society for Testing and Materials (ASTM)
criteria for moisture, mineral-free matter basis (if greater than 11,500 Btu/lb,
coal type is designated to be bituminous; if between 8,300 and 11,500 Btu/lb, coal
type is designated to be subbituminous; and if less than 8,300 Btu/lb, coal type is
designated to be lignite) and the boiler type (firing configuration and bottom type)
as specified by AP-42. If both coal and oil were burned in the same boiler, it was
Page 6
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assumed that the oil is distillate; otherwise, if only oil was burned, it was
assumed to be residual. Then, based on the fuel and boiler type, the SCC is
assigned. For natural gas, the SCC is based on fuel and boiler type. See Table
II- 1 for a complete list of the relationships among fuel type, firing type, bottom
type, and SCC.
NOX control efficiency was based on the assumption that the unit would be
controlled so that its emission rate would equal its regulatory limit, expressed on
an annual equivalent basis. After calculating the heat input, controlled
emissions, assuming compliance with the applicable standard, was back-
calculated. After calculating the uncontrolled NO, emissions, the presumed net
control efficiency was calculated.
PM-10 control efficiency was used to calculate both PM-10 and PM-2.5 emissions.
Since only TSP control efficiency is reported on Form EIA-767, the PM-10
calculator program was used to derive PM-10 efficiencies (EPA, 1994). (The PM-
10 calculator estimates PM-10 control efficiencies based on the SCC and the
primary and secondary control devices. The control efficiencies from the PM-10
calculator are based on data from AP-42 for specific SCCs.)
The following equation was used to compute controlled PM-10 and PM-2.5
emissions:
PM10 or PM2& - - - (1 - PM10 or
The following equation was used to compute controlled SO2 emissions:
SO2 fuel AP-42 ,-.
(ftvJ) = burned ' emf ' % sulfur
The following equation was used to compute controlled NO, emissions with EPA-
specified 80 percent rule effectiveness (emf means emission factor; eff means
control efficiency):
NO, fuel AP-42 . (Q8
(tons) burned
Page?
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Table 11-1
Steam Electric Utility Unit
Source Classification Code Relationships
Fossil-Fuel
Coal
Bituminous
Subbrtuminous
Lignite
Firing Type
No data
Wall-
Opposed
Tangential
Stoker
Cyclone
Fluidized Bed
No data
Wall
Opposed
Tangential
Stoker
Cyclone
No data
Wall
Opposed
Tangential
Stoker
Cyclone
Bottom Type
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
All
All
N/A
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
All
All
All
All
All
All
All
All
sec
1-01-002-02
1-01-002-01
1-01-002-02
1-01-002-02
1-01-002-01
1-01-002-02
1-01-002-02
1-01-002-01
1-01-002-02
1-01-002-12
1-01-002-01
1-01-002-12
1-01-002-04
1-01-002-03
1-01-002-17
1-01-002-22
1-01-002-21
1-01-002-22
1-01-002-22
1-01-002-21
1-01-002-22
1-01-002-22
1-01-002-21
1-01-002-22
1-01-002-26
1-01-002-21
1-01-002-26
1-01-002-24
1-01-002-23
1-01-003-01
1-01-003-01
1-01-003-01
1-01-003-02
1-01-003-06
1-01-003-03
PageS
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Table 11-1 (continued)
Fossil-Fuel Firing Type Bottom Type SCO
Residual Oil
Distillate Oil
Natural Gas
No data
Wall
Opposed
Tangential
Stoker
Cyclone
No data
Wall
Opposed
Tangential
Stoker
Cyclone
No data
Wall
Opposed
Tangential
Stoker
Cyclone
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
1-01-004-01
1-01-004-01
1-01-004-01
1-01-004-04
1-01-004-01
1-01-004-01
1-01-005-01
1-01-005-01
1-01-005-01
1-01-005-01
1-01-005-01
1-01-005-01
1-01-006-01
1-01-006-01
1-01-006-01
1-01 -006-04
1-01-006-01
1-01-006-01
NOTE: Wall firing includes front, arch, concentric, rear, side, vertical, and duct burner firing.
The following equation was used to compute uncontrolled VOC emissions (there
were no control efficiencies):
VOC fuel . AP-42 . 1
(tons) ~ burned emf 2000
The following equation was used to compute heat input:
heat input _ fuel , heat
(MMBtu) ~ burned content
Although Form EIA-767 data are collected from plants with a total plant capacity of
at least 10 MW, there are fewer required data elements (identification data, boiler fuel
quantity and quality data, and FGD data, if applicable) for those plants with a total
capacity between 10 MW and 100 MW. Thus, missing values are introduced in these
situations. Because of time constraints, most data elements were not assigned a default
value other than zero. If variables for boiler firing and bottom type were missing (these
are needed in the SCC assignment) the default values for wall-fired and dry bottom type
were assigned. For ambient modeling purposes, it is necessary to know the location
(latitude and longitude) of each boiler. If the longitude and latitude for a specific boiler
were missing, they were replaced whenever possible with either (1) the latitude and
longitude from other boilers in that same plant or (2) county centroid coordinates.
Page 9
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B. NON-UTIUTY POINT SOURCES
The PM-10, PM-2.5, and NH3 emissions were calculated using a methodology
consistent with emission estimates in the 1990 Interim Inventory/Trends Inventory. This
means non-utility point source emissions are calculated based on emission estimates from
the 1985 NAPAP Inventory projected to 1990 using Bureau of Economic Analysis (BEA)
Industrial Earnings data. Because annual PM-10 and PM-2.5 emission estimates are not
available from the NAPAP files, annual TSP emissions were used as the starting point for
estimating PM-10 and PM-2.5 emissions. The procedure used to estimate 1990 PM-10,
PM-2.5 and NH3 emissions from the 1985 NAPAP TSP emissions is described below.
1. PM-10 Emission Calculations
The following steps were used to calculate PM-10 emissions:
Step 1 Controlled TSP emissions were projected to 1990 using BEA data and
emission estimates from each point source in the 1985 NAPAP Inventory (excluding steam
electric utilities). Emission estimates were projected to 1990 based on the growth in
earnings by industry (2-digit Standard Industrial Classification code [SIC]). Historical
earnings data from BEA's Table SA-5 were used to represent growth in earnings from
1985 to 1990 (BEA, 1991).
Each record in the point source inventory was matched to the BEA earnings data
based on the State and the 2-digit SIC. Table II-2 shows the BEA earnings category used
to project growth for each of the 2-digit SICs found in the 1985 NAPAP Inventory. No
growth in emissions was assumed for all point sources for which the matching BEA
earnings data was not complete. Table II-3 shows the national average growth and
earnings by industry from BEA's Table SA-5. Controlled emissions of TSP for 1990 were
calculated as follows:
= CTSPM + (crspM
Where:
= Controlled TSP Emissions for 1990
CTSP^ = Controlled TSP Emissions from 1985 NAPAP
EGSMO = Earnings Growth from 1985 to 1990
Step 2 1990 uncontrolled TSP emissions were calculated from the controlled emissions
and the control efficiency from the 1985 NAPAP Inventory using the following formula:
UTSPW =
, . \TSPeff]
( 100 J
Where:
UTSP.,0 = Uncontrolled TSP Emissions for 1990
CTSPao = Controlled TSP Emissions for 1990
TSPeff = TSP Control Efficiency from 1985 NAPAP Inventory
Page 10
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Table 11-2
BEA Industry Earnings Data Classifications
SIC BEA Industry Earnings Category*
01 Farm
02 Farm
07 Agricultural services, forestry, fisheries, and other
08 Agricultural services, forestry, fisheries, and other
09 Agricultural services, forestry, fisheries, and other
10 Metal mining
11 Coal mining
12 Coal mining
13 Oil and gas extraction
14 Nonmetallic minerals, except fuels
15 Construction
16 Construction
17 Construction
20 Food and kindred products
21 Tobacco manufactures
22 Textile mill products
23 Apparel and other textile products
24 Lumber and wood products
25 Furniture and fixtures
26 Paper and allied products
27 Printing and publishing
28 Chemicals and allied products
29 Petroleum and coal products
30 Rubber and miscellaneous plastic products
31 Leather and leather products
32 Stone, clay, and glass products
33 Primary metal industries
34 Fabricated metal products
35 Machinery, except electrical
36 Electric and electronic equipment
37 Transportation equipment, excluding motor vehicles
38 Instruments and related products
39 Miscellaneous manufacturing industries
40 Railroad transportation
41 Local and interurban passenger transit
42 Trucking and warehousing
44 Water transportation
45 Transportation by air
46 Pipelines, except natural gas
47 Transportation services
48 Communication
49 Electric, gas, and sanitary services
50 Wholesale trade
51 Wholesale trade
Page 11
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Table 11-2 (continued)
SIC BEA Industry Earnings Category*
~~52Retail trade
53 Retail trade
54 Retail trade
55 Retail trade
56 Retail trade
57 Retail trade
58 Retail trade
59 Retail trade
60 Banking and credit agencies
61 Banking and credit agencies
62 Holding companies and investment services
63 Insurance
64 Insurance
65 Real estate
66 Real estate
67 Holding companies and investment services
70 Hotels and other lodging places
72 Personal services
73 Business and miscellaneous repair services
75 Auto repair, services, and garages
76 Business and miscellaneous repair services
78 Amusement and recreation services and motion pictures
79 Amusement and recreation services and motion pictures
80 Health services
81 Legal services
82 Educational services
83 Social services and membership organizations
84 Miscellaneous professional services
86 Social services and membership organizations
88 Private households
89 Miscellaneous professional services
91 Federal government, civilian
92 State and local government
93 State and local government
94 State and local government
95 State and local government
96 State and local government
97 Federal government, civilian
371 Motor vehicles and equipment
NOTE: State earnings by industry were matched to each of the 2- and
3-dgpt SICs to develop annual growth rates.
Page 12
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Summary of BEA's Tab.e SA-5:
,n Earnings By .ndustry
Percent Growth from:
Industry
Farm
Agricultural services, forestry, fisheries, and other
Coal mining
Oil and gas extraction
Metal mining
Nonmetallic minerals, except fuels
Construction
Manufacturing
Nondurable goods
Food and kindred products
Textile mill products
Apparel and other textile products
Paper and allied products
Printing and publishing
Chemicals and allied products
Petroleum and coal products
Tobacco manufactures
Rubber and miscellaneous plastic products
Leather and leather products
Durable goods
Lumber and wood products
Furniture and fixtures
Primary metal industries
Fabricated metal products
Machinery, except electrical
Electric and electronic equipment
Transportation equipment, excluding motor vehicles
Motor vehicles and equipment
Stone, day, and glass products
Instruments and related products
Miscellaneous manufacturing industries
Railroad transportation
1965 to 1987
14.67
23.58
-17.46
-39.23
3.03
2.33
7.27
-0.39
2.54
1.67
8.50
1.72
2.62
7.44
1.75
10.82
-1.97
5.27
-9.39
2.03
10.03
6.82
-9.09
-4.72
6.72
3.17
8.44
-6.45
023
0.04
1.84
14.13
1987 to 1988
2.73
5.43
6.37
4.94
18.01
3.74
4.81
2.95
3.26
1.34
-0.64
1.25 .
0.94
5.67
6.94
-322
2.43
5.51
1.64
2.76
5.15
2.35
5.32
2.55
6.02
16.01
-1.57
220
1.61
60.65
6.92
-2.53
1988 to 1989
14.58
1.01
-4.16
3.88
8.94
2.79
-1.36
0.97
-0.67
-120
-1.39
1.62
-0.14
0.81
0.32
-3.02
2.43
0.68
-3.58
-1.14
-3.54
-1.46
0.34
-0.86
0.32
1.91
0.55
2.96
-1.96
-0.82
-221
-3.83
1989 to 1990
-3.11
2.46
4.73
5.16
4.56
-0.45
-3.80
-1.65
-0.38
-024
-4.97
-422
-0.39
0.43
1.61
1.06
5.01
-0.14
-2.55
2.72
3.71
2.98
3.03
-1.91
-1.92
-322
-1.07
-5.43
3.19
2.91
2.54
6.03
Page 13
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Table 11-3 (continued)
Percent Growth from:
Industry
Trucking and warehousing
Water transportation
Local and interurban passenger transit
Transportation by air
Pipelines, except natural gas
Transportation services
Communication
Electric, gas, and sanitary services
Wholesale trade
Retail trade
Banking and credit agencies
Insurance
Real estate
Holding companies and investment services
Services
Hotels and other lodging places
Personal services
Private households
Business and miscellaneous repair services
Auto repair, services, and garages
Amusement and recreation services and motion pictures
Health services
Legal services
Educational services
Social services and membership organizations
Miscellaneous professional services
Government and government enterprises
Federal, civilian
Federal, military
State and local
1965 to 1987 1967 to 1988
5.63
6.92
13.45
12.01
521
15.92
1.94
0.07
5.01
5.19
12.44
14.09
92.14
39.05
14.63
12.65
7.17
-5.68
17.05
6.65
17.93
15.15
20.14
9.35
17.39
11.28
5.43
-0.54
1.96
7.88
326
0.07
0.51
4.63
3.67
6.52
0.68
3.05
5.87
4.39
2.45
420
-6.98
-34.86
7.84
5.59
2.35
2.41
-17.34
2.46
16.43
7.08
9.92
7.17
6.45
5.04
320
3.79
-1.07
3.63
1988 to 1989 1989 to 1990
020
-1.02
2.14
4.94
4.93
4.60
2.81
0.63
2.44
0.65
-0.33
1.52
-7.87
-12.18
527
1.71
7.44
0.83
5.79
3.00
4.06
5.11
4.09
3.88
7.95
7.08
2.33
121
-1.58
3.19
0.99
2.83
1.44
4.36
3.53
4.97
2.07
0.39
-1.02
0.94
-0.49
2.71
0.48
16.91
4.87
229
5.41
3.69
4.34
3.93
7.59
628
4.80
2.60
7.37
4.12
226
1.96
3.19
3.04
Page 14
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Step 3 1990 uncontrolled PM-10 emissions were calculated by applying an SCC-
specific (uncontrolled) particle size distribution factor to the uncontrolled TSP emissions.
The SCC-specific uncontrolled particle size distribution factors were developed based on
information contained in AP-42, engineering judgement, and other sources. These are the
same uncontrolled particle size distribution factors used in the PM-10 calculator program.
The following formula was used to estimate uncontrolled PM-10 emissions:
UPM10W = UTSPn PSM10
Where:
UPMIO,*, = Uncontrolled PM-10 Emissions for 1990
UTSP,*, = Uncontrolled TSP Emissions for 1990
PSM10 = Fraction of TSP that is PM-10 for SCC
Step 4 The PM-10 control efficiency was determined using the PM-10 calculator
program (as explained previously) (EPA, 1994). Where SCC-specific control information
was not available, the control efficiencies were based on general particulate control
information found in Appendix C of AP-42.
Step 5 Controlled PM-10 emissions for 1990 were calculated using the following
formula:
I 100 )
Where:
= Controlled PM-10 Emissions for 1990
= Uncontrolled PM-10 Emissions for 1990
PMlOeff = PM-10 Control Efficiency for given SCC and control equipment
In many cases, the PM-10 emissions calculated based on the particle size distribution
and PM-10 control efficiency were higher than the TSP emissions. The source of this
problem is inconsistency between the TSP control efficiencies from the 1985 NAPAP
Inventory and the control efficiencies determined using the PM-10 calculator (Step 4). In
the instances where the controlled PM-10 emissions were calculated to be higher than the
controlled TSP emissions, the controlled PM-10 emissions were replaced with the
controlled TSP emissions. The uncontrolled PM-10 was then recalculated using the
revised PM-10 emissions and the control efficiency from the PM-10 calculator. In other
words, it is assumed that in these instances, virtually all of the particles above 10 microns
are being controlled and that particles emitted after the control device are all particles of
10 microns or less. This assumption is consistent with Trends Inventory methodology.
The basis for replacing the PM-10 emissions with the TSP emissions in these cases is
the assumption that the controlled TSP emissions from NAPAP are the best data that are
available as a measure of point source particulate emissions. If the assumption was that
the uncontrolled emissions were the best data available, then an adjustment to the TSP
control efficiency (resulting in an increase to actual TSP emissions) would be performed
rather than replacing the PM-10 emissions. Table II-4 shows the breakdown of emissions
Page 15
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Table 11-4
PM-10 Emission Estimates
Estimation Method"
W/0 TSP replacement
0
1
2
3
4
5
Total w/o TSP replacement
TSP replacement
0
1
2
3
4
5
Total with TSP replacement
TOTAL
Number of
Records
(Processes)
508
2,644
3,491
1 1 .527
2,387
10,344
30,901
11
1.228
543
1,711
554
1.069
5,116
36,017
TSP
Emissions
(tpy)
22.961
134,072
105,916
437.480
105.374
334,648
1,140,451
3.091
84.886
18,130
57.708
19.907
51.263
234,985
1.375.436
Calculated
PM-10
Emissions"
(tpy)
13,838
80,112
63,394
208,591
53,182
171,810
590,927
4,424
782,234
345.062
495,047
666,454
520,766
2,813,987
3.404,914
Final
PM-10
Emissions0
(tpy)
13,838
80,112
63,394
208,591
53,182
171,810
590,927
3.091
84,886
18,130
57.708
19,907
51.263
234,985.0
825,912
NOTES: 'Estimation Methods:
0 - Not applicable. Emissions are known to be zero.
