SECTION E: AREA SOURCES (continued)
-------
REPORT NO. DOT-TSC-OST-75-42
AN ESTIMATION OF RIVER TOWBOAT
AIR POLLUTION IN SAINT LOUIS, MISSOURI
Joseph C. Sturm
FEBRUARY 1976
FINAL REPORT
DOCUMENT IS AVAILABLE TO THE PUBLIC
THROUGH THE NATIONAL TECHNICAL
INFORMATION SERVICE. SPRINGFIELD
VIRGINIA 22161
Prepared for
U,S, DEPARTMENT OF TRANSPORTATION
OFFICE OF THE SECRETARY
Office of the Assistant Secretary for Systems
Development and Technology
Office of Systems Engineering
Washington DC 20590
-------
NOTICE
This document is disseminated under the sponsorship
of the Department of Transportation in the interest
of information exchange. The United States Govern-
ment assumes no liability for its contents or use
thereof.
NOTICE
The United States Government does not endorse pro-
ducts or manufacturers. Trade or manufacturers'
names appear herein solely because they are con-
sidered essential to the. object of this report.
-------
TECHNICAL REPORT STANDARD TITLE PAGE
1. Report No. 2. Government Accession Ne.
DOT-TSC-OST-75-42
4. Title and Subtitle
AN bo I IMA HUM OF KlvtK iUWDUAl A1K rULLiUHUW
IN SAINT LOUIS, MISSOURI
7. Author's)
Joseph C. Sturm
9. Performing Organisation Name and Address
U.S. Department of Transportation
Transportation Systems Center
Kendall Square
Cambridge MA 02142
U.S. Department of Transportation
Office of the Secretary
Office of the Asst. Sec. for Sys. Dev. and Tech.
Office of Systems Engineering
Washington DC 20590
3. Recipient's Catalog Ne.
5. Report Date
February 1976
6. Performing Orgoniiotion Code
8. Performing Organ! lotion Report No.
DOT-TSC-OST-75-42
10. Work Unit No.
OS622/R6501
11. Contract or Grant No.
13. Type of Report and Period Covered
Final Report
July 1974 - February 1975
14. Spontortng Agency Cod*
IS. Supplementary Notes
16. Abstract
This study gives an estimate of river towboat air pollution emissions for
the St. Louis Air Pollution Study area. No emissions from secondary sources or
from recreational boating on the river of other areas are considered. The
emission estimate is based primarily on river traffic data taken by the Corps of
Engineers at Lock 27 near St. Louis and on exhaust emission factors of similar
engines of the Coast Guard fleet and railroad locomotives.
The emissions are given for each grid of the Environmental Protection
Agency (EPA) St. Louis Grid Plan so that these results can be utilized for the
St. Louis Regional Air Pollution Study.
The total annual emissions in the SLAPS region from towboats operating on
the 135 miles of the Mississippi river and the 95 miles on the Missouri river are
estimated to be:
Oxides of nitrogen
Total hydrocarbons
Carbon Monoxide
Oxides of sulfur
Particulates
3,297 tons/year
939 " "
2,101 "
462 " "
198 "
17. Key Words
Exhaust Emissions
Air Pollution
River Towboats
St. Louis Regional Air Pollution Study
18. Distribution Statement
DOCUMENT IS AVAILABLE TO THE PUBLIC
THROUGH THE NATIONAL TECHNICAL
INFORMATION SERVICE, SPRINGFIELD
VIRGINIA 22161
19. Security Cloisif. (of this report) 20. Security Cloitif. (of this page)
Unclassified Unclassified
21. No. of Pages
64
22. Price
Perm DOT F 1700.7 (..69)
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PREFACE
This report presents the methodology for, and results of,
estimating river towboat air pollution emissions for the St. Louis
Air Pollution Study. The study was conducted as part of the Tech-
nology for Environmental Analysis Project (PPA-OS-522) by the DOT
Environmental Measurements Branch, Transportation Systems Center,
for the Energy and Environment Division, Office of the Secretary
of Transportation.
The St. Louis Air Pollution Study (SLAPS) is composed of
several individual studies exploring the relationships between the
urban complex and air quality. These studies are investigating
the sources of air pollution, the transport and transformation of
air pollutants, and the effects of air pollution upon receptors.