1 Emissions based on source testing or other emission measurements.
2 - Emissions based on material balance using eng'neering expertise and knowledge of process.
3 Emissions computer-calculated based on Federal emission factors.
4 - Best guess
5 - Emissions calculated using a special emission factor.
6 The value of PM-10 emissions as calculated in Step 5.
° The value of PM-10 emissions included in inventory results.
by estimation method and whether or not the PM-10 emissions were replaced with TSP
emissions as a result of this issue.
Page 16
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2. PM-2.5 Emission Calculations
The following steps were used to estimate PM-2.5 emissions.
Step 1 Uncontrolled PM-2.5 emissions were calculated by applying an SCC-specific
particle size distribution factor to the "final" uncontrolled PM-10 emissions (i.e., after any
replacements with TSP). The following formula was used:
UPM2.5 = UPM10 - PSMz6
PSM10
Where:
UPM2.590 = Uncontrolled PM-2.5 Emissions for 1990
UPMlOgo = Uncontrolled PM-10 Emissions for 1990
PSM2.J = Fraction of TSP that is PM-2.5 for SCC
PSM10 = Fraction of TSP that is PM-10 for SCC
Step 2 PM-2.5 emissions were calculated by repeating Steps 4 and 5 from the
PM-10 calculation procedure, and substituting PM-2.5 for PM-10.
3. Calculation of NH, Emissions
Ammonia emissions were calculated by growing the 1985 NAPAP NH3 emissions
using the BEA growth factors, and the following formula:
Where:
CNHa^j = Controlled NH3 Emissions for 1990
CNH3(86) = Controlled NH3 Emissions for 1985 NAPAP
EGgg.90 = Earnings growth from 1985 to 1990
C. FUGITIVE DUST SOURCES
Estimates of 1990 fugitive dust PM-10 emissions for the National Particulates
Inventory were taken from the Trends Inventory for the following categories: agricultural
tilling, construction, paved roads, unpaved roads, and wind erosion. New PM-10 and
PM-2.5 emission estimates for cattle feed lots were developed. The Trends Inventory
emission estimates are all for PM-10 at the State level, with the exception of construction
which are for PM-10 at the EPA-region level. This section describes how the Trends
Inventory PM-10 estimates were developed, how they were distributed to the county-level
for the National Particulates Inventory, and how the PM-2.5 estimates were developed.
1. Agricultural Tilling
The following AP-42 particulate emission factor equation was used to determine
regional PM-10 emissions from agricultural tilling for 1985-1990:
Page 17
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E = c k - s °6 p a
Where:
E = PM-10 emissions (Ibs/yr)
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier (PM-10=0.21)
s = silt content of surface soil, defined as the mass fraction of particles
smaller than 75 urn diameter found in soil to a depth of 10 cm (%)
p = number of passes or tillings in a year (assumed to be 3 passes)
a = acres of land planted
By comparing the USD A surface soil map with the USD A county map, soil types were
assigned to all counties of the continental U.S. Silt percentages were determined by using
a soil texture classification triangle (USDA, 1988; USDA, 1978). For those counties with
organic material as its soil type, silt percentages were obtained from EPA (1974a).
Weighted mean State silt values were determined using the number of hectares and silt
percentages for each county as the weighting criteria. These silt values were assumed
constant for the six year period examined. It was assumed that crops are tilled three
times each year, on average, and this value was used for p (EPA, 1974b) The acres of
crops planted in each State were obtained for 1990 from the USDA (USDA, 1991).
State-level PM-10 estimates were distributed to the county-level using county
estimates of cropland harvested from the 1987 Census of Agriculture (BOC, 1987a). The
following formula was used:
County Emissions = County Cropland Harvested . Sfate Emissions
State Cropland Harvested
PM-2.5 emissions were calculated from the county-level PM-10 emissions by applying
the AP-42 particle size multiplier of 0.10 (or 0.476 of PM-10).
2. Construction Activities
The following AP-42 particulate emission factor equation for heavy construction was
used to determine regional PM-10 emissions from construction activities for 1990:
E = T '$/" 771 P
Where:
E = PM-10 emissions tons per year (tpy)
T = TSP emission factor (1.2 ton/acre of construction/month of activity)
$ = dollars spent on construction (million $)
f = factor for converting dollars spent on construction to acres of construction
(varies by type of construction, acres/million $)
m = months of activity per year (varies by type of construction)
P = dimensionless PM-10/TSP ratio (0.22)
Page 18
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Estimates of the dollars spent on the various types of construction by EPA region for
1987 were obtained from the Census Bureau (BOC, 1987b). The fraction of total U.S.
dollars spent in 1987 for each region for each construction type was calculated. Since
values from the Census Bureau are only available every five years, the Census dollars
spent in the U.S. for construction were normalized for 1990 using estimates of the dollars
spent on construction in the U.S. as estimated by the F.W. Dodge corporation (BOC,
1990). This normalized Census value was distributed by region and construction type
using the above calculated fractions.
EPA (1974a) determined that for different types of construction, the number of acres
was proportional to dollars spent on that type construction. This information
(proportioned to constant dollars) was utilized along with total construction receipts to
determine the total number of acres of each construction type. Estimates of the duration
(in months) for each type construction were derived from EPA (1974a) PM-10/TSP ratios
for 19 test sites for 3 different construction activities were averaged to derive the PM-10
fraction used in the emission estimates (EPA, 1988).
Regional-level PM-10 estimates were distributed to the county-level using county
estimates of payroll for construction (SICs 15, 16, 17) from County Business Patterns
(BOC, 1992). The following formula was used:
County Emissions = Counfr Construction Payroll , Rggional Emissions
Regional Construction Payroll
PM-2.5 emissions were calculated using the county-level PM-10 emissions by applying
the particle size multiplier of 0.02024.
3. Paved Road Resuspension
The participate emission factor equation for paved urban roads was used to determine
regional PM-10 emissions from paved road resuspension for 1990. This methodology was
modified slightly by adding a "dry days" term similar to that used in the unpaved road
emission factor in an effort to account for meteorological influences on emissions.
E = VMT
Where:
E = PM-10 emissions from paved road resuspension (Ibs/yr)
VMT = vehicle miles travelled per year on paved roads
k = base emission factor (0.0081 Ibs/VMT for PM-10)
sL = road surface silt loading (grains/sq.ft)
q = exponent (0.8 dimensionless)
p = number of days with at least 0.01 inch of precipitation per year
x = number of days in a year (either 365 or 366)
An empirical model was used to develop silt loading values based on traffic volume
(EPA, 1984). The surface silt loading values were determined for various paved road
Page 19
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functional classes by EPA region. The average daily traffic volume (ADTV) was calculated
by dividing the total vehicle miles travelled (VMT) for a particular functional class by the
total mileage of roads within that functional class and then dividing that quantity by the
number of days in the year. The number of dry days per year were calculated by
obtaining the number of days with 0.01 inches or more of precipitation in a year from the
local climatological data for several meteorological stations within each EPA region
(NCDC, 1990). For 1990, the total VMT (by EPA region and functional class) was
obtained from the Annual Highway Statistics report (DOT, 1990). VMT from paved roads
was calculated by subtracting the unpaved VMT (see unpaved roads section) from the
total VMT.
State-level PM-10 estimates were distributed to the county-level using estimates of
county rural and urban land area from the U.S. Census Bureau (BOC, 1994). The
following formula was used:
County Emissions = f CoM"fr Urban Land . State Urban Emissions] +
^ State Urban Land )
(County Rural Land , Sfate Rural Emissions}
State Rural Land
j
PM-2.5 emissions were calculated from the county-level PM-10 emissions by applying
the particle size multiplier of 0.6 (or 0.75 of PM-10).
4. Unpaved Roads
The following AP-42 participate emission factor equation was used to determine
regional PM-10 emissions from unpaved roads for 1990:
E - VMT .» .5.9 -JL - A .
12 30 [3
Where:
E = PM-10 emissions (Ibs/year)
VMT = vehicle miles travelled annually on unpaved roads
k = dimensionless particle size multiplier (PM-10=0.36)
s = silt content of road surface material (%)
S = mean vehicle speed (mph)
W = mean vehicle weight (ton)
w = mean number of wheels
p = number of days with at least 0.01 inch of precipitation per year
x = number of days in a year (either 365 or 366)
Average State silt content values developed as part of the 1985 NAPAP Inventory,
were obtained from the Illinois State Water Survey. Silt contents of over 200 unpaved
Page 20
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roads from over 30 States were obtained. Average silt content of unpaved roads were
calculated for each State that had three or more samples for that State. For States that
did not have three or more samples, the average for all samples from all States was
substituted.
Mean vehicle speeds were assumed for the various unpaved road functional classes.
The assumed speeds are listed below:
Rural Roads
Minor arterial
Major collector
Minor collector
Speed
(mph)
45
40
40
Urban Roads
Other principal arterial
Minor arterial
Collector
Speed
(mph)
50
45
40
Local 35 Local 40
Estimates of vehicle weight and the number of wheels per vehicle were made using
information provided by the U.S. DOT (DOT, 1991a). These data indicated that the
following weighted average values were appropriate for the following vehicle classes:
Vehicle Type
Single Trailer Trucks
Multi Trailer Trucks
Single Unit Trucks
Passenger Vehicles
Weight Number of
(tons) Wheels
26.7
31.5
9.55
2.5
IS
20
7
4
National statistics provided by U.S. DOT on travel activity by vehicle type were
utilized to allocate the percentage of travel on each road type to each vehicle type (DOT,
1991b).
The number of dry days per year was calculated by obtaining the number of days with
0.01 inches or more of precipitation in a year from the local climatological data for several
meteorological stations within each EPA region (NCDC, 1990).
The VMT on unpaved roads was developed from two sources. The first source, Annual
Highway Statistics provided rural and urban mileage by surface type and functional
classification (DOT, 1990). This source, however, did not include local functional class
unpaved road mileage. As a consequence, a second source of information was utilized to
determine VMT from local functional class unpaved roads. Lotus 1-2-3 spreadsheets were
obtained from the U.S. DOT (DOT, 1991b). These spreadsheets contained local functional
class rural and urban unpaved road mileage by ADTV ranges. These ADTV ranges were
then used to calculate the VMT for both local and nonlocal functional systems.
State-level PM-10 estimates were distributed to the county-level using the same
methodology as was used for Paved Roads.
PM-2.5 emissions were calculated from the county-level PM-10 emissions by applying
the AP-42 particle size multiplier of 0.095 (or 0.264 of PM-10).
Page 21
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5. Wind Erosion
PM-10 wind erosion emission estimates for agricultural lands were calculated using a
modification of the methodology used by Gillette and Passi (1988) to develop wind erosion
emission estimates for the 1985 NAPAP Inventory. Several simplifying assumptions were
made in order to perform the calculations using a spreadsheet model (Gillette, 1991). The
NAPAP methodology and the method used to develop the wind erosion estimates in this
study both determine expected dust fluz based on the probability distribution of wind
energy. The methodology uses the mean wind speed coupled with information concerning
the threshold friction velocity for the soil and information on precipitation to predict the
wind erosion flux potential for soils.
The basic equation used to determine the expected dust flux is given by the following
equation:
Where:
I = dust flux (gm/cm2/sec)
k = PM-10 particle size multiplier (0.9)
C = constant (4 x 10~14 gm/cm2/sec)
Cd = coefficient of drag
u = mean wind speed (cm/sec)
(3,x) = incomplete gamma function (i.e., probability distribution)
In order to evaluate (3,x), x must be determined from the following equation:
The threshold velocity (u^) can be determined from the threshold friction velocity (u^,
which is a function of soil type and precipitation) from the following equation:
In order to calculate the flux of emissions from wind erosion using the above equation,
information concerning the average monthly wind speed, total monthly precipitation, and
anemometer height used to measure the wind speed was necessary. Values for monthly
wind speed, total monthly precipitation, and anemometer height were obtained from the
local climatological data for several meteorological stations within each State (NCDC,
1990). For most States, several meteorological stations' data were obtained and an
overall average was determined for the State. The anemometer height was used to
determine the coefficient of drag (Cd) from the following equation:
Page 22
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Where:
za = anemometer height
Information concerning the average soil type for each State was determined from the
USDA surface soil map (USDA, 1988). A single soil type was assigned to each State in
order to determine a single value for the threshold friction velocity (u.,). The u,, utilized
represented either a before or after rain value, depending upon whether or not
precipitation exceeded 5.08 cm during a month. If precipitation exceeded this amount, the
"after-rain" u.t value was used for all succeeding months until the time of a significant
tillage operation or plant emergence. Values of the threshold friction velocity for different
soil types both before and after rain have been reported by Gillette and Passi (1988). The
value of Ut was then calculated using the value of u,t determined and Cd. Once u» is
determined, then x is calculated and the incomplete gamma function is evaluated.
Following determination of the incomplete gamma function, the flux for each month was
calculated.
Wind erosion was assumed to be zero from the time of plant emergence until harvest
(i.e., the percent of time when the ground is planted). Separate flux estimates were made
for fall-planted crops and spring-planted crops. This meant that flux estimates were only
calculated from July to October (for fall-planted crops) and from September until May (for
spring-planted crops). This approach is consistent with the methodology utilized by
Gillette and Passi. However, because they were evaluating the erosion potential over a
multi-year time frame, Gillette and Passi utilized previous year precipitation information
to assign the threshold friction velocity to an area. La this work, the before rain u,, value
was always utilized for January for spring planted crops rather than evaluating whether
or not any month between September and December of the previous year had more than
5.08 cm of precipitation.
Once the emission flux potential for each month for each crop type (fall- or spring-
planted) for each State was calculated, then information on the number of acres of spring-
or fall-planted crops in each State were required (and the number of seconds per month)
to determine the emissions. The number of acres of crops planted in each State was
obtained for each of the six years from the USDA, Evaluation of which crops were spring-
planted or fall-planted for each State was made using information available from the
USDA (1984).
State-level PM-10 estimates were distributed to the county-level using estimates of
county rural land area from the U.S. Census Bureau (BOC, 1994). The following formula
was used:
County Emissions = County Ruralrlmf - State Emissions
State Rural Land
Page 23
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PM-2.5 emissions were calculated from the county-level PM-10 emissions by applying
the AP-42 particle size multiplier for industrial wind erosion of 0.2 (or 0.40 of PM-10), as
no other particle size data were available.
6. Cattle Feed Lots
County-level PM-10 emission estimates for cattle feed lots were estimated using
activity data from the Census of Agriculture (head of cattle per county) and a PM-10
emission factor of 17 tons per 1,000 head (BOC, 1987a; EPA, 1988). The following
formula was used:
County Emissions = County Head of Cattle - 17
1,000
PM-2.5 emissions were calculated from the county-level PM-10 emissions by applying the
AP-42 particle size multiplier for agricultural tilling of 0.10 or (0.476 of PM-10).
The National Particulates Inventory also includes NH3 emissions for cattle feet lots,
which were estimated based on the 1985 NAPAP Inventory estimates.
D. OTHER AREA AND MOBILE SOURCES
The basis for the emission estimates for most (non- fugitive dust) area source
categories was the 1985 NAPAP Area Source Emissions Inventory (EPA, 1989), with the
exception of nonroad mobile sources, and prescribed burning. This section discusses area
source emission estimates performed for this study other than those for fugitive dust. The
methodology used to estimate emissions for 1990, including the sources for growth
indicators and updated emission factors, are discussed. Nonroad mobile source emission
estimates are based on a 1990 nonroad emission inventory compiled by EPA (EPA,
1992a).
As with the point sources, the 1985 NAPAP Inventory contained TSP emissions.
Except where noted, these TSP emissions were grown to 1990 and then particle size
multipliers were applied to estimate PM-10 and PM-2.5 emissions. Ammonia emissions
were estimated by growing NH3 emissions taken from the 1985 NAPAP Inventory.
1. Growth Indicators
Emission estimates from the 1985 NAPAP Inventory were grown to 1990 based on
historical BEA earnings data (see Section II.B.l), historical estimates of fuel consumption,
or other category-specific growth indicators. Table II-5 shows the growth indicators used
for each NAPAP area source category.