This report is a revision of an earlier draft. It includes
the following changes:
a) The revised EPA St. Louis Air Pollution Study grid layout
is used.
b) Vessel emissions are given for the complete SLAPS area,
including the Missouri River. The extended coverage plus
the grid revisions have resulted in an increase of grid
elements with vessel traffic, from 47 in the draft report,
to 131.
c) Additional river traffic volume data from the Corps of
Engineers for a week in January and one in April, 1974
have been used to improve the traffic data base.
d) A revised methodology estimates river traffic character-
istics on the basis of a simple origin/destination
analysis.
e) A simplified explanation of the methodology is given.
The results of this study are estimates of towboat exhaust
emissions, and thus must be treated as approximations. However,
variations in daily traffic volume are not large, and the emissions
111
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estimates are considered representative of daily river towboat
emissions. Reduced emissions occur under exceptional conditions,
when river operations are severely curtailed (during periods of
extreme flooding or icing, blockage of navigation, work stoppage,
and so on). *
The author gratefully acknowledges the assistance of Mr.
Lambert Buckhold, Navigation Branch, U.S. Army Corps of
Engineers, St. Louis, who provided the vessel traffic information;
Lt. Wilburn Elkins, Marine Inspection Office, 2nd District, U.S.
Coast Guard; Mr. J.B. King, Chief, Construction-Operations Divi-
sion, U.S. Army Corps of Engineers, Omaha; and Mr. James Swift,
Business Manager, Waterways Journal, St. Louis. The author also
acknowledges the assistance provided by Russel R. Waesche, DOT
Secretarial Representative, Region VII, and his staff; Ms. Dianne
Soble, HUD Area Office, St. Louisfand the TSC staff members con-
ducting the U.S. Coast Guard Vessel Emissions Monitoring and
Control Project. The author is also grateful for the many hours
of effort by Mr. David A. Knapton, Mr. Frank D. Lonergan, Mr.
Robert Murphy, Mrs. Virginia Christiansen, and Mr. Paul R. Phaneuf,
Raytheon Service Company.
IV
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TABLE OF CONTENTS
Section Page
1. INTRODUCTION 1-1
1.1 Objective 1-1
1.2 Scope 1-1
1.3 Limitation of Results 1-3
2. RIVER TRAFFIC - ST. LOUIS, MISSOURI 2-1
2.1 Historical Perspective 2-1
2.2 St. Louis - Present 2-2
3. EMISSIONS ASSESSMENT' 3-1
3.1 Methodology 3-1
3. 2 River Traffic 3-3
3.2.1 Daily Mississippi Towboat Traffic 3-3
3.2.2 Towboat Route and Horsepower Size
Distribution at Lock 27 3-4
3.2.3 Traffic Below St. Louis 3-6
3.2.4 Missouri River Traffic .. 3-8
3.2.5 Port Area Traffic 3-8
3.2.6 Passage through Locks 26 and 27 3-9
3.2.7 Towboat Speeds and Throttle Settings 3-11
3.2.8 Temporal Distribution of Traffic 3-12
3.2.8.1 Daily and Monthly 3-12
3.2.8.2 Hourly 3-13
3.2.9 Summary of River Traffic Data Used for
Emissions Calculations 3-13
3.3 Emission Factors 3-13
4. EMISSIONS CALCULATIONS 4-1
5. SUMMARY AND CONCLUSIONS 5-1
5.1 Data Base 5-1
5.2 Results 5-1
6 . REFERENCES 6-1
-------
LIST OF ILLUSTRATIONS
Figure Page
1-1 St. Louis Air Pollution Study Area 1-2
3-1 St. Louis Area River Vessel Traffic
Estimates 3-7
3-2 Horsepower Times Number of Engines in
Statistical Sample 3-16
VI
-------
LIST OF TABLES
Table Page
3-1. SUMMARY OF TRAFFIC, LOCK 27, MISSISSIPPI RIVER 3-4
3-2. MAJOR ROUTES OF TOWBtiATS THROUGH LOCK 27 SEPTEMBER