The State Energy Data System (SEDS) data (DOE, 1991a) were used as an indicator
of emissions growth for the area source fuel combustion categories and for the gasoline
marketing categories shown in Table II-5. (SEDS reports fuel consumption by sector and
fuel type.) Since fuel consumption is the activity level used to estimate emissions for
Page 24
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Table 11-5
Area Source Growth Indicators
NAPAP
SCC
Category Description
Data
Source*
Growth Indicator
1 Residential Fuel - Anthracite Coal SEOS
2 Residential Fuel - Bituminous Coal SEDS
3 Residential Fuel - Distillate Oil SEDS
4 Residential Fuel - Residual Oil N/A
5 Residential Fuel Natural Gas SEDS
6 Residential Fuel - Wood BEA
7 Commercial/Institutional Fuel - Anthracite Coal SEDS
8 Commercial/Institutional Fuel Bituminous Coal SEDS
9 Commercial/Institutional - Distillate Oil SEDS
10 Commercial/Institutional - Residual Oil SEDS
11 Commercial/Institutional - Natural Gas SEDS
12 Commercial/Institutional - Wood BEA
13 Industrial Fuel - Anthracite Coal SEDS
14 Industrial Fuel - Bituminous Coal SEDS
15 Industrial Fuel - Coke BEA
16 Industrial Fuel - Distillate Oil SEDS
17 Industrial Fuel - Residual Oil SEDS
18 Industrial Fuel - Natural Gas SEDS
19 Industrial Fuel - Wood BEA
20 Industrial Fuel - Process Gas SEDS
21 On-Site Incineration - Residential BEA
22 On-Site Incineration - Industrial BEA
23 On-Site Incineration-Commercial/lnstitutional BEA
24 Open Burning - Residential BEA
25 Open Burning - Industrial BEA
26 Open Burning - Commercial/Institutional BEA
45 Railroad Locomotives AAR, 1991
46 Aircraft LTOs - Military BEA
47 Aircraft LTOs - Civil FAA, 1991a
48 Aircraft LTOs - Commercial FAA, I991b
49 Vessels-Coal COE, 1991
50 Vessels - Diesel Oil COE, 1991
51 Vessels - Residual Oil COE. 1991
54 Gasoline Marketed SEDS
60 Forest Wild Fires N/A
61 Managed Burning - Prescribed N/A
62 Agricultural Field Burning BEA
63 Frost Control - Orchard Heaters BEA
64 Structural Fires N/A
Res - Anthracite
Res - Bituminous
Res - Distillate oil
Zero growth
Res - Natural gas
Population
Comm Anthracite
Comm Bituminous
Comm - Distillate oil
Comm Residual oil
Comm - Natural gas
Services
Ind - Anthracite
Ind - Bituminous
Total Manufacturing
Ind - Distillate oil
Ind - Residual oil
Ind - Natural gas
Total Manufacturing
Ind - LPG
Population
Total Manufacturing
Services
Population
Total Manufacturing
Services
Railroad ton-miles (national)
Military
Aircraft civil
Aircraft - commercial
Cargo tonnage (national)
Cargo tonnage (national)
Cargo tonnage (national)
Trans - Motor gasoline
Zero growth
Zero growth
Farm
Farm
Zero growth
Page 25
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Table 11-5 (continued)
NAPAP
sec
Category Description
Data
Source*
Growth Indicator
99 Minor Point Sources BEA
100 Publicly Owned Treatment Works BEA
102 Fugitive Emissions From Synthetic Organic BEA
Chemical Manufacturing
103 Bulk Terminal and Bulk Plants BEA
104 Fugitive Emissions From Petroleum Refinery DOE
105 Process Emissions From Bakeries BEA
106 Process Emissions From Pharmaceutical BEA
Manufacturing
107 Process Emissions From Synthetic Fiber BEA
Manufacturing
108 Crude Oil and Natural Gas Production Fields BEA
109 Hazardous Waste Treatment, Storage, and BEA
Disposal Facilities (TSDFs)
Population
Electric, Gas, and Sanitary Services
Mfg - Chemicals and Allied Products
Trucking and Warehousing
Refinery operating cap
Mfg - Food and Kindred Products
Mfg Chemicals and Allied Products
Mfg Textile Mill Products
Oil and Gas Extraction
Total Manufacturing
NOTES:
N/A
AAR
COE
FAA
SEDS
Not applicable
Association of American Railroads
U.S. Army Corps of Engineers
Federal Aviation Administration
State Energy Data System
these categories, fuel consumption is a more accurate predictor of changes in emissions,
compared to other surrogate indicators such as earnings or population. A log linear
regression procedure was used to fill in missing data points for fuel consumption
categories if at least three data points in the time series (1985 to 1989) were available. A
summary of SEDS national fuel consumption by fuel and sector can be found in Table
II-6.
Additional data were gathered for several categories for use in the emission
projections. Growth indicators, other than BEA or SEDS data, were developed for
petroleum refinery fugitives and several nonroad vehicle source categories, including
aircraft (commercial and civil), railroads, and marine vessels (other than gasoline-
powered).
Activity levels for aircraft are measured by the number of landing-takeoff operations
(LTOs). Annual LTO totals are compiled by the Federal Aviation Administration (FAA)
on a regional basis. Commercial aircraft growth was derived from the summation of air
carrier and air taxi regional totals of LTOs from FAA-operated control towers and FAA
traffic control centers (FAA, 199 Ib). These data were compiled on a regional basis, so the
regional trends were applied to each State. Civil aircraft growth indicators were also
developed from regional LTO totals. Civil aircraft activity levels were determined from
terminal area activity for the years 1985 through 1989, and from a 1990 forecast of
Page 26
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Table 11-6
SEOS National Fuel Consumption
Category
1985
1986
1987
1988
1989
1990
Anthracite Coal (thousand short tons)
Commercial 524 494 478 430 422 410
Industrial 575 470 437 434 392 387
Residential 786 740 717 646 633 615
Bituminous Coal (thousand short tons)
Commercial 4,205 4.182 3,717 3,935 3,323 3,470
Industrial 115,854 111,119 111,695 117.729 117,112 118,322
Residential 2,264 2,252 2,002 2,119 1,789 1,869
Distillate Fuel (thousand barrels)
Commercial 107,233 102,246 101,891 98.479 91,891 95,385
Industrial 203,659 206,108 210,699 209,553 197,035 205,856
Residential 171,339 173,736 176,822 182.475 178.629 184,501
Liquefied Petroleum Gases (thousand barrels)
Industrial 437,964 411,451 447,120
453,599 441,784 457,013
Motor Gasoline (thousand barrels)
Transportation 2,433,592 2,507,936 2,570.047 2,627,331 2,617,450 2,703,666
All Sectors 2,493,361 2.567,436 2,630,089 2,685.145 2,674,669 2,760,414
Natural Gas (million cubic feet)
Commercial
Industrial
Residential
Residual Fuel
Commercial
Industrial
2,432
6,867
4,433
(thousand barrel*)
30,956
120.002
2,318
6,502
4,314
39,480
132,249
2,430
7.103
4,315
41.667
107,116
2,670
7.479
4,630
42,256
105,448
2,719
7.887
4,777
35.406
95,646
2,810
8.120
4,805
27.776
118,122
terminal area activity (FAA, 1991a). Military aircraft LTO totals were not available;
consequently, BEA data on military sector economic growth were used.
Railroad data are provided by the Association of American Railroads (AAR). National
totals of revenue-ton-miles for the years 1985 through 1990 were used to estimate changes
in activity during this period. The national growth was applied to each State and county
(AAR, 1991).
Page 27
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Marine vessel activity is recorded annually by the U.S. Army Corp of Engineers
(COE). Cargo tonnage national totals are used to determine growth in diesel- and
residual-fueled vessel use through the year 1989 (COE, 1991). Gasoline-powered vessels
are used predominantly for recreation, so growth for this category is therefore based on
population.
Petroleum refinery fugitive emissions were grown to 1990 based on refinery capacity
by State, as reported in DOE's Petroleum Supply Annuals for 1985 through 1990 (DOE,
199 Ib).
2. Residential Wood Combustion
Emission factors for residential wood combustion (normally inventoried in the "Fuel
Combustion Other" category) were updated to reflect recent improvements in AP-42
emission factors.
Table II-7 lists the NAPAP PM-10 emission factors (which reflect a combination of
wood-burning devices) and the emission factors taken from the latest AP-42 supplement.
Since no data are available to weight these emission factors (based on stove type
population), and because conventional woodstoves constitute the majority of woodstoves
nationwide, the emission factor for conventional wood stoves was used to calculate all
residential wood combustion emissions. This method provides a conservative (high)
emissions estimate because conventional stove emissions are generally higher than other
wood-burning devices. Usage data were taken from the NAPAP emission inventory.
Table 11-7
Summary of Residential Wood Combustion Emission Factors
PM-10 Emission Factors
(Ibs/ton)
1985 NAPAP Emission Factor 39.30"
AP-42 Emission Factors:
Conventional Stoves 30.60
Noncatalytic Stoves 19.60
Pellet Stoves" 4.20
Catalytic Stoves 20.40
Fireplaces 34.60
NOTES: * TSP emission factor
' Pellet stoves comprise less than 2 percent of the national population.
Paee28
-------
3. Highway Vehicles
Highway vehicle emissions were estimated using county/SCC-level VMT estimates
from the 1990 Interim Inventory and emission factors for PM-10, PM-2.5, and NH3
developed for the National Particulates Inventory.
Emission factors for gasoline and diesel vehicles were developed from different
sources. Gasoline emission factors were developed using EPA's Office of Mobile Sources
(OMS) data containing gasoline TSP emission factors by vehicle type and model year
(EPA, 1987). Travel fractions by model year from MOBILES Emission Factor Model were
used to develop a single emission factor for each vehicle type (EPA, 1992a). Diesel
emission factors were developed from other OMS data containing diesel TSP emission
rates by vehicle type and model year, and diesel fraction of VMT by vehicle type and
model year (EPA, 1992d). As with the gasoline factors, a single emission factor for each
vehicle type was developed. PM-10 and PM-2.5 emission factors for gasoline and diesel
were developed using PM-10 and PM-2.5 particle size multipliers from AP-42 and TSP
emission factors. (EPA's PART5 model was not available for use in this inventory.)
Ammonia emission factors were taken from a report developed as part of the 1985
NAPAP Inventory (EPA, 1985). This report contained emission factors for unleaded
gasoline vehicles and diesel vehicles in Ib NHg/1,000 gallons. These emission factors were
converted to vehicle type-specific emission factors in grams/mile by using 1990 fuel
economy data from the MOBILE4.1 Fuel Consumption Model (EPA, 1991b). Table II-8
shows the derivation of the NH3 emission factors.
4. Nonroad Sources
Nonroad sources include motorized vehicles and equipment that are not normally
operated on public roadways. The nonroad mobile source emission estimates in the
National Particulates Inventory are based on 1990 nonroad emission estimates compiled
by EPA's Emission Inventory Branch (ED3) (EPA, 1992b). The EBB nonroad data contains
a total emission estimate for nonroad sources at the county level. These emission
estimates include all nonroad sources except aircraft, commercial marine vessels,
railroads, and fugitive road dust. The nonroad sources not included in the ED3 estimates
were determined by growing the applicable NAPAP source categories. The ED5 nonroad
emission estimates were developed from nonroad emission inventories for 27 ozone
nonattainment areas (NAAs) by EPA's OMS (EPA, 1992c). The OMS inventories
contained 1990 emission estimates at the SCO-level for each county within the 27 NAAs.
These nonroad data do not include emission estimates for
EIB performed a two step process to convert the OMS emission estimates to county/
SCC-level emission estimates from the NAA level. The first step was to use the OMS
1990 nonroad emission estimates for the 27 ozone NAAs to estimate nonroad emissions
for the rest of the country. In the second step, total nonroad emission estimates for each
county were used to create 1990 county/SCC-level nonroad emission estimates. Details of
these steps are described below.
Page 29
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Table 11-8
Calculation of Ammonia Emission Factors for Highway Vehicles
Vehicle
LDGV
LDGT1
LDGT2
HDGV
LDDV
LDDT
HDDV
MC
NOTES:
Type'
LDGV
LDGT1
LDGT2
HDGV
LDDV
LDDT
HDDV
MC
Emission Factor
(lb/1,000gal)
0.63
0.63
0.63
0.63
0.95
0.95
0.95
0.63
Light-duty gasoline-powered
Light-duty gasoline-powered
Light-duty gasoline-powered
1990 Fuel Economy 1990 Emission Factor
(mpg)
20.77
16.06
16.06
9.03
25.43
24.65
6.19
50.00
vehicles
trucks (gross vehicle weight & 6,000 Ib)
trucks (gross vehicle weight z 6,000 Ib and
(g/mi)
0.0138
0.0178
0.0178
0.0316
0.0169
0.0175
0.0696
0.0057
£ 8,500 Ib)
Heavy-duty gasoline-powered vehicles (gross vehicle weight > 8,500 Ib)
Light-duty desel-powered vehicles
Light-duty desel-powered bucks (gross vehicle weight s 6.500 Ib)
Heavy-duty desel-powered vehicles (gross vehicle weight > 8,500 Ib)
Motorcycles
a. Creation of National County-Level 1990 Nonroad Emission Estimates
OMS had prepared 1990 nonroad emission inventories for 27 ozone and 6 carbon
monoxide (CO) NAAs. (Data from the CO NAAs were not used because they did not
include PM-10 emission estimates). Table n-9 lists the 27 ozone NAAs for which nonroad
inventories were compiled. Each NAA inventory contained county-level emission
estimates for 279 equipment/engine type combinations for each county in the NAA. The
following methodology was used to create 1990 nonroad emission estimates for the entire
country:
1. per capita PM-10 emission factors were developed for each NAA by summing
each pollutant's emission estimates for all equipment/engine categories for all
counties within the NAA and dividing by the NAA population;
2. for counties located entirely within a NAA, the emission estimates in the OMS
inventories was used;
3. for counties located partially within a NAA, emission estimates were calculated
by multiplying the NAA per capita emission factor by the total county population;
and
Page 30
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Table 11-9
Oione Konanain, Areas wi.h O^pare* Nonroad Emission
Atlanta. GA
Baltimore, MD
Baton Rouge, LA
Beaumont, TX
Boston, MA
Chicago, IL
Cleveland, OH
Denver, CO
El Paso, TX
Hartford. CT
Houston, TX
Miami, FL
Milwaukee, Wl
Muskegon, Ml
New York, NY
Philadelphia, PA
Phoenix, AZ
Portsmouth, NH
Providence, Rl
San Diego, CA
San Joaquin, CA
Seattle, WA
Sheboygan, Wl
South Coast, CA
Springfield, MA
St. Louis, MO
Washington, DC
Page 31
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4. all other counties were assigned a "surrogate NAA" based on geography and
climate, emission estimates were calculated by multiplying the "surrogate NAA"
per capita emission factors by the total county population. Figure II-1 shows how
the surrogate NAA each area of the country was assigned.
b. Distribution of Total Nonroad Emissions to SCCs
The resulting emission estimates from step 1 above, represent total county nonroad
emissions. The following methodology was used to distribute total nonroad emissions to
the SCC level:
1. an SCC was assigned to each of the 279 equipment/engine type combinations; the
27 SCCs used are listed in Table IMO;
2. for each of the 27 NAA inventories, the percentage of emissions from sources
assigned to each SCC was calculated; and
3. for the remaining counties, total nonroad emissions were distributed to the 27
SCCs using the SCC percentages from its surrogate NAA.
5. Other Combustion
"Other combustion" includes agricultural (field) burning, wildfires, structural fires,
and prescribed (forest and range management) burning.
Emissions for agricultural burning, wildfires, and structural burning were taken from
the 1985 NAPAP inventory. For agricultural burning and wildfires, the NAPAP emissions
were estimated from the number of acres burned in each county and fuel loading
(tons/acre) factors for each crop type. Agricultural burning emissions were grown to a
1990 level using BEA farm income growth statistics; zero growth was assumed for the
wildfires category.
For prescribed burning, PM-10, PM-2.5, SO2, NO,, and VOC emissions were
estimated. The estimates were based on a 1989 USDA Forest Service inventory of
particulate matter and air toxics from prescribed burning (USDA, 1989). The Forest
Service inventory contained State-level totals for PM-10, PM-2.5, non-methane
hydrocarbons (used as a surrogate for VOC), and CO, and several air toxics.
The pollutants in the Forest Service inventory (PM-10, PM-2.5, VOC, and CO) were
distributed to the county-level using the same county-level distribution as was used in the
1985 NAPAP Inventory where forest acreage per county was obtained from local officials
and State land usage maps. Emission estimates for NOZ, and S02 were developed by
assuming the ratio of CO to each pollutant in the Forest Service inventory was the same
as the ratio of CO to pollutant in the 1985 NAPAP inventory. The following formula was
used:
-------
. , Rgure 11-1
Assignment of Surrogate Nonattainment Areas
CO
CO
Springfield
Milwaukee
Milwaukee
Seattle / Milwaukee
Denver
St. LouisNSfTLouis
^-
Atlanta
Houston Bat
\l
otj San Jqaquin
SoutfxCoast
Sarf Diego / Phoenix / _..
L_1_C / Phoenix
o/tsmouth
Boston
*^ Providence
Hartford
'New York
Philadelphia
Baltimore
-------
Table 11-10
Source Categories Used for Nonroad Emission Estimates
sec
Category Description
22-60-001-000
22-60-002-000
22-60-003-000
22-60-004-000
22-60-005-000
22-60-006-000
22-60-007-000
22-60-008-000
22-65-001-000
22-65-002-000
22-65-003-000
22-65-004-000
22-65-005-000
22-65-006-000
22-65-007-000
22-65-008-000
22-70-001-000
22-70-002-000
22-70-003-000
22-70-004-000
22-70-005-000
22-70-006-000
22-70-007-000
22-70-008-000
22-82-005-000
22-82-010-000
22-82-020-000
Recreational Vehicles: Gasoline, 2-Stroke
Construction Equipment: Gasoline, 2-Stroke
Industrial Equipment: Gasoline, 2-Stroke
Lawn & Garden Equipment: Gasoline, 2-Stroke
Farm Equipment: Gasoline, 2-Stroke
Light Commercial: Gasoline, 2-Stroke
Logging Equipment: Gasoline, 2-Stroke
Airport Service Equipment: Gasoline, 2-Stroke
Recreational Vehicles: Gasoline, 4-Stroke
Construction Equipment: Gasoline, 4-Stroke
Industrial Equipment: Gasoline, 4-Stroke
Lawn & Garden Equipment: Gasoline, 4-Stroke
Farm Equipment: Gasoline, 4-Stroke
Light Commercial: Gasoline, 4-Stroke
Logging Equipment: Gasoline, 4-Stroke
Airport Service Equipment: Gasoline, 4-Stroke
Recreational Vehicles: Diesel
Construction Equipment: Diesel
Industrial Equipment: Diesel
Lawn & Garden Equipment: Diesel
Farm Equipment: Diesel
Light Commercial: Diesel
Logging Equipment: Diesel
Airport Service Equipment: Diesel
Recreational Marine Vessels: Gasoline, 2-Stroke
Recreational Marine Vessels: Gasoline, 4-Stroke
Recreational Marine Vessels: Diesel
Page 34
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NAPAP,
POL
NAPAPCO
X
Where:
FSpOL = Prescribed burning (N0t, or SO2) emissions from Forest
Service
FSCO = Prescribed burning CO emissions from Forest Service
NAPAPpoL = Prescribed burning (NO,, or SO2) emissions from NAPAP
NAPAPCO = Prescribed burning CO emissions from NAPAP
E. BIOGENIC EMISSIONS
Estimates for biogenic VOC emissions were taken from a report by Lamb et al (1993).