1973 (4 DAYS) AND APRIL 1974 (6 DAYS) 3-5
3-3. ALLOCATION OF SWITCHING BOAT HP-HR TO GRID AREAS.. 3-10
3-4. TOWBOAT PASSAGE THROUGH LOCKS IN SLAPS REGION 3-11
3-5. THROTTLE SETTING VALUES ON THE UPPER MISSISSIPPI RIVER
SYSTEM 3-12
3-6. SLAPS TOWBOAT TRAFFIC CHARACTERISTICS 3-14
3-7. EMISSION FACTORS (CO, NOX AND THC) FOR THE MOST
PROMINENT GM ENGINES (85% OF FULL POWER) 3-18
3-8. EMISSIONS FACTORS (CO, NOX AND THC) FOR THE MOST
PROMINENT GM ENGINES (50% OF FULL POWER) 3-19
3-9. COMPOSITE EMISSIONS FACTORS (CO, NOX AND THC) 3-20
3-10. EMISSIONS FACTORS FOR SOX AND PARTICULATES 3-21
3-11. EMISSIONS FACTORS AT IDLE 3-22
4-1. EMISSIONS CALCULATION OUTLINE 4-2
4-2. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSOURI
RIVER (HERMANN TO MISSISSIPPI RIVER - SECTION 1) 4-3
4-3. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSOURI
RIVER (HERMANN TO MISSISSIPPI RIVER - SECTION 1) 4-4
4-4. TOWBOAT TRAFFIC (HP-HR PERS'GRID PER DAY) MISSISSIPPI
RIVER (ALTON TO CHAIN-OF-ROCKS CANAL - SECTION 4) 4-5
4-5. TOWBOAT EMISSIONS (GRAMS PER'GRID PER DAY), MISSISSIPPI
RIVER (ALTON TO CHAIN-OF-ROCKS CANAL - SECTION 4;
ILLINOIS RIVER TO ALTON - SECTION 3; PERUQUE ISLAND
TO ILLINOIS RIVER - SECTION 2) 4-6
vn
-------
LIST OF TABLES (CONTINUED)
Table Page
4-6. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI
RIVER (CHAIN-OF-ROCKS CANAL TO LOCK 27 LOCALE E -
SECTION 5) 4-7
4-7. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI
RIVER (CHAIN-OF-ROCKS CANAL TO LOCK 27 LOCALE E -
SECTION 5) 4-8
4-8. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI
RIVER (CHAIN-OF-ROCKS CANAL TO MONSANTO - SECTION 7).. 4-9
4-9. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI
RIVER (CHAIN-OF-ROCKS CANAL TO MONSANTO - SECTION 7).. 4-10
4-10. TOTAL EMISSIONS FOR SWITCH BOATS GRAMS PER GRID PER
DAY 4-11
4-11. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY) MISSISSIPPI
RIVER - ST. LOUIS PORT AREA, NORTH (CHAIN-OF-ROCKS
CANAL TO MONSANTO - SECTION 7) 4-12
4-12. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI
RIVER (MONSANTO TO SUGAR LOAF - SECTION 8) 4-13
4-13. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI
RIVER (MONSANTO TO SUGAR LOAF - SECTION 8) 4-14
4-14. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI
RIVER - ST. LOUIS PORT AREA. SOUTH (MONSANTO TO SUGAR
LOAF - SECTION 8) 4-15
4-15. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI
RIVER (SUGAR LOAF TO ROCKWOOD ISLAND - SECTION 9) 4-16
4-16. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY) MISSISSIPPI
RIVER (SUGAR ROCKWOOD ISLAND - SECTION 9) 4-17
4-17. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY) FOR
LOCK 26 - SECTION 10, AND LOCK 27~- SECTION 6,
ENGINES AT IDLE 4-18
4-18. RIVER VESSEL EMISSIONS IN ST. LOUIS AIR POLLUTION STUDY
REGION, BY ZONE 4-19
5-1. RIVER VESSEL EMISSIONS (GRAMS PER RIVER MILE PER DAY). 5-3
5-2. ANNUAL EMISSIONS FOR ST. LOUIS AIR QUALITY CONTROL
REGION (#70) 5-3
viii
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1, INTRODUCTION
1.1 OBJECTIVE
This report describes a method for estimating river towboat
air pollution and it presents the estimates of air pollution
emissions from river towboats operating in the region of St. Louis,
Missouri. These emissions include: carbon monoxide (CO), oxides
of nitrogen (NO ), total hydrocarbon (THC), oxides of sulfur
A
(SOX), and particulates (Part). The emissions estimate will be
used by participants of the St. Louis Air Pollution Study (SLAPS).