In this study, the authors prepared a national biogenic emissions inventory for eight
landcover types: oak forests, other deciduous forests, coniferous forests, grasslands,
scrublands, urban vegetation, agricultural crops, and inland waters. A forest canopy
model was used to account for the effects of solar radiation, temperature, humidity, and
wind speed on predicted VOC emission rates. Estimates for isoprene, terpenes, and other
hydrocarbons were given. The 1990 biogenic emissions presented here assume an
emission factor of zero for corn crops. This assumption is based on the results of recent
field studies that have shown that previous emission factors for corn have been overstated
by roughly a factor of 1,000.
F. METHODS FOR ASSESSING SECONDARY ORGANIC AEROSOL FORMATION
The methodology used to estimate SOA emissions is presented next. Techniques for
estimating SOA emissions from both anthropogenic and biogenic sources of VOC are
presented.
Methods for assessing SOA formation draw heavily from the work of Grosjean and
Seinfeld (1989). In that study, the researchers assigned fractional aerosol coefficients
(FACs) to a wide variety of organic species to express the fraction of emissions that may
form SOA. FACs are based on the reactivity of an organic compound with atmospheric
oxidants and the vapor pressure of the resulting products. The FAC is expressed as a
dimensionless fraction that can be multiplied by the total mass of the organic compound
released, resulting in a mass of secondary aerosol formed.
In general, short-chain (low molecular weight) species, especially those with fewer
than six carbon atoms, are assumed to have little or no SOA formation potential. The
order of reactivity and SOA formation potential based on chemical structure is as follows:
cyclic olefLns> olefins> cyclic paraffins> aromatics> paraffins> oxygenated aliphatics.
Some miscellaneous species have much higher SOA formation potentials than the cyclic
olefins. Notable among these are terpene species, such as pinenes and terpinenes
(Grosjean and Seinfeld, 1989). For the purposes of this study, halogenated organics and
species containing nitrogen and sulfur atoms are assumed to have similarly low formation
potentials as the oxygenated aliphatics.
Page 35
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In order to determine SOA formation potentials for a source or group of sources, the
general approach was to obtain a speciation profile that listed each organic compound and
weight fraction. Next, the weight fraction of each compound was multiplied by the
appropriate FAC and the resulting individual formation potentials were summed to
provide a source-specific FAC. For use with VOC inventories that contain estimates of
non-methane organic compounds, the source-specific FACs are adjusted to account for the
presence of methane, where applicable. Table 11-11 provides an example of how a source-
specific FAC is determined.
In determining source-specific FACs, an effort was made to provide conservative
estimates of formation potential that would result in conservative (high) estimates of SOA
formation. For example, a major simplifying assumption made for this assessment was
that 100 percent of all photochemically-reactive species released would eventually react.
This assumption may lead to overstated air quality modeling impacts (i.e., create higher
ground-level SOA concentrations) in areas close to the emission sources of organic species
that have long reaction times. Grosjean (1992) made adjustments to account for this in a
study of Los Angeles basin SOA formation. A reaction time of 6 hours was used to
determine that, depending on the specific compound, 10 to 20 percent of the alkane
species, 50 to 60 percent of the alkene species, 10 to 70 percent of the aromatic species,
and 100 percent of the terpene species would react. Therefore, depending on the
meteorology and location of the modeled receptor in relation to the source, the SOA
formation rates determined here have the potential to be overstated by a factor of 2 to 10.
However, since the source-specific FACs are driven to a large extent by the most reactive
species (e.g., olefins, aromatics), SOA formation is probably overstated by less than a
factor of two to five for most source categories.
After determining source-specific FACs for all of the VOC source categories, SOA
estimates were prepared by multiplying the source-specific FAC (adjusted for methane, if
necessary) by the annual VOC emissions for that source category. The following
paragraphs describe data sources and assumptions used in creating SOA formation
potentials for anthropogenic and biogenic sources, respectively.
1. Anthropogenic VOC Sources
Speciation profiles for anthropogenic sources are taken primarily from EPA's
SPECIATE database (version 1.5; Radian, 1992). For several source categories, speciation
profiles from the California Air Resources Board (CARS) VOC speciation manual were felt
to be either more comprehensive or more representative for a certain source category and
were used instead of tile EPA profiles (CARS, 1991). An effort was made to screen out, if
possible, profiles that might represent a VOC composition that could have been altered by
control equipment (e.g., wet scrubbers).
Where applicable, profiles that do not provide a comprehensive speciation of the
expected constituents were substituted with alternate profiles. An example of this
approach is coal combustion. Three profiles are used within the SPECIATE system and
are generally assigned to electricity generation, industrial boilers, and coal slurry-fired
PfllTA 3fi
-------
Table 11-11
Sample Determination of a Source-Specific FAC: Asphaltic Concrete
VOC Species
isomers of dodecane
isomers of tetradecane
Cj-substituled cyclohexane
Cg-substituted cyclohexane
n-decane
n-undecane
n-dodecane
C4-substrtuted cyclohexanone
trimethylbenzene
2,2-dichloronitroaniline
naphthalene
methyl naphthalenes
C2alkyl indan
n-pentylcyclohexane
trimethyldecene
Total (source-specific FAC)
Weight Fraction
0.0959
0.0312
0.0416
0.0312
0.0287
0.0778
0.1856
0.0238
0.0889
0.0394
0.0654
0.1019
0.1121
0.0204
0.0562
Species FAC
0.0300
0.0400
0.0500
0.0600
0.0200
0.0250
0.0300
0.0017
0.0260
0
0.0400
0.0500
0.0250
0.0500
0.1500
SOA Formation
Potential
0.0029
0.0013
0.0021
0.0019
0.0006
0.0020
0.0056
0.0000
0.0023
0
0.0026
0.0051
0.0028
0.0010
0.0084
0.0386 (3.86%)
boilers (Radian, 1992). The profile for electricity generation provides a comprehensive
speciation of the expected compounds, however the profile for coal slurry-fired boilers lists
the heavier compounds (C-,-Cl6 paraffins) as a group. The profile for industrial boilers
provides a comprehensive listing of expected compounds, but was obtained from a source
controlled by a wet scrubber that could have changed the composition of the exhaust
stream. Therefore, the profile for electricity generation was selected to represent all of
the coal combustion sources.
Considerable uncertainty exists in the quality of the source-specific FACs determined
from EPA's and CARB's speciation profiles. Profiles for most of the combustion categories
were based on one survey conducted in 1978 (Radian, 1992). Testing and analytical
methods have been much improved since that time and the potential for bias from the use
of only one set of results from one facility is extremely high. Probably the weakest data
Page 37
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contained in the SPECIATE system are for the chemical manufacturing industry. Many
of the source categories are represented by a single profile that lists an overall industry
average. In addition, for solvent use categories, profiles were developed in 1987 and do
not reflect recent changes in solvent usage trends.
Additional uncertainty accompanies the assignment of many source categories to a
limited number of profiles in the SPECIATE system. For stationary internal combustion
sources using distillate oil, this problem was compounded by the fact that the profile for
reciprocating engines was deemed unsatisfactory for use (many of the expected heavier
compounds were absent). Therefore, a single profile had to be used for all turbine and
reciprocating diesel engines.
Table 11-12 provides a listing of several source-specific FACs in order to show the
range of values determined for anthropogenic sources and some of the typical stationary
and area source FAC assignments.
2. Biogenic Sources
Estimates of SOA formation are based primarily on speciation data provided by Lamb
et al., (1993) and the methods described above to determine source-specific FACs. For
biogenic sources, Lamb et al., provided data to speciate the emissions for eight landcover
types into terpene, olefin, paraffin, and aromatic fractions. The terpene fraction was
assigned an SOA formation potential of 30 percent based on the FACs for alpha- and beta-
pinene and alpha- and gamma-terpinene (Grosjean and Seinfeld, 1989). A CARB
speciation profile for Kern County, California cropland (the only comprehensive biogenic
speciation obtained) was used to determine the types of compounds likely to be present in
the remaining fractions (CARB, 1991). According to this profile, the olefin and paraffin
fractions contain only short-chain hydrocarbons with no SOA formation potential. The
aromatic fraction of the CARB crop profile consisted of C7 - C9 aromatics that would
typically yield a formation potential of about two percent (Grosjean and Seinfeld, 1989).
Table 11-13 presents the data used to determine source-specific FACs for the eight
landcover types presented by Lamb et al. The high terpene weight fractions lead to
significant SOA formation potentials. As a comparison, the CARB speciation for crops
suggests terpene fractions of about 14 percent compared to the agricultural land cover
terpene fraction of 26 percent provided by Lamb et al., (1993).
G. CANADA AND MEXICO EMISSIONS
In order to provide a complete emissions inventory for modeling purposes and to
account for the amount of particulate matter transported over the border from Mexico and
Canada into the U.S., it is necessary to determine the amount of primary and secondary
particulate matter emissions emanating from Mexico and Canada.
3ft
-------
Table 11-12
Sample Source-Specific FACs
Source Category
Sample SCC Assignments Source-Specific FAC
External Combustion - Coal
External Combustion Distillate Oil
External Combustion - Natural Gas
Internal Combustion - Distillate Oil
Internal Combustion Natural Gas
Chemical Manufacturing Average
Textile Products - Average
Surface Coating Operations - Average
Pulp and Paper Industry - Average
Degreasing - Mineral Spirits
Gasoline - Summer Blend
Light-Duty Gasoline Vehicles
(exhaust)
Heavy-Duty Diesel Vehicles
21-04-001-000; 21-03-001-000;
1-01-002-02; 1-05-001-02;
3-05-003-16
21-04-004-000; 1-01-005-01;
3-01-900-01; 4-02-001-02
21-03-006-000; 1-01-006-01;
3-01-900-03
22-75-020-000; 2-01-001-01;
2-02-001-02
2-01 -002-01; 2-01 -007-02;
2-03-001-01
3-01-003-05; 3-01-060-01;
3-01-176-01; 3-01-888-01
3-20-999-97; 3-30-888-01
3-01-005-01; 3-08-007-03;
4-02-009-98
3-07-002-99; 3-07-008-96;
3-07-020-98; 3-07-999-98
4-01-002-01; 4-01-002-21
4-03-002-01; 4-04-001-03;
4-04-004-04; 4-06-003-07
22-01-001-000; 22-01-040-000;
22-01-020-000
22-30-070-000
0.0199
0.0236
0.0003
0.0031
0.0001
0.0019
0.2192
0.0074
0.3000
0.0164
0.0026
0.0056
0.0232
Page 39
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Table 11-13
Source-Specific FACs by Landcover Type
Landcover
Oak Forest
Other Deciduous
Coniferous Forest
Scrub Land
Grass Land
Agricultural
Inland Water
Urban
Weight Fraction
Terpene
0.149
0.262
0.607
0.374
0.282
0.261
0.571
0.385
Olefin
0.023
0.028
0.059
0.021
0.017
0.055
0.026
0.015
Paraffin
0.135
0.167
0.176
0.216
0.174
0.354
0.167
0.156
Aromatic
0.121
0.149
0.086
0.189
0.153
0.299
0.141
0.137
Source-
Specific
FAC*
0.047
0.082
0.184
0.116
0.088
0.084
0.174
0.118
NOTE: Assumes an overall FAC for terpenes of 30%, 0% for ofefins and paraffins, and 2% for aromatics.
1. 1990 Emission Estimates for Canada
Emissions for Canada were developed by growing 1985 NAPAP emissions to 1990
using emission growth factors. The 1985 NAPAP Inventory contained Canadian TSP
emissions for point and area sources. The point source emissions are at the point level
and are accounted for by (U.S.) SCC. The area source emissions are at the province-level
and use Canadian SCCs. These Canadian area source SCCs were converted to the U.S.
area source SCCs so that the SCC-specific particle size multipliers could be applied.
The growth factors used to grow the 1985 NAPAP emissions to 1990 were provided by
Environment Canada (Stephanson, 1994). 1985 and 1990 particulate emissions by source
category were provided for each province. The percentage change from 1985 to 1990 was
used as the growth factor. Growth was assumed to be zero for any category with no data
Each SCC in the point and area source file was assigned to one of the source categories
for which there was Canadian growth. The same particle size multipliers used for U.S.
emissions were used to estimate Canadian PM-10 and PM-2.5 emissions.
2. 1990 Emission Estimates for Mexico
It was assumed that only emissions emanating from the six Mexican States that
border the U.S. immediately to the South were of concern relative to transported
particulate matter. The six Mexican States considered in this analysis are Baja California
Norte, Coahuila, Chihuahua, Nuevo Leon, Sonora, and Tamaulias.
Page 40
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a. The World Bank Report
Emissions estimates of S02, NOI( hydrocarbons, and TSP for Mexico are contained in
a draft report released as part of a joint project carried out in 1990 to 1991 by the World
Bank, United Nations Statistical Office, and the Mexican "Institute Nacional de
Estadistica, Geografia e Informatica" (INEGI), with partial funding from United Nations
Development Program (World Bank, 1992). This report, referred to hereafter as the
World Bank report, also includes CO and lead emissions. The World Bank report includes
emissions estimates for five sectors (i.e., motor vehicles, thermoelectricals, manufacturing,
services, and oil refining), and does not include emissions for residential fuel combustion
and fugitive dust. It is assumed that fugitive dust will not contribute significantly to the
particulate matter that is transported, due to its (assumed) large diameter size relative to
other particulate matter sources. The absence of residential fuel combustion emissions
does, however, mean that any analysis based on emissions from the World Bank Report
underestimates the PM-10 and PM-2.5 emissions emanating from the Mexican border
States, especially during the winter months when residential heating occurs with greater
intensity. Unfortunately, at this time data are not available to estimate Mexico-generated
residential fuel combustion particulate emissions.
In the World Bank report, emissions were based on calculated "contamination
coefficients" that related the emissions in the Federal District (i.e., Mexico City) to the
number of motor vehicles, and output from oil refining, electricity plants and other
manufacturing industries in the Federal District. A generalized example of a
contamination coefficient is the ratio of the amount of pollutant in the Federal District
generated by motor vehicles to the number of vehicles in the Federal District. The
coefficient is multiplied by the number of vehicles in a given State to estimate the
emissions in that State.
For the transportation sector, a coefficient for the Federal District was calculated for
each pollutant and for each type of vehicle (i.e., car, truck, bus) in circulation. The
coefficients were applied to the number of vehicles in circulation within each State during
1985 to determine state-level transportation sector emissions for 1985, by vehicle type.
For the thermoelectricals sector, a coefficient for the Federal District was calculated
for each pollutant by determining the ratio of air pollutant to the generating capacity (in
gigawatts/hour), by type of plant (e.g., oil, coal, etc.). The coefficients were applied to the
generating capacity within each State during 1985 to determine state-level
thermoelectrical emissions for 1985.
For the manufacturing and services sectors, a coefficient for the Federal District was
calculated for each pollutant by determining the ratio of air pollutant to the total number
of establishments. The coefficients were applied to the number of establishments within
each State during 1985 to determine state-level manufacturing and services emissions for
1985. The manufacturing sector includes industries such as chemicals, iron and steel
smelting, rubber, paper, food, glass, plastics, metal works, asphalt, oils and greases, and
cement. Services refers to miscellaneous industries that use combustion processes (using
different fuels) such as public bathing services, bakeries, hotels, sports clubs, and
hospitals.
Page 41
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No air emissions generated by oil refineries were reported in the World Bank report
for the Mexican border states.
b. Estimating 1990 Emissions
In order to estimate air emissions emanating from the Mexican border States for
1990, the emissions documented in the World Bank report were used, and additional
analysis was done to determine growth in emissions between 1985 and 1990.
First, population data were obtained that provided totals by State, and by major city
(Webster's, 1984; Europa, 1993). Population growth, in percent, was determined for each
State for the 10-year period 1980 to 1990; percent growth for the 5-year period 1985 to
1990 was extrapolated. Transportation-sector emissions were then grown to 1990 levels
based on the percent growth in population. Population by city was also determined in
this step for use later when disaggregating state-level emissions to the city level.
Secondly, population data were also used to grow 1985 thermoelectrical, sources, and
manufacturing sectors' emissions based on the following analysis. Data showing trends in
fuel consumption per capita and gross national product (GNP) per capita from 1970 to
1990 were obtained (World Resources, 1992). It was felt that an increase (or decrease) in
fuel consumption from 1985 to 1990 might be a better indication of change in
thermoelectrial and services sector emissions than population change. Also, it was felt
that any change in GNP from 1985 to 1990 could be an indicator of growth (or decline) in
manufacturing sector emissions. It was determined, however, that there was virtually no
change in energy consumption per capita between 1985 and 1990. GNP per capita
showed a slight downward trend from 1985 to 1990, and was assumed to be zero in order
to provide a conservative (high) estimate of emissions. Since these economic indicators
were reported on a per capita basis, and it was determined from this analysis that there
was no growth in the indicators for the period 1985 to 1990, this was interpreted to mean
that any growth in population would represent a corresponding growth in emissions in the
thermoelectrical, services, and manufacturing sectors. Thus, these sectors' emissions were
grown to 1990 levels based on population growth.
c. Determining PM-10 and PM-2.5 Emissions
After the 1990 TSP emissions were determined, the PM-10 and PM-2.5 emissions
were calculated. This was accomplished using AP-42 particle size distributions (EPA,
199la). For the thermoelectrical and service sectors, the category 2 (combustion/mixed
fuels) particle size distribution was used; for the manufacturing sector, the category 9
(condensation, hydration, absorption, prilling and distillation/all materials) particle size
distribution was used; for the transportation sector, leaded and unleaded fuel particle size
distributions were used.
d. Determining Secondary Organic Aerosol Emissions
After the 1990 hydrocarbon emissions were determined, SOA emissions were
calculated based on the total hydrocarbon emissions. Source-specific FACs were assigned
to the emissions sectors using some simplifying assumptions. First, source-specific FACs
were assigned by vehicle type for the transportation sector by assuming 50 percent of all
Page 42
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trucks use gasoline, and 50 percent of all trucks and 100 percent of all buses use diesel
fuel. A source-specific FAC was developed for the manufacturing sector based on the
assumption that 95 percent of hydrocarbons emanate from solvents and 5 percent are
generated from combustion. The source-specific FAC was derived for the services sector
based on the assumption that all combustion is performed with residual #6 and distillate
#2 oils. An average source-specific FAC for the thermoelectrials sector was developed
based on the assumption of 50 percent coal and 50 percent #2/#6 oils combustion by the
average facility.
e. Compilation of Emissions
The final step in determining emissions for the Mexican border States was to sum the
emissions from all sectors for each State and then disaggregate the state-level totals to
the city/rural area level based on population.
H. DEVELOPMENT OF AMBIENT MODELING INVENTORY
Additional processing of the annual emission estimates was necessary to prepare the
emission estimates for use in ambient modeling. A second set of inventory files was
developed for use as input to ambient modeling. This ambient modeling inventory
contains the annual emission estimates for each pollutant temporally allocated to the four
seasons. In addition, emission estimates were aggregated into three summary files (one
area and two point source) and one point source detail file. All Canada emissions were
aggregated into a province-level summary file. As explained earlier, all Mexico emissions
were summarized by Mexico/LJ.S. border State.
1. Temporal Allocation
For U.S. point sources, emissions were temporally allocated to the four seasons using
the seasonal percent throughput variables on each point source record. State-SCC default
temporal allocation factors from the 1985 NAPAP Inventory were used for any point
source records for which seasonal throughput data were unavailable. Area source
emissions were temporally allocated using the State-SCC default temporal allocation
factors from the 1985 NAPAP Inventory (EPA, 1990). The resulting inventory files
contained annna^ winter, spring, summer, and fall emission estimates for each pollutant.
Temporal allocation data was not available for the Mexico and Canada emissions.
Thus, Mexico and Canada emissions were temporally allocated evenly (25 percent) across
the four seasons.
2. Aggregation of Emissions
To reduce the number of sources to be input into the ambient modeling routines, the
temporally allocated U.S. emissions were aggregated into three summary files and one
point source detail file. The first summary file contained all area source emissions
aggregated to the county-level. The second summary file contained emissions from point
sources with a plume height between 0 and 249.9 meters aggregated to the county-level.
The third summary file contained emissions from point sources with a plume height of
Page 43
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between 250 and 499.9 meters, aggregated to the county-level. The point source detail
files contained all the detail information for points with plume heights of 500 meters or
greater. There were 573 points in the inventory with plume heights of 500 meters or
greater.
I. QUALITY OF EMISSIONS ESTIMATES AND RECOMMENDATIONS FOR
IMPROVEMENT
The quality of any emissions inventory is dependent on the quality, availability, and
completeness of the data on which the estimates are based (e.g., product throughput,
VMT, population, etc.) and the methods used to perform the estimates (e.g., projecting
past years emissions to the future, use of emission factors, use of algorithms, etc.). The
following paragraphs describe the relative quality of the National Particulates Inventory
estimates by qualitatively comparing the data quality/availability/completeness and
methodologies used to estimate emissions of various source categories and pollutants.
1. Relative Quality
The most accurate emissions estimates are those based on current (1990) data and on
data that have been compiled at the county level. Estimates made by "growing" older
estimates (e.g., 1985 NAPAP emissions estimates) to a 1990 level based on population,
land use or other statistics represent areas where further study could improve the
estimates. Also, emissions estimated using "bottom up" methodologies will generally have
higher quality than estimates prepared by growing older estimates.
In the National Particulates Inventory, several source categories can be assessed as
having higher relative quality than the aggregate of all other categories due to use of
more current or complete data and/or methods. These categories include the following:
1. Utilities SO2, NO, and VOC emissions were based on DOE Form EIA-767 and
emission factors from AP-42. The Form EIA-767 data included the most recent
(updated) data available from the utilities. Documented or assumed control
efficiencies were also used For PM-10 and PM-2.5, the controlled emissions were
estimated using control efficiencies calculated from the TSP control efficiencies.
Due to the lack of documented PM-10/PM-2.5 control efficiencies these estimates
are probably less reliable than the SO2, NOS, and VOC estimates for utilities.
2. Motor vehicles all motor vehicle emissions were estimated using county-level
VMT data.
3. Nonroad engines and equipment all emissions (except SO2) were estimated
from OMS's 1990 inventory (EPA, 1992b).
4. Fugitive dust particulate emissions (PM-10 and PM-2.5) estimated for
agricultural tilling, construction activities, paved and unpaved road resuspension,
and cattle feed lot activity were based on state- or county-level data for 1990 and
were estimated using equations or emission factors specific to the source
category. These estimates were prepared using more current data than estimates
for other types of area sources (e.g., residential fuel combustion which was
Page 44
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estimated using activity data from prior years and growing the activity data to
account for an increase in emissions).
Emissions estimates for the following five categories were based on older or limited
data. The categories represent areas where further study might improve emissions
estimates depending on the availability of emissions data:
1. Non-utility point sources all emissions for this group of sources were
determined for 1990 based on 1985 NAPAP estimates. In instances where the
PM calculator was used to determine controlled PM-10 emissions (see page 15),
the quality of the resulting estimates is affected.
2. Miscellaneous fuel combustion all emissions for this general category (that
includes the small combustion sources not included in the point source categories)
were determined for 1990 based on 1985 NAPAP estimates.
3. Fuel combustion, residential wood participate emissions for residential wood
combustion were based on new AP-42 emission factors, however, activity data
(i.e., stove population and tons of wood burned) was determined from the 1985
NAPAP inventory and grown to 1990 levels.
4. Wind erosion particulate emissions for this category include wind erosion from
agricultural land only due to lack of data needed for other land types (e.g., desert,
pasture, grasslands, etc.); thus, these emissions provide an underestimate of total
wind erosion.
5. Agricultural burning agricultural burning emissions were estimated from the
1985 NAPAP inventory.
Other general quality assessments can be made related to total pollutant emissions.
For instance, SOA emissions were estimated based on limited availability of speciation
profiles, and the accuracy of all estimates of SOA emissions is probably low compared to
the accuracy of all S02 and NO, emissions. Also, NH3 emissions estimates were subject to
a lack of emission factors and other data need to calculate these emissions, especially for
point sources. Finally, estimates of PM-2.5 emissions may be less accurate than PM-10
estimates due to the required use of generalized particle-size distribution data for many
source categories.
2. Recommendations for Improvement
Based on the research and analysis conducted during the compilation of the National
Particulates Inventory, and based on evaluation of the factors used above to evaluate the
relative quality of some source groups, some recommendations for improving future
emissions inventory efforts of this type can be made. These recommendations fall into
three general categories, depending on the potential level of that would be needed to
accomplish the tasks and/or the probable impact of the enhancements on the emissions
inventory.
Page 45
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First, certain enhancements provide opportunities to make expedient improvements to
the emissions estimates, depending on availability of data. If new or updated data were
readily available, the following tasks could be accomplished:
Update the non-utility point source estimated by using available State data;
Update paved road resuspension estimates using a new emission factors) due out
with the next release of AP-42;
Enhance the wind erosion estimates by obtaining the necessary data to estimate
State totals for other, non-agricultural land types (county-level data could also be
used, but this would represent a deviation from methods currently used to
compile the Trends Inventory);
Second, certain enhancements would require more effort to accomplish due to limited
availability of existing data and the need to do research to locate more data, or perform
analysis to develop the data. An example of the need to perform analysis would be where
source testing may be needed in order to obtain data to develop new emission factors (e.g.,
ammonia). Areas of emissions inventory improvement requiring more effort than those
listed above include the following:
Improve ammonia emissions by developing new emission factors for categories
not currently covered; a literature search to determine sources of available data
for estimating ammonia emissions from categories not currently inventoried
would be the starting point;
Expand and/or enhance the speciation profiles needed to estimate SOA emissions
from VOC emissions; and,
Enhance the PM-10 calculator to include data for TSP and PM-2.5 (this should
eliminate the issue of calculated PM-10 emissions being greater than TSP
emissions).
Third, even though particle size distribution data exist to derive PM-2.5 emissions
from PM-10 emissions for area sources, enhancements to the PM-2.5 emission factors,
especially for the fugitive dust category would be advisable, as the area source fugitive
dust category dominates the total PM-2.5 (and PM-10) emissions inventory. Thus the
following recommendation is made to enhance the estimates of this most significant
category:
Develop source specific PM-2.5 particle size distribution data or emission factors
for area sources of fugitive dust.
Page 46
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CHAPTER III
RESULTS AND DISCUSSION
This chapter contains the results of the National Participates Inventory for the base
year of 1990. U.S. emissions are presented first, followed by Canada and Mexico
emissions. The results of the U.S. emissions are given by pollutant and aggregated to the
EPA-region level. All totals are shown in tons. Figure III-l shows a map of the EPA
regions.
A. 1990 U.S. EMISSIONS
Tables III-l through III-5 provide results of the emission inventory for each pollutant
(or set of pollutants), PM-10 and PM-2.5, SO2, NO,, and NH3, VOC and SOA, respectively.
1. 1990 PM-10 and PM-2.5 Emissions
Table III-l shows the results of the national PM-10 and PM-2.5 emissions inventory.
Results are shown for source categories by EPA region, and by national total.
Figure III-2 shows total PM-10 emissions by major source category, as well as the
relative contribution of sources composing the largest (in terms of percent of total PM-10
emissions) source category. This figure shows that fugitive dust is the largest source of
PM-10 emissions on a national basis (68 percent), followed by agriculture and forestry
(i.e., agricultural tilling and livestock feed lot emissions) (15 percent), and natural sources
(wind erosion of agricultural land) (9 percent). Most of the emissions from fugitive dust
are due to unpaved roads, construction activities, and paved roads.
Figure III-3 shows total PM-2.5 emissions by major source category, as well aa the
relative contribution of sources composing the largest (in terms of percent of total PM-2.5
emissions) source category. This figure shows that fugitive dust is the largest source of
PM-2.5 emissions on a national basis (56 percent), followed by agriculture (19 percent),
fuel combustion (10 percent), and natural sources (9 percent). As was the case with PM-
10, fugitive dust is the largest source; however, witkUi the fugitive dust category paved
road resuspension is the largest source of fugitive dust PM-2.5 emissions, whereas
unpaved roads were the largest source of fugitive dust PM-10 emissions. Also, fuel
combustion contributes 10 percent of the total PM-2.5 emissions, and only 5 percent of the
total PM-10 emissions.
Figure III-4 shows a graphic summary of PM-10 emissions by EPA region. On a
regional basis, the single largest source of PM-10 is fugitive dust. Region VI has the
highest fugitive dust PM-10 emissions (approximately 8.6 million tons per year). In
regions I, II, VIII, and X fugitive dust PM-10 emissions are estimated less than 2 million
tons per year. In five regions, agriculture is the second largest source of PM-10. In
Page 47
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Figure 111-1
Map of EPA Regions
-------
'l
Table IIM
United States 1990 PM-10 and PM-2.5 Emissions by EPA Region
(tons)
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600 502
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1.759 1.591
1,988 1,616
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11,754 7,974
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29.312 25,991
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56.416 U.019
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61636 6.665
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31.140 20.230
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1.985 1.961
64,855 51.047
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670 344
1.023 (78
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FUEL COMB, OTHER
CanmrcWIraffiullomlOl 2.194 1,101
CanMnWIdlUlDTnl Ota 155 146
Hte.Fu.1 Comb. (Eic.pl RM««*M) 435 396
RrtHMWMod 59.829 58.625
HnamUMr 2.120 992
TcM" 14.863 ' H.4IJ
1,490 MO
4.079 2.056
844 617
21 18
71221 71221
2.619 1,544
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2.506 1,192
USe 898
355 393
102 86
121.010 121.010
4.337 2,111
IM.885 155.431
1.037 610
2.781 I.OW
401 377
489 464
186,257 1B8JS7
1.716 1.085
"mm T5I.M3
W12 1^70
730 347
U57 1,190
164 149
199.315 199.315
3.648 2.651
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98 41
582 217
612 583
484 434
48.527 48.527
807 717
45.IM tf.519
1^12 531
83 33
364 347
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40.845 40.645
798 806
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249 103
281 250
288 210
27.497 27.07
702 414
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5 2
910 229
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145 132
40.179 40.179
889 796
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295 180
222 203
629 599
8S.199 85.196
383 194
M.9M U.IK
10.1S UXU
12.842 6.031
4,761 4433
2.766 2.489
680.673 680.873
18.375 11J03
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31,358 27,493
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ier «
g g
0 0
549 195
110 99
0 C
653 HI
1 r
0 0
399 128
19 9
0 0
BJ IHT
0 0
449 ISO
0 0
0 0
- 4!4 "IB
1 (
0 0
22 8
0 0
0 I
49 IB
0 0
7 3
0 0
0 I
7 3
0 0
9 12
0 0
0 0
S 15
0 0
0 0
0 0
0 0
0 0
3 1
1.910 917
199 49
0 0
Page 49
(CantmMd)
-------
Table IIH (continued)
Rjgtonl
Sonet Cifcgoir (~~p5lO P9HJ
STORME t TRANSPORT
UTwnrablPlanb B B
PitrahunlPitralMmPniludStaioi 0 0
Pttrc4«m«P«rollum Product Trarapcit 32 11
SmtnStflonxSUgil 0 0
5infe>a«kira:SUo«n 0 0
S4nk»S«lom:Bn«»*gt\ &nftlng 0 0
Or5»fcCt»T*«l Scrag.
Ovyvric Cfwiltil T'flnvpofl
kttiQiinlc Chwfcsl StoraQ*)
hoTQWiii Chtflilcri Tf Bisport
8t*UMilkSlcng> 3 1
IMMdn*) Import
Rtfta)
PM-IO p)Mj
II i
2 1
0 0
0 0
0 0
0 0
60 21
10 4
61 23
RfgtaiN
PIUO PIUS
0 0
0 0
0 0
0 0
0 0
0 0
9 3
2 1
10.703 3.9*2
R*gtaiR>
PHO PIUS
0 0
7 2
0 0
0 0
0 0
0 0
109 40
0 0
S3 20
4.493 1496
RigtonV
PIUO PM-2.B
B " B
6 2
0 0
0 0
0 0
0 0
157 105
256 tOO
0 0
21.481 7418
37 3
51.515 I.IM
R*|taiVI
PH-IO p»u
B B
7 2
4 1
0 0
0 0
0 0
454 348
48 17
107 37
0 0
8.567 2409
RtgtanVJ
P*HO P»U
B B
0 0
0 0
0 0
0 0
0 0
26 10
0 0
747) 2483
41 3
Motet *
P*M> P»M4
B 0
0 0
0 0
0 0
0 0
846 214
*4*JmK
M-l) PK-U
B 0
36 13
0 0
0 0
0 0
IJ342 411
\}19 439
*»*1<»X
PIM* PIUI
... j .... ,-j
0 0
18 6
0 0
0 0
24 IS
531 130
SB ISI
IMmriToM)
PH-ie PH-U
_ j.
59 21
54 11
0 0
0 0
0 0
834 93)
46 17
430 181
0 0
62.175 1*489
78 )
54.4JM IMjUJ
wwrc Dwttu. a RECTCUM
tMJwdkjii 2,810 IJBcD
OpvBwIng 9.118 428
MumWontMB
TSOF 0 0
LmM
am
ToU 1 1,738 1041 IB
iflff 3^91
12.187 11.079
0 0
17,00* 14,470
Z.OS 1,5/0
2.L247 20,225
0 0
0 0
24,290 21,795
3495 2,818
49.143 44,975
0 0
53,030 47,493
'WH v,4AO
41.253 37,503
0 0
12 4
54,937 4C.9B7
3,129 2,209
20.147 11.330
10 4
0 0
0 0
107 31
23,394 20,560
5,727 4,306
14.131 12.948
0 0
70 25
19,928 17,190
2,195 1.512
8.504 8413
0 0
8,889 7,42!