1.2 SCOPE
This study is limited to primary air pollutants emitted from
river towboat diesel engines. Emissions originating from ship
electrical-service generating unitSj cargo, loading and unloading
activities, and fueling and maintenance operations are not in-
cluded. *
The emissions are estimated for river towboats operating on
the waterways within the SLAPS region. This area includes the
Mississippi River, from Mile 100 below St. Louis to Mile 235**
above St. Louis; and the Missouri River from the confluence of the
Mississippi River to near Mile 95. This area is shown in Figure
1-1-
The method used consists of estimating river traffic and propul-
sion engine characteristics from limited statistical information on
river traffic and from observations by Coast Guard and Army Corps of
Secondary emissions are probably rather small, as compared to
the exhaust emission. For example, ship electrical service
generating units have a maximum rating that is approximately three
percent of the propulsion unit and they are normally operated well
below capacity.
**
River miles given here are based on Corps of Engineers river
distance measured above the mouth of the Ohio River. The Gateway
Arch in St. Louis is located near Mile 180.
1-1
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ILLINOIS RIVER
ALTON MADISON
OCK COUNTY
26
HAIN OF ROCKS CANAL
LOCK 27
PORT AREA
GATEWAY ARCH
ST CLAIR
COUNTY
BOND
COUNTY
I
\
I 7
_\ I
RIVER MILE
100
Figure 1-1. St. Louis Air Pollution Study Area
1-2
-------
Engineers personnel familiar with river vessel operations. The
traffic volumes and engine types are then used to calculate emis-
sions, based on measurements of emissions factors for similar
diesel engines used on Coast Guard vessels and rail locomotives.
No emission testing of towboats was undertaken during this study.
The emissions are calculated and presented for the EPA St.
Louis grid plan.* This layout divides the 10 county area into
grids of 1, 2, 3, 4, 5, 6, and 10 km squares, depending upon the
level of expected air pollutant emissions.
1.3 LIMITATION OF RESULTS
Emissions estimates presented are made from the averages of
daily river traffic volume and towboat engine characteristics. The
volume of traffic is low and the range of vessel characteristics
is large, so that an estimate of hourly emission rates is not
practical; therefore, the emissions are expressed only as daily
estimations. No error estimate or sensitivity analysis was made,
as a greater level of effort would be required to establish the
distribution and accuracy of all the variables. While no verifica-
tion of the emissions inventory accuracy was possible, the estima-
j-,
tion is sufficiently accurate to determine the relative contribu-
tion of towboats to St. Louis air pollution and to serve as input
data for the urban atmospheric dispersion model under development
by the U.S. Environmental Protection Agency (EPA) for their
Regional Air Pollution Study (RAPS).
EPA, St. Louis Grid Square Coordinates, June, 1974,
1-3
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2, RIVER TRAFFIC - ST, LOUIS, MISSOURI
2.1 HISTORICAL PERSPECTIVE
The American river system has provided a vital right-of-way
for transportation. It contributed to the Western expansion of a
century ago and today continues to promote the economic well-being
of our nation.
In the early 19th century, keelboats were the primary mode of
river traffic. Transportation downstream was relatively easy; but
to move upstream, laborers had to walk the river bank, pulling the
boat with ropes while others aboard pushed with poles which
reached the river bottom. Although steam-powered towboats became
dominant around the.time of the Civil War, freight movements were
still usually with the river flow. Not until the development of
propeller towboats, around World War I, was there significant bi-
directional freight traffic. In 1930, the diesel engine began
replacing the steam engine as the primary propulsion for towboats,
and by 1974 there were no steam-powered towboats on the Mississippi
River system.
Since the introduction of the diesel engine and the develop-
ment and improvement of existing waterways by the U.S. Army Corps
of Engineers, total ton mileage has increased from 9 billion in
1930 to 210 billion in 1970.
v (D*
The Mississippi River constitutes the major link for the 6,000
navigable miles of the Mississippi Valley's system of inland water-
ways. The river is navigable from Minneapolis, MN to New Orleans,
LA, a distance of 1,837 miles. St. Louis lies near the mid-point.