5,756 5499
9433 8453
0 0
139 48
18,724 14.999
2.4331 IJTS
7.572 8393
0 0
10.008 0,16!
49^U SI.7IB
192,136 174.707
10 4
0 0
136 46
18) 67
241.W 4W.B11
HBHW»YWHCt£8
ugni-oulyaasvililcMsaMclorcyOM 1,744 1,4951 2.72B 2432
Ugrt-OUyQ»Trudo 974 477J B7S 727
M«««y-Oulr0«lVrt*to 162 134 289 196
MM 6.786 9L085J 12.585 11.559
Te(4l 11^67 tO,190| 18^426 14,814
3482 2.899
1,155 960
369 284
19443 17,796
24.266 21.938
6.7SO 5.786
2428 1433
79) 880
39,749 38.569
49,611 44,867
6.290 1492
2.132 1.772
708 520
35,122 3U12
44^50 3P.99B
4^42 3,637
1.436 1.195
476 351
23,712 21,816
29.0W 26,998
1,740 1,492
814 810
215 156
11,097 10.161
13,636 12,342
362 318
134 99
6473 6423
8,473 7,666
5,004 42691 1^94 1.109
1404 1433J 445 370
496 KM ISO III
22.840 21,0131 7479 84H
29,933 MMK\ 9,466 6,562
34^49 29,367
11,543 t.see
3.789 2.76)
1)7.837 172,627
Tjtjtv 214.JKI
NOMXMOEMU6B
NOfMKMdOMOIna 1.667 1.414
NnvltadniMl 5^66 5,507
Men* 873 618
IhrtvVMNk 26f 239
HBMi 8I« 608
TOW 9.BV 6,576
2,172 1.621
13461 12.771
U50 962
1.308 851
1^09 1^04
19,921 17,529
3^48 2,723
18.760 17^78
2.509 1.787
2J91 1.735
3.402 3^03
X.397 26.7OE
10,362 8.701
38.948 33,991
14.941 10.537
3^10 3J87
7^98 81605
79,5/1 AS,3m
7.540 W26
30j5> 27,838
2.123 1.497
3.886 1^52
6J42 7J875
51,931 45,288
6^64 7.006
41.109 37.620
9.499 6,894
7,700 5.882
8.400 7.726
75.065 65,131
9,679 9,0/9
10.102 9^94
942 884
504 483
BJ46 4^26
20,468 18,326
9UB /Bl
«461 8J31
1498 1.337
3^66 3.86B
19,704 12.124
5^01 4J68
90J67 36.125
9L304 5457
1,147 3.097
1.139 4,776
63,057 64,172
1.436 1^06
7499 7483
1.2S5 892
1J67 952
1453 2425
14,642 12.750
44.643 37.433
210.910 194.037
43.503 30,744
21299 19.458
47.031 43J«
d/^.«4O 9/J.KI/
IUTUML8DUBCCT
1,1 98,0 18j 006,7
323,9)7] 44,804
4flL290|
179,487
38.895
S68.726
100,751
1,000.794
87,826
73.838
380,9*1
161.4Z4
W«nr«i 2 2
W3*a*9ui*q 299 272
(HiKribcd BurtiQ
SOuttrtFltM S.5S3 SJ31
1293 10K
7.454 6.779
TaM 34S5| U05 8,749 7471
\JK \JSB5
n eg
4149 3816
6417 6.197
12.910 11,391
3.115 2432
29941 26948
224302 190194
9,539 6498
296.891 228,008
207 188
106 69
12.059 10.934
12.340 11.211
1^91 1410
26998 24544
40953 34708
5443 8.403
75.864 99.482
406 371
37S6 3413
3J21 91010
7.4(3 6403
33.514 301487
1MIT 16489
1497 1415
94.596 49,436
39.50^ S.B79I 6.0U oB.973'1 141.598 128,725
13S47 1231H 7906 7189) B2J22 74.747
63904 541881 109951 93I79J 464,164 313.391
9418 94171 1443 1.67»l 86,777 63.434
122,651 107.582) 181,713 156,419) 740,792 03U«7
RJQfflVlDUST
PiMdfWH 348.515 281488
unpma Midi 394,449 93.535
CcramicoonMMM 421451 6440
MKltmOUl 81 34
ToM 1.124497 383.495
mTMM.*NmeUUTEEMS»M I4M471 tM4)8
52Z33) 3)1.74)
264,412 89,779
892471 18.082
109 80
664.429 498,322
563.530 148.070
694.984 18.112
161 97
147)410 479.665| 2,113404 882.571
Page 50
1425,767 1,219440
2.I1Z75I 557.531
1,820,119 32.913
2471 699
5497.828 1410.680
740*41) 140141*
1.480222 1.095.186
2.109.504 558.875
1.433434 29.011
6.860 2267
5.009420 1.683.1 19
7,47*4*1 34>M4)
892.854 861.991
6J98435 14H223
1.491483 30.189
1J97 382
8,644,249 2449,785
13,tDJ47 44D4N
485.957 394.4)8
1463491 523,553
325.127 6.5)1
942 1)2
2.799417 BH7M
vmjar 1411471
341.190 287482
869.324 228.502
473.004 9.874
1.229 546
1475.707 493.954
!4914» 1JMJ44
855.910 641.933
290496 76.190
1473.078 33.883
4,102 14M
2429498 755.860
M1MM 1,144,147
.
302,605 Z28954
720,003 1)0001
890.584 13370
1493 531
1484.465 430.8561
14*1*7) D4JD
7.4)1.99) 9,916.87V
19.521.9*9 4.099.055
94914*4 2001219
1)232 849)
fiUJjU UJIJU
49.I4M14 M,1tU*7|
-------
FUEL COMBUSTION
HIGHWAY VEHICLES & NONROAD ENGINES 1%
NATURAL SOURCES
(WIND EROSION. AG. LAND)
9%
MISC. MFG.. PROCESSES
& SERVICES
2%
FUGITIVE DUST
68%
& FORESTRY
(TIUJNG * FEED LOTS)
15%
16
14
!
&
12
10
Unpaved Roads Conduction ActMttos
Major PM-10 Fugitive Dust Sources
Paved Roads
Page 51
-------
FUGITIVE DUST
56%
FUEL COMBUSTION
10%
HIGHWAY VEHICLES
& NONROAD ENGINES
3%
NATURAL SOURCES
(WIND EROSION. AG. LAND)
9%
MISC. MFG., PROCESSES.
& SERVICES 3%
AGRICULTURE & FORESTRY
(TILLING & FEED LOTS)
19%
!
1
Ul
Unpaved Roads Construction Activities Paved Roads
Major PM-2.5 Fugitive Dust Sources
-------
Figure 111-4
Summary of 1990 PM-10 Emissions by EPA Region
14
12
10
Region Region Region Region Region Region Region Region Region Region
I II III IV V VI VII VIII IX X
FUGITIVE DUST G AGRICULTURE & M NATURAL
FORESTRY SOURCES
I FUEL
COMBUSTION
MISC MFQ,
PROCESSES &
SERVICES
HIGHWAY
VEHICLES &
NONROAD
ENGINES
Page 53
-------
region VI, however, natural sources (agricultural wind erosion) is the second largest
source of PM-10. In seven regions, fuel combustion is the third largest source of PM-10
emissions.
Figure III-5 shows a graphic summary of PM-2.5 emissions by EPA region. On a
regional basis, the single largest source of PM-2.5 is fugitive dust. Region VI has the
highest fugitive dust PM-2.5 emissions (approximately 2.3 million tons per year), as well
as the highest PM-10 emissions. In regions I, II, VIII, and X fugitive dust PM-2.5
emissions are estimated less than 0.5 million tons per year. In six regions, fuel
combustion is the second largest source of PM-2.5 emissions; in three regions the second
largest source is agriculture; and, in region VI the second largest cause of PM-2.5
emissions is estimated to be natural sources.
2. 1990 SO2 Emissions
Table III-2 shows the results of the national SO2 emissions inventory. Results are
shown for source categories by EPA region, and by national total.
Figure III-6 shows total SO2 emissions by major source category, as well as the
relative contribution of sources composing the two largest (in terms of percent of total SO2
emissions) source categories. This figure shows that fuel combustion by electric utilities is
the largest source of SO2 emissions on a national basis (71 percent), followed by fuel
combustion by industrial sources (14 percent), and miscellaneous manufacturing,
processes and services (i.e., metals processing, chemical and allied product manufacturing,
petroleum and related industries, other industrial processes, mineral products, solvent
utilization, storage and transport, and waste disposal and recycling) (6 percent). Most of
the utility and industrial fuel combustion emissions are due to coal combustion.
Figure III-7 shows a graphic summary of SO2 emissions by EPA region. In eight
regions, utility fuel combustion is the single largest source of SO2; however, in region DC
metals processing is the largest source (metals processing contributed only 4 percent of
the total national SO2 emissions). Regions IV and V have the highest utility fuel
combustion emissions (over 4 million tons in 1990). In regions I, II, VI, VIII, DC and X
utility fuel combustion SO2 emissions are estimated less than 1 million tons per year. In
six regions, industrial fuel combustion is the second largest source of SO2.
3. 1990 NO, Emissions
Table III-3 shows the results of the national NO, emissions inventory. Results are
shown for source categories by EPA region, and by national total.
Figure III-8 shows total NO, emissions by major source category, as well as the
relative contribution of sources composing the two largest (in terms of percent of total NO,
emissions) source categories. This figure shows that fuel combustion by electric utilities is
the largest source of NO, emissions on a national basis (34 percent), followed by highway
vehicles (32 percent), fuel combustion by industrial sources (14 percent), and nonroad
engines and equipment (12 percent). Most of the utility fuel combustion NO, emissions
are due to coal combustion, while most of the industrial fuel combustion emissions are
due to natural gas combustion.
Page 54
-------
Figure 111-5
Summary of 1990 PM-2.5 Emissions by EPA Region
Region Region Region Region Region Regbn Region Region Region Regton
I II III IV V VI VII VIII IX X
FUGITIVE DUST D AGRICULTURE & H NATURAL
FORESTRY SOURCES
DO FUEL
COMBUSTION
MISC MFG,
PROCESSES &
SERVICES
HIGHWAY
VEHICLES &
NONROAD
ENGINES
Page 55
-------
Table 111*2
United States 1990 SO2 Emissions by EPA Region
(tons)
Source Category
FUEL COMB. ELEC.UTIL.
Region I Region II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X National Totali
Coal 165,937
00 210.447
Gas 16
Internal Combustion 629
Total 367,030
340.651
152,989
79
26,869
520,788
2,618.768
53,814
7
289
2,672,878
4.200.380
174,711
67
1.408
4.376.566
5,387.965
8,444
9
1.071
5.397.489
799.557
2,071
426
98
802,152
1.105.921
62)
10
37
1.106,588
379,192
133
2
9
379.336
174.337
8.998
140
119
183.594
63.644
32
0
63.677
15.226.552
612.261
757
30,529
15.870,099
FUEL COMB. INDUSTRIAL
Coal 6.360
OH 84,266
Gas 26
Other 487
Internal Combustion 466
Total 91.605
85,388
63,797
5.430
2.690
157.305
239.361
68,376
10.328
13.894
6
321.965
330.158
197.905
19.927
15,253
474
563,716
847,894
115.630
50.276
6.633
85
1,020.517
133,495
208.102
196.600
22.312
4.616
565,325
129,828
10.481
3,681
6,266
30
150.486
46.394
31.279
55.691
3.446
123
136.932
11.631
25.789
6.560
1.355
266
45.620
12.036
27.715
2.698
9.687
52.336
1.842.545
823,340
351.63;
82,222
6.063
3.105.807
FUEL COMB. OTHER
Commercial/Institutional Coal 2.471
Commercial/Institutional Ot 32.51 4
Commercial/Institutional Gas 33
Mbc. Fuel Comb. (Except Residential) 67
Residential Wood 638
Total 35.723
9.613
54,789
442
10
773
65,627
27.464
27,904
70
48
1,294
56.779
14.322
63.738
76
160
2,012
80,309
91,021
14,250
377
117
2,131
107,896
883
17.056
98
83
600
18.618
24.251
1.838
186
8
437
26.719
4.856
5.477
48
162
296
10.838
31
8.608
440
316
430
9,825
1.569
6.953
212
59
911
9.724
176,499
233.125
1.982
1.031
9.421
422,056
RESIDENTIAL COMBUSTION
Anthracite Coal 217
Bit/SubMCoal 15
Distillate CM 32,713
Residual 01
Natural Gas 64
Total 33,009
255
844
33,734
182
35,014
5.647
7,773
34,325
526
156
48.427
221
5.037
9,311
152
14.720
230
8.476
16,524
519
25.748
782
905
151
1.838
142
3.746
1,114
117
5.119
2,237
1.682
SB
3,977
10
690
175
875
692
5.949
22
6.663
6.711
29,612
136.945
526
1.595
175,389
CHEMICAL « ALLIED PRODUCT MFQ
Organic Chemical Mlg 10
Inorganic Chemical Mlg 305
Polymer A Resin Mlg
Agricultural Chemical Mlg
Pharmaceutical Mlg 1
Other Chemical Mlg
Total 316
16
5,891
16
785
6.710
9
10.941
22
7.286
18.257
1.709
77.497
4.367
2.109
217
8.415
94.312
19
32.140
78
6
10
15.559
47,812
9.083
136.747
2.376
1.246
39,012
188,464
2.226
2.190
406
4
4.826
4.022
15.491
19.514
21
43.380
3
67
43.471
8.517
2
7,543
16,062
17.117
333,098
6.836
3,793
228
78,6/1
439,744
(Continued)
-------
Table 111-2 (continued)
Source Category
METALS PROCESSING
Region I Region II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X National Totals
Non-Ferrous Metals Processing
Ferrous Metals Processing 5
Metals Processing NEC
Total 5
157
3.789
170
4,116
8.549
62,164
til
70,824
28,149
14.268
1.147
43,563
18.450
67.927
245
86.623
172.777
6,284
3.168
182,219
63.529
616
64.144
36,409
3,564
39
40.011
386,102
141
5
386.247
16.778
1.384
12.246
30,408
730.900
160.141
17.120
908.160
PETROLEUM ft RELATED INDUSTRIES
Oil ft Gas Production
Petroleum Refineries ft Related Industries
Asphalt Manufacturing 68
Total 68
55
5.325
5,380
33.265
108
33,373
45,901
26.524
282
72,707
112
80,210
151
80.473
112.460
78.647
319
191.426
9,422
59
9,481
5,129
B.969
14,099
535
27.233
77
27,845
4,756
68
4,824
164.193
274.351
1,131
439.675
OTHER INDUSTRIAL PROCESSES
Agriculture. Food, ft Kindred Products 23
Textiles, Leather, & Apparel Products
Wood, Putp A Paper, ft Publishing Products 4,330
Rubber & Miscellaneous Plastic Products
Machinery Products 29
Electronic Equipment
Miscellaneous Industrial Processes 7
Total 4.389
3
1
265
1
1
311
947
1.S29
261
11.072
435
11.768
195
76.697
23
157
77.072
1,644
6.004
203
274
8,124
14
24,153
6
271
24,443
135
1
628
149
1.114
851
374
176
1.402
16
3,062
82
3.159
203
11,215
23
11,441
3,345
1
137.173
835
255
311
2,522
144.441
en
~J
MINERAL PRODUCTS
Cement Manufacturing
Glass Manufacturing 743
Total 743
1.395
1.396
26.833
16.132
42.965
19,503
27,929
47.432
27,581
16,692
44.2/4
37.225
11.526
48.751
45.372
4,238
49.610
4,207
627
4.834
5,152
4,039
9.191
3,585
3.9)4
7.499
169,459
87.236
256.695
SOLVENT UTILIZATION
Oegreaslng
Graphic Arts
Surface Coating 1
aher Industrial
Total 1
0
0
667
667
11
49
60
31
3
26
60
46
46
2
2
0
0
0
31
3
753
49
836
(Continued)
-------
Table 111-2 (continued)
Source Category
STORAGE & TRANSPORT
Rtglonl Region II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X National Tottle
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Organic Chemical Storage
Inorganic Chemical Storage
Inorganic Chemical Transport
Bulk Materials Storage
ToUl 0
7
7
13
13
4
31B
181
502
136
1
S16
653
95
1.128
740
1.963
0
0
38
38
1
1,140
1.140
248
248
272
1,376
741
337
1
1,836
4.564
WASTE DISPOSAL ft RECYCUNO
Incineration 3,011
Open Burning 343
landfito
Other
ToUl 3,354
5,852
460
6.*:
3,143
636
3.980
2.037
1.BS6
3.893
3.892
1,557
1
5.450
9.020
754
319
10,093
688
534
42
1263
209
243
452
490
367
276
1.134
190
282
472
28,531
7.231
276
362
36.400
H1QHWAY VEHICLES
Light-Duty Gas Vehicles & Motorcycles 7.476
Light-Duty Gas Trucks 2.917
Heavy-Duty Gas Vehicles 468
Diesels 3.071
ToUl 13,933
11.664
4.446
687
4.392
21,190
14,497
6.869
997
6.758
28.121
28.932
11.819
2,033
13.887
56.671
26.966
10.838
1,821
12.272
51.897
18,186
7.312
1.229
8.285
35.012
7.459
3.123
555
3.866
15.003
4.644
1.943
345
2,401
9,333
21.454
8.153
1.253
7.985
38.845
5.545
2.262
388
2.648
10,842
146.824
58,682
9,776
65,566
280,848
cn
oo
NONROAD ENGINES AND EQUPMENT
Aircraft 340
Marine Vessels 614
Raflroads 1,262
Total 2,216
474
14.460
1.885
16.819
550
19.576
5.014
25,140
1.792
16.193
10.651
28,636
914
5.765
12,011
18.690
1.132
53.451
12.096
66,679
260
658
7.553
8,470
380
5.745
6.125
1.155
66,138
7,400
74.693
277
13,548
4.109
17.934
7.273
190,403
67,725
265,401
MISCELLANEOUS
Other Combustion
TOTAL
1
652^91
(42^04
66)
3.336,222
2.769|
6,462,926
'1
6,896,707
541|
2.137.571
'1
1,442,627
540)
627,4)0
i.iioj
(26.760
1.956
234,084
6.997J
22,357,11))
-------
Figure 111-6
United States 1990 SO2 Emissions by Source Category
FUEL COMBUSTION,
ELECTRIC UTILITY
71%
FUEL COMBUSTION.
OTHER & RESIDENTIAL 3%
FUEL COMBUSTION, INDUSTRIAL 14%
METALS PROCESSING 4%
MISC. MFG.. PROCESSES,
& SERVICES 6%
HIGHWAY VEHICLES
& NONROAD ENGINES
2%
Coal
OQ
Fuel Combustion
Electric UtOltes G Industrial
Gas
Page 59
-------
Figure 111-7
Summary of 1990 SO2 Emissions by EPA Region
Region Region Region Rogkxi Region Region Region Region Region Region
I II III IV V VI VII VIII IX X
FUEL D FUEL
COMBUSTION, COMBUSTION,
ELECTRIC UTILITY INDUSTRIAL
METALS
PROCESSING
I MISC MFQ,
PROCESSES &
SERVICES
FUEL
COMBUSTION,
OTHER &
RESIDENTIAL
! HIGHWAY
VEHICLES &
NONROAD
ENGINES
60
-------
Source Category
FUEL COMB. ELEC.UTIL
Table 1(1-3
United States 1990 NO, Emissfons by EPA Region
(tons)
Region! Region II Region III Region IV Region V Region W Region VII Region VIII Region IX Region X National Totals
Coal 66.724
00 49,019
Gas 12.524
Internal Combustion 1,794
Total 130.062
130,382
63,391
49,651
14,63)
256.055
846,566
18,207
5.822
1.725
672.342
1,464,738
50.211
50.301
12.196
t.577,446
1,964,368
4,049
8,915
3,493
1,978,824
842.255
1,246
316.621
7.966
1,168.088
582,874
159
6.005
5,787
594.825
580.980
133
1.479
177
582,769
173.624
10,205
115,226
2,394
301.450
42.448
13
20
42.481
6.694,981
196,634
564.565
50.164
7.5ofi.:W3
FUEL COMB. INDUSTRIAL
Coal 4.732
OH 20,527
Qa» 1 1,007
Other 3,906
Internal Combustion 1.628
Total 41,799
31,920
28,008
23,232
2.193
6
85,359
83.245
18.210
91.565
9.008
34,343
236.371
145.057
46.391
164,141
38.771
39.064
433.423
216.226
52,302
178.658
10,568
46.055
503,808
55,357
88,351
853.194
24.731
306,251
1,327,684
22,330
3,007
79.591
2.420
63,132
170,479
1 3U4T
5.937
103,544
5.046
26,489
172,156
15.130
24,658
125,333
6.106
52.442
225.668
8.142
9,786
26.234
14.110
466
58.739
613.279
297.177
1,656.497
118.657
569,876
3.255.6$
FUEL COMB. OTHER
Commercial/Institutional Coal 1.067
CommerclaMnsttlutlonalOfl 14,153
CommerciaWnslltullonal Ga« 5,476
Mbc. Fuel Comb. (Except Residential) 1.204
Residential Wood 4,457
Total 26.357
2.341
33,613
18.958
141
5,398
60,351
7.023
11,505
13,481
624
9.047
41,680
4.316
16.913
14.502
652
14,074
50,658
17,721
6,736
46.953
1,553
14,903
67,667
265
5.628
18.473
3,496
3,479
31.340
2.975
1.049
10,417
38
3.054
17^33
1,772
10.644
417
2.056
17^61
120
5.624
19.645
2.432
3,005
31,025
685
2.054
5,376
362
6.369
15^046
39,085
98,947
164,125
11.119
65.843
379,119
9
0)
RESIDENTIAL COMBUSTION
Anthracite Coal 23
BII/SubMCoal 2
Distillate Oil 20,455
Residual 01
Natural Gas 10,569
Total 31.049
27
61
21,142
30.385
51,616
620
547
18.677
65
26,239
46,148
24
394
5.832
26.573
32.823
20
378
10.355
87.545
98.297
32
438
25.641
26,111
16
149
698
19,147
20,009
335
940
9,830
11,105
2
290
29.196
29.487
69
1,923
3,777
5,789
1,988
80,749
268,902
352.433]
CHEMICAL ft ALLIED PRODUCT MFQ
Organic Chemical M(g 16
Inorganic Chemical Mb 14
Polymer ft Resin Mlg 5
Agricultural Chemical Mlb
Paint. Varnish. Lacquer, Enamel Mfc
Pharmaceutical Mlg
Other Chemical Mlg
Total 37
237
620
8,034
151
5.186
14,427
74
130
84
966
12,860
14,114
1,684
1.450
11.078
91.282
12,519
118.013
97
140
79
9,999
59
1
2.795
13.170
35.979
15.035
4,209
109,936
24
3,737
168,920
3.531
0
31,499
215
35,245
35
5,985
6.020
973
1,345
376
2,725
3
25.942
582
26,527
18.600
yttss
277. 104
59
25
38.269
(Continued)
-------
Table 111-3 (continued)
Source Category
METALS PROCESSING
Region! Region II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X National Tolali
Non-Ferrous Metals Processing
Ferrous Metals Processing
Metals Processing NEC
Total 0
1.497
1.411
196
3.104
297
9.844
2
10.143
5,744
7.727
11.656
25.129
1.399
22.534
297
24.230
2.502
4.956
282
7.740
1,400
48
10
1.458
1.528
6.522
8,050
742
26
5
773
356
237
91
684
15.466
53,306
12,540
BT3TT
PETROLEUM A RELATED INDUSTRIES
Oil & Gas Production
Petroleum Refineries ft Related Industries
Asphal Manufacturing 31
Total 31
225
225
3.775
41
3.816
169
3,200
201
3,570
20
8.717
171
8.909
48.514
19.601
65
68,179
2.156
6
2,162
707
3.193
3,900
801
7,118
48
7,967
1,683
20
1.703
50,211
49.668
582
100,461
OTHER INDUSTRIAL PROCESSES
Agriculture. Food, & Kindred Products S46
Textiles. Leather, ft Apparel Products
Wood, Pulp & Paper, ftPubOshlng Products 1,114
Rubber ft Miscellaneous Plastic Products
Machinery Products 149
Transportation Equipment
Miscellaneous Industrial Processes 82
Total 1,690
921
351
0
2
0
2,528
3,802
91
3,040
33
602
3,766
379
59
45,336
6
45
625
46,450
1.646
2.204
5
1,366
553
5,774
97
16.451
34
1.344
17.927
73
26
317
417
395
1.334
262
1.990
206
1,445
407
2,057
360
5,294
78
5.732
4,714
59
76.595
45
1.595
0
6.797
89.605
y
8
8
MINERAL PRODUCTS
Cement Manufacturing
Glass Manufacturing 509
Miscellaneous 64
Total 673
1.726
261
1,987
12,852
11,522
6.105
30.478
16.405
4.010
13.382
33.796
14.410
7.966
8.777
31.153
39.707
6.533
3.586
49.826
11.628
522
9.551
21.701
5,553
435
3.744
9.732
18.814
9,303
3.159
31.276
1.555
1.339
2.666
5.559
120.923
43.864
51.294
216,082
SOLVENT UTILIZATION
Degreaslng
Graphic Arts 4
Surface Coating 1
Other Industrial
Total 5
1
1
4
6
3
1.773
1.776
30
194
12
236
45
229
106
381
12
27
2
41
4
18
22
0
28
28
0
46
283
2.151
14
, :- .
STORAGE ft TRANSPORT
Petroleum & Petroleum Product Storage
Petroleum ft Petroleum Product Transport
Organic Chemical Storage
Inorganic Chemical Storage
Bulk Materials Storage
Total 0
82
82
0
4
45
35
84
0
1.046
47
511
8
1.613
11
11
5
5
67
504
570
108
toe
(0
1.055
155
51 1
213
539
J.473
OTtJOfcM
-------
Table Hl-3 (continued)
y
9
s
Source Category
WASTE DISPOSAL < RECYCLING
Incineration
Open Burning
Landfills
Other
"9'onl B.g|onn B.8lon(ll Regh)n(v
Region VI Region VII Region vill
HIGHWAY VEHICLES
Light-Duty Gas Vehicles « Motorcycles
Light-Duty Gas Trucks
Heavy-Duty Gas Vehicles
Diesels
669,0) i
267.802
67.938
500.801
1.505.557
671.204
262.397
64.297
442,710
1.440108
3.417.710
1.332,957
333.107
2.361,806
NONROAP ENGINES* EQUIPMENT
Non-Road Gasoline
Non-Road Diesel
Aircraft
Marine Vessels
Railroads
10,990
94.681
9.325
8.461
25.668
149T325)
11.508
136.61 f
10.662
17.374
68.796
244,951
22.568
291.793
33.812
33.194
146.139
52T505)
25.610
221,838
16.046
7.276
164,627
437,597
16.704
262.794
21.201
59.077
165.976
525.751
6.698
71,022
5,t4t
4.701
103.647
191.209
15.2!
250,376
21.823
30.612
tOt,544
4.97
48.665
5.310
9.502
56.377
124.832
123,51
1.478.206
139.372
172.587
929.290
MISCELLANEOUS
wner Combustion
TOTAL
-------
Figure 111-8
United States 1990 NO, Emissions by Source Category
MISC. MFG.,
PROCESSES & SERVICES
4%
FUEL COMBUSTION.
OTHER &
RESIDENTIAL
4%
NONROAD ENGINES
& EQUIPMENT
12%
HIGHWAY VEHICLES
32%
FUEL COMBUSTION.
ELECTRIC UTILITY
34%
FUEL COMBUSTION,
INDUSTRIAL
14%
Coal
Oil Gas
Fuel Combustion
I Electric Utilities G Industrial
Other
Page 64
-------
Figure III-9 shows a graphic summary of NOX emissions by EPA region. In nine
regions, utility fuel combustion and highway vehicles comprise the largest portion of total
NOr emissions; however, in region VI industrial fuel combustion is also a main
contributor. Interestingly, nonroad engines and equipment contribute 12 percent of the
total NOX emissions on a national basis, and approximately that amount (11 to 12
percent) on a regional basis.
4. 1990 NH, Emissions
Table III-4 shows the results of the national ammonia emissions inventory. Results
are shown for source categories by EPA region, and by national total.
Figure III-10 shows total NH3 emissions by major source category. This figure shows
that livestock feed lots are the largest source of NH3 emissions on a national basis (72
percent), followed by chemical product (i.e., agricultural chemicals or fertilizer)
manufacturing (12 percent). It should be reiterated from the discussion on relative
quality of emissions estimates, that these estimates are of lower reliability than estimates
for other pollutants due to a general lack of available data and emission factors that cause
a probable underestimation of emissions from other, mainly industrial, sources.
Figure III-11 shows a graphic summary of NH3 emissions by EPA region. In eight
regions, livestock feed lots comprise the largest portion of total NH3 emissions. In regions
IV, VI, and VI the second largest source of NHg is chemical (agricultural) product
manufacturing, while waste disposal and recycling is the second largest source of NHg
emissions in regions I, II, III, V, and DC In region VIII, livestock feed lots contribute
nearly all of the NH3 emissions (97 percent).
5. 1990 VOC and SOA Emissions
Table III-5 shows the results of the national VOC and SOA emissions inventory.
Results are shown for source categories by EPA region, and by national total.
Figure III-12 shows total anthropogenic SOA emissions by major source category, as
well as the relative contribution of sources composing the largest (in terms of percent of
total PM-10 emissions) source category. This figure shows that solvent utilization is the
largest source of SOA emissions on a national basis (34 percent), followed by highway
vehicles (25 percent), nonroad engines and equipment (12 percent), fuel combustion
(utility, industrial and all others) (11 percent), and storage and transport (including bulk
terminals and plants, petroleum storage, service stations, organic and inorganic chemical
storage, and bulk materials storage (10 percent). Most of the SOA emissions from solvent
utilization are due to surface coating and nonindustrial activities. It should be reiterated
from the discussion on relative quality of emissions estimates, that these estimates are of
lower reliability than estimates for other pollutants due to limited availability of source-
specific special profiles, which are needed to estimated SOA emissions based on VOC
emissions.
Figure III-13 shows a graphic summary of anthropogenic SOA emissions by EPA
region. On a regional basis, the single largest source of SOA is solvent utilization, while
the significance of other sources on a regional basis is distributed among all other source
Page 65
-------
Figure 111-9
Summary of 1990 NOX Emissions by EPA Region
Rogton Region Regbn Region Region Region Region Raglon Region Region
I II III IV V VI VII VIII IX X
FUEL HIGHWAY
COMBUSTION. VEHICLES
ELECTRIC UTILITY
LI FUEL
COMBUSTION.
INDUSTRIAL
NONROAO
ENGINES &
EQUIPMENT
iFUEL
COMBUSTION.
OTHER &
RESIDENTIAL
MISC MFG.
PROCESSES &
SERVICES
Page 66
-------
Source Category
FUELCOUB.ELEC.UTIL
Table 111-4
United States 1990 NH3 Emissions by EPA Region
(tons)
Region! Rofllon II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X Nallonal Totals
Coal
at 19
Qua 3
Total 22
18
57
75
0
11
11
22
0
26
40
67
0
e
28
36
19
195
4,544
4.758
0
1
4
4
0
0
2
2
8
38
46
0
0
1
19
286
4,727
5.033
FUEL COMB. INDUSTRIAL
Coal 0
Oil 294
Gas 105
Total 399
1
574
270
845
2
322
610
934
4
832
1,091
1,926
6
385
1,671
2.062
2
1,088
4.280
5.370
1
54
397
452
1
89
317
407
0
526
3.516
4,045
0
125
703
829
18
4.290
12,960
17.269
FUEL COMB. OTHER
Commarcial/lnsUMIonalCoal 0
CommorclaWraWuJlonalOl 375
CornrnerdalrtnalltutlonalGas 26
Residential Other 961
Total 1,362
0
637
79
1.089
1.804
0
324
61
962
1.347
0
369
68
390
827
1
184
169
889
1.263
0
181
78
145
405
0
39
50
125
213
0
50
32
90
171
0
153
79
156
388
0
72
16
104
193
2
2.384
677
4.9)0
7,973
WASTE DISPOSAL ft RECYCLIM
POTW
10.1761
8.421
20i675J 7.032)3,677) 2.50l|
9,479)
-------
Table 111-4 (continued)
Source Category
HIGHWAY VEHKLES
Region! RegionII Region III RegionIV Region V Region VI Region VII Region VIII RegionIX Region X NallonalTotal*
UgM-Diity Gas Vehicles*. Motorcycles 1.157
Light-Duty Gas Trucks 448
Heavy-Duty Qas Vehicles 72
Diesels 422
Total 2.099
1,605
683
105
604
3,197
2.244
902
153
929
4.228
4.476
1.617
312
1,909
8.515
4.173
1.666
279
1,667
7.806
2.814
1.124
189
1.139
5,266
1.154
480
85
531
2,251
719
299
53
330
1.400
3.320
1.253
192
1.098
5.863
858
348
60
364
22.723
9,020
1.500
9.011
1.629 42.254
NONROAD ENGINES AND EQUPIIENT
Marine Vessels 11
Rafiroads 33
Total 44
69| 117
SOJ 132
119) 249
170| 31
-. 281 1 317
451 1 348
365
319
684
20|
199) 152
219) 152
285
195
480
71
108
179
1.139
1.788
2.926
AGRICULTURE ft FORESTRY
Livestock Feed Lots 1.500
1.290
54.125
176.356) 214.814
126.846) 207.957
227.293) 59.469
33.690
1.105.337
TOTAL 10.9M 17,550 78.9SS 252,036 273.059 266,308 264,093 234.009 92,185 44.298 1 1,531.440
-------
.. .. w Figure IIMO
Un,ted States 1990 NH3 Emissions by Source Category
FUEL COMBUSTION,
METAL & INDUSTRIAL
PROCESSES
5%
LIVESTOCK FEED
LOTS
72%
CHEMICAL PRODUCT MFG. 12%
PETROLEUM & RELATED
INDUSTRIES 3%
WASTE DISPOSAL &
RECYCLING
5%
HIGHWAY VEHICLES &
NONROAD ENGINES 3%
Page 69
-------
Figure 111-11
Summary of 1990 NH3 Emissions by EPA Region
300
200
100
R«glon Roglon Region Ragkxi Ragton Ragton Ragton Ragton Ragton Ragton
I II III IV V VI VII VIII IX X
LIVESTOCK FEED D CHEMICAL
LOTS PRODUCT MFQ
PETROLEUM &
RELATED
INDUSTRIES
I WASTE DISPOSAL
& RECYCLING
FUEL
COMBUSTION.