Above St. Louis are 28 locks and dams constructed by the U.S. Army
Corps of Engineers to aid navigation. Open waters lie south of St.
Louis and extend below New Orleans to the delta at the Gulf of
Mexico.
Numbers in parentheses refer to the references in Chapter 6,
2-1
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2.2 ST. LOUIS - PRESENT
The Port of Metropolitan St. Louis consists of 70 miles of
Mississippi River frontage between mile 138.8 and mile 208.8.
Included in this stretch are Lock 27 on the Chain-of-Rocks Canal,
a ten-mile canal built to bypass a low-water area approximately
6 miles above St. Louis, and Lock 26, further upstream near Alton,
111.
St. Louis is one of the busiest inland ports in the United
States. It serves as a major transfer point for both upstream and
downstream traffic on the Mississippi River System. However, port
freight volume has increased only about 10 percent since 1960 while
other port cities along the Mississippi have sometimes doubled or
tripled their freight volume over that same period.* '
St. Louis is still the third largest port on the river and the
river itself in the St. Louis area handles approximately 50,000,000
tons per year in terminal and thru traffic. This freight volume
generates a significant amount of river traffic.
The vessel traffic in the St. Louis area consists of long-
distance transit tows, originating and terminating long-distance
tows, intra-port traffic, switcher boat fleeting operations (making
and breaking tows) and the operations in passing through Lock 26 to
Lock 27. Approximately 50 barges are either loaded or unloaded per
day and about 700-800 barges are handled each day in making and
braking tows in the St. Louis port area.^ '
Tows vary in size from one to as many as fifty or sixty
barges. A typical tow is about 1,000 ft. long. Tows above the
St. Louis area are restricted by the Army Corps of Engineers to
17 barges. The lock width restrictions dictate that tows be
split into smaller tows with a minimum size of two barges in width
by six barges in length. Below St. Louis, tows are generally
larger. Typical tows are usually five barges in length by six
barges in width.
2-2
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Towboat engines range in size from around 100 hp to over
10,000 hp. Towboats with engine sizes under 500 hp have proved
inefficient for transporting barges over large distances and are
generally utilized in the immediate port vicinity for preparation
of larger tows. Engine sizes substantially over 10,000 hp are
unlikely in the near future, since engines in 7,000-9,000 hp range
can handle as large a tow as is possible to maneuver on the
Mississippi with present navigation technology. * *
2-3
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3, EMISSIONS ASSESSMENT
3.1 METHODOLOGY
The air pollutants emitted by river vessel propulsion systems
are calculated by the following expression:
Emissions = Horsepower-Hours x Emission Factor
where:
Emissions (grams per day per grid element)
Horsepower-Hours (Number of towboats per grid per day) x
(average HP x average throttle position) x
(grid distance/average speed)
Emission Factory (grams per HP-hour for carbon monoxide (CO),
oxides of nitrogen (NO ), total hydrocarbon
J\.
(THC), oxides of sulfur (S0x), and particu-
lates (Part)).
River traffic data (the number of towboats and engine horse-
power, average throttle settings and average speeds) are estimated
in Section 3.2. The selection of emission factors is presented in
Section 3.3. In Section 4, the emissions are calculated by grid
and in Section 5 per mile of navigable waterway in the SLAPS
region.
The following methodology was used to determine river vessel
exhaust emissions.
Step 1 - River Vessel Traffic Characteristics
Examine available statistical data and qualitative
information on river traffic in order to divide the
river and traffic into zones, where, within each
zone, traffic characteristics can be assumed to be
uniform. (Sections 3.2.1 and 3.2.2)
Step 2 - EPA Grid Locations and Travel Distance for each Grid
Draw grids on U.S. Geological Survey Maps using the
3-1
-------
Universal Transverse Mercator (UTM) system coordin-
ates specified by EPA. Vessel travel distances for
each grid are then measured along the river center.
Step 3 - Determine River Traffic Volume Counts
Determine average traffic flow for the assumed types
of traffic and zones determined in Step 1. (Sections
3.2.2, 3.2.3, 