METAL &
INDUSTRIAL
PROCESSES
I HIGHWAY
VEHICLES &
NONROAD
ENGINES
Page 70
-------
Table 111-5
United States 1990 VOC and SOA Emissions by EPA Region
(tons)
CM
SOA
Hoglonll
R»glonlV
.', :
nrtMHHjlll JMOO ,. .',.; 1.115
MgttnMOm IJ634 ; ' V «
acMCAi i AU.OJO PBooua mo
apf«cCtwi*tfMp O63 a
tapricambiwg '
Po««ir9R9«iWg Ull f 1
Vtl*nfCMn**Ug
PMVnWvUegnr.EnnKHg ">
nwncuMug ueo , t>
OMCMM^Wg 130 1
TOW U6S4 ;. > ;...., IT
tTALiPinccaon
r«««»mufcpiiii»«m i * 0
ItjMPKBMtagNEC -,
ToM 1 ^ 0
PETBOUIM 1 RELATED BWUSTRW
aKtenoduabi m o
PHOtulMnrtllMMMHIki « tt
AvMMMKMkg m > 4
TOM no __^ M
onm MDUSTMUIL noccssES
Atrtoitn. Foot 6, UnM Prate* 3J93 ^N 0
T«ttl,U4lr«».9«n>MIPnilDCtl H7 ,- tP«»r.>iM*Mvn<*ai u* v v M
nttKlUMMmonnMctaU* 1JM ,, ' 4
MMnattft » , ,. > 1
McirPmfcok "
EkdortcEiMnM * "
UtamaiMuMPraxM 0» > 7
TOM U« 43
" 4* T
tn :.;.';. M
ID .. '. . 0
*>'*".'".' '
1674 /p: -V 91
ua- ::'.;' i
no . '. , '; :" n
«so» toe
52 .^-. t
isio '. :' '' :37
400 ,« ', > 0
"VX-" 1
BJ1I , . tjIM
IJW . L SI
J/jOOt . 1,527
tt,Ot * - 64
i.m ' ' , 4
»»;>' ''in
n x t
» , ,0
U4j''° , ;'l
IW1I - ' - '«
U60 ^ C9
*§>.-..>"»
».-:-,;-. ho
«.,';; j|''o
SJBI , a
&234 8
IOJT> »
K108 K
4M 0
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IB ?
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suer «M
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W 7
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B. U
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I.IS5 «
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isn n
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SUII . 1409
Mil .'.'. IIS
2.041 . 2J538
3A>1B , ^ 64
m ' o
21.020 ' 99
m . t
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nsn , t»
3«S27 , ' 'n
IIUBS :>-.. M8
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e i
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30.SI1 227
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not AT
1.4)1 ' 1
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320 6
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19.964 . . ; . Ifl07
ISt^M 64
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47Z390 --- . . UK2
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7.530 290
63.147 0
U(7 \3tl
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14.433 M
2.M B
M . 1
V
tus m
131.707 Ml
U77- i5
S3 > t
22 1
n o
Z£6V 4D
1513 .71
3712 i
tjn a
1.523 1
I1J04 177
475 .; . .- .1
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WS , 1
I.4J2 - $
tlOT UM
1120 41
7.142 . :. 4,
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100.797 301
I36J014 * M
SO ° t
«ai . in
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n t
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171200 407
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Page 71
(Oonijwef
-------
Table 111-5 (continued)
Sourc* Category
SOLVENT Un-HATON
R»glonll
RtBtonB
RttfonlV
RMhmV
RMkmVI
R*donVI
RMhmVD
RtcjtonOt
RooonX
NHanriToWs
I **
I .**
I roc so* I
SOU
WC
tO*
VOC
80*
WC
SO* | VOC «OA I YOC S0> I YOC
CO*
Oi^SBj - 41787. ,;;,, tff
OVMCIM U3H ."';,' VI*
DrCMlJ US' ',,.. 48
teknCatog 1S0470 ,Uj \ JM>
CM MUM tin '':: ''. I
»-«UM B.«i .'''. an
TQM 318£91 . . 31181
5640 M
awK1 .< 253
is4'. . . it
219481 ' , .:: UN
»s» "-*.,. o
mat ' s ' . - 1440
494.707 j . \.^411
4S43I . . >'''> 97
I9488 ,.'}''". »
917445 ''. MM
tun :.-'.- at
181449. ...1471
nijQOO . . 7.442
124.843 B7
HB.6B8 1^8
48.993 ., ' /.'. US
SOMW ";'.. :S4B»
17742 ;.;/',..; B
922431 . VMB
1.119275 it478
184*13 ; ..'. tW
151 417 : : 1490
a ;.,';..;. m
ones »«i
«n .';;:.-' 158
avis ; ..-; u«
1.475.8U ; tun
535! B.
H1I7 ':'>":' 1*9
17*7 '.'; W
21IJSS4 ; MM
2428 ,'/ 1
aau .'..' lot
582431 un
4344S ,-M
ajm : »
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2750 ''.'.'; 1
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358.403 94BS
iiSi "Bo-
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t7.7« . . ' ', M
1.7W :'.' t
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mm TM
14.140 131
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i»i , n
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112.115 UI7
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11.114 M
2.142 4]
82X11 801
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tarn tat
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221.717 4JUJ
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tors -.' '.;;. 7»9
403 TO
4404 . 101
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8.IM 148
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1
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cum 7421
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tun . (14
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Page 72
-------
Figure 111-12
United States 1990 Anthropogenic SOA Emissions by Source Category
NONROAD ENGINES
& EQUIPMENT
12%
FUEL COMBUSTION
11%
HIGHWAY VEHICLES
25%
MISC. MFG.. PROCESSES
& SERVICES
8%
SOLVENT
UTILIZATION
34%
STORAGE &
TRANSPORT
10%
40
-C-30
I
20
10
Degrading Graphic Arts Dry Cleaning
Surface
Coating
Solvent Utilization
Other
Industrial
Nonindustrial
Page 73
-------
Figure 111-13
Summary of 1990 Anthropogenic SOA Emissions by EPA Region
Region Region Regton Region Region Region Region Regton Region Region
I || III IV V VI VII VIII IX X
SOLVENT
UTILIZATION
I MISC MFQ,
PROCESSES &
SERVICES
HIGHWAY
VEHICLES
STORAGE &
TRANSPORT
O NONROAD
ENGINES &
EQUIPMENT
CD FUEL
COMBUSTION
-------
categories. The second largest source of SOA is highway vehicles in all 10 regions, which
follows the national trend noted by Figure III-12.
B. 1990 CANADA EMISSIONS
Table III-6 shows the results of the 1990 Canada emissions inventory for PM-10, PM-
2.5, S02, NOX, NH3, VOC and SOA. Results are shown for each pollutant by Canadian
province. The table shows that most primary particulate is emitted from sources located
in British Colombia, Ontario, and Quebec. Most precursor emissions come from sources
located in British Columbia, Ontario, Alberta and Quebec.
C. 1990 MEXICO EMISSIONS
Table III-7 shows the results of the 1990 Mexico emissions inventory for PM-10, PM-
2.5, SO2, NO,, VOC and SOA Results are shown for each pollutant by Mexican state, for
states that border the U.S. The table shows that primary particulate and precursor
emissions are emitted from sources scattered throughout these states, and no significant
relative contributions from any one or two states are noted.
Table 111-6
1990 Canada Emissions
(tons)
Province
Alberta
British Columbia
Manitoba
New Brunswick
Newfoundland
Nova Scotia
Ontario
Prince Edward Island
Quebec
Saskatchewan
PM-10
191,643
403,789
44,018
78,003
119,813
70.686
286,249
8,826
261,534
93,581
PM-2.5
137,526
330,822
32,901
64,209
91,036
51,522
228,319
8,634
213,023
66,670
SO,
558.889
96,967
418,081
108,950
39,987
139,902
1.153,756
1.908
607.489
68.047
NO,
531.774
275,648
94,022
51,574
46,062
83,946
609.219
6,511
267.754
160.464
NH,
101,008
9,700
22,592
1.817
733
2.577
41.877
1,052
25,755
26,160
VOC
392,307
516,601
90,215
60,258
86,420
79,797
746,123
11.963
439.674
169,918
SOA
2.447
3,148
564
461
516
647
4,661
190
3.233
1,167
Page 75
-------
Table 111-7
1990 Mexico Emissions
(tons)
Mexico State
Baja Ca, Norte
Chihuahua
Coahuila
Nuevo Leon
Sonora
Tamaulipas
PM-10
25.189
26,182
26,079
29,224
24,590
28,917
PM-2.5
15,257
16,115
15,649
17,953
14,684
17,371
SO,
465,520
481,131
501,672
534,258
476,598
550,433
NO,
112,697
126,269
99,813
116,868
93,987
113,138
voc*
133,647
133,441
76,035
108.706
73.711
100,566
SOA
1,232
1,395
848
1,150
862
1,086
NOTE: The World Bank report actually reports hydrocarbon emissions, but these emissions have been assumed, for use
in the National Particulates Inventory, to be VOCs including methane.
Page 76
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ABBREVIATIONS AND ACRONYMS
AAR
ADTV
AIRS/AQS
ASTM
BEA
CARB
cm
Cd
CO
COE
DOE
EIA
EIB
FAA
FAC
FGD
GNP
GT
1C
INEGI
LTO
MW
NAA
NAAQS
NAPAP
NH3
NO,
OMS
OPPE
PM
PM-10
PM-2.5
SCC
SEDS
SIC
SIP
SOA
SO2
tpy .
TSP
u,
u.,
VMT
VOC
Association of American Railroads
average daily traffic volume
Aerometric Information Retrieval System/Air Quality Subsystem
American Society for Testing and Materials
Bureau of Economic Analysis
California Air Resources Board
centimeter
coefficient of drag
carbon monoxide
U.S. Army Corp of Engineers
Department of Energy
Energy Information Administration
(EPA/OAQPS) Emission Inventory Branch
Federal Aviation Administration
fractional aerosol coefficients)
flue gas desulfurization
gross national product
gas turbines
internal combustion
Institute Nacional de Estadistica, Geografia e Informatica
Ian ding-takeoff operation(s)
megawatts
nonattainment area(s)
national ambient air quality standard
National Acid Precipitation Assessment Program
ammonia
oxides of nitrogen
(EPA) Office of Mobile Sources
(EPA) Office of Policy, Planning, and Evaluation
particulate matter
particulate matter with an aerodynamic diameter of 10 urn or less
particulate matter with an aerodynamic diameter of 2.5 um or less
Source classification code
State Energy Data System
Standard Industrial Classification
State Implementation Plan
secondary organic aerosols
sulfur dioxide
tons per year
total suspended particulate
threshold velocity
threshold friction velocity
vehicle miles travelled
volatile organic compound
Page 77
-------
Page 78
-------
REFERENCES
AAR, 1991: Association of American Railroads, "Railroad Ten-Year Trends 1981-1990,"
Washington, DC, 1991.
BEA, 1991: Bureau of Economic Analysis, U.S. Department of Commerce, "Table SA-5 --
Total Personal Income by Major Sources 1969-1990," data files, Washington, DC,
September 1991.
BOG, 1987a: Bureau of the Census, U.S. Department of Commerce, "1987 Census of
Agriculture, Volume 1: Geographic Area Series," county data file, Washington, DC,
1987.
BOC, 1987b: Bureau of the Census, "Industrial Series Census of Construction," Table 10:
Value of Construction Work for Establishments with Payroll by Location of
Construction Work, 1987.
BOC, 1990: Bureau of the Census, "Construction Review," Reporting F.W. Dodge Division
values from McGraw Hill, (1985-1990).
BOC, 1992: Bureau of the Census, U.S. Department of Commerce, "1990 County Business
Patterns," Washington, DC, 1992.
BOC, 1994: Bureau of the Census, U.S. Department of Commerce, "1990 Census of
Population and Housing," county data file, Washington, DC, 1994.
GARB, 1991: California Air Resources Board, "Identification of Volatile Organic
Compound Species Profiles, ARB Speciation Manual," Second Edition, August 1991.
COE, 1991: U.S. Army Corp of Engineers, "Waterborne Commerce of the
United States, Calendar Year 1989," WRSC-WCUS-89, Part 5, New Orleans, LA, June
1991.
DOE, 1990: U.S. Department of Energy, Energy Information Administration,
"Steam-Electric Plant Operation and Design Report," Form EIA-767, data files for
1990.
DOE, 199la: U.S. Department of Energy, Energy Information Administration, "State
Energy Data Report -- Consumption Estimates 1960-1989, "DOE/EIA-0214<89),
Washington, DC, May 1991.
DOE, 1991b: U.S. Department of Energy, Energy Information Administration, "1990
Petroleum Supply Annual, "DOE/EIA-0340, annual publications for 1985 through
1990, Ofiice of Oil and Gas, Washington, DC, May 1991.
Page 79
-------
REFERENCES (continued)
DOT, 1990: U.S. Department of Transportation, "Highway Statistics 1985-1989
(Annual)," 1986-1990.
DOT, 199 la: J. Haugh, U.S. Department of Transportation, Federal Highway
Administration, Highway Information Management, personal communications with
E.H. Pechan & Associates, Inc., Durham, NC, 1991.
DOT, 199 Ib: U.S. Department of Transportation, Federal Highway Administration,
Highway Information Management, Summary of Local Functional System Mileage in
a Lotus 1-2-3 spreadsheet provided by D. Kestyn, 1991.
EPA, 1974a: U.S. Environmental Protection Agency, "Emission Inventory of Agricultural
Tilling, Unpaved Roads and Airstrips and Construction Sites," EPA-450/3-74-085,
November 1974.
EPA, 1974b: U.S. Environmental Protection Agency, "Development of Emission Factors
for Fugitive Dust Sources," EPA-450/3-74-037, Research Triangle Park, NC, June
1974.
EPA, 1984: U.S. Environmental Protection Agency, "Paved Road Particulate Emissions,"
EPA-600/7-84-077, Washington, DC 1984.
EPA, 1985: U.S. Environmental Protection Agency, Air and Energy Engineering Research
Laboratory, "Ammonia Emission Factors for the NAPAP Emission Inventory,"
Research Triangle Park, NC, December 1985.
EPA, 1987: U.S. Environmental Protection Agency, Office of Mobile Sources, "Air Toxics
Emissions from Motor Vehicles," EPA-AA-TSS-PA-86-5, Ann Arbor, MI, September
1987.
EP.\, 1988: U.S. Environmental Protection Agency, "Gap Filling PM10 Emission Factors
for Selected Open Area Dust Sources," EPA-450/4-88-003, February, 1988.
EPA, 1989: U.S. Environmental Protection Agency, Air and Energy Engineering Research
Laboratory, "The 1985 NAPAP Emissions Inventory (Version 2): Development of the
Annual Data and Modeler's Tapes," EPA-600/7-89-012a, Research Triangle Park, NC,
November 1989.
EPA, 1990: U.S. Environmental Protection Agency, Air and Energy Engineering Research
Laboratory, "The 1985 NAPAP Emissions Inventory: Development of Temporal
Allocation Factors," EPA-600/7-89-010d, Research Triangle Park, NC, April 1990.
Page 80
-------
REFERENCES (continued)
EPA, 1991a: U.S. Environmental Protection Agency, "Compilation of Air
Pollutant Emission Factors, Volume I: Stationary Point and Area Sources," AP-42,
including Supplements A, B, C, and D, September 1991.
EPA, 1991b: U.S. Environmental Protection Agency, Office of Mobile Sources,
"MOBILE4.1 Fuel Consumption Model (Draft)," Ann Arbor, MI, August 1991.
EPA, 1992a: U.S. Environmental Protection Agency, "User's Guide to MOBILES (Mobile
Source Emission Factor Model)," Draft, Chapter 2, Ann Arbor, MI, December 1992.
EPA, 1992b: U.S. Environmental Protection Agency, "Documentation for Estimation of
Nonroad Emission Estimates for the United States," Research Triangle Park, NC,
November 1992.
EPA, 1992c: U.S. Environmental Protection Agency, "Nonroad Engine Emission
Inventories for CO and Ozone Nonattainment Boundaries," Ann Arbor, MI, October
1992.
EPA, 1992d: U.S. Environmental Protection Agency, Office of Mobile Sources, "Motor
Vehicle-Related Air Toxics Study," Public Review, Draft, Ann Arbor, MI, December
1992.
EPA, 1993a: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, "Regional Interim Emission Inventories (1987-1991)," EPA-454/R-93-0212,
Research Triangle Park, NC, May 1993.
EPA, 1993b: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, "National Air Pollution Trends, 1900-1992," EPA-454/R93-032, Research
Triangle Park, NC, October 1993.
EPA, 1994: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, "PM-10 Controlled Emissions Calculator," Research Triangle Park, NC,
January 1994.
Europa, 1993: The Europa World Year Book, 1992, Volume II, Kenya to Zimbabwe, 33rd
edition, pgs. 1875-1877, 1993.
FAA, 199 la: Federal Aviation Administration, "Terminal Area Forecasts, FY 1991-2005,"
FAA-APO-91-5, U.S. Department of Transportation, July 1991.
FAA, 199Ib: Federal Aviation Administration, "Air Traffic Activity,'1 U.S. Department of
Transportation, 1991.
Page 81
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REFERENCES (continued)
Gillette and Passi, 1988: D.A. Gillette and R. Passi, "Modeling Dust Emission Caused by
Wind Erosion," Journal of Geophysical Research, Vol. 93, #D11, pp. 14233-14242,
,fe-November, 1988. '
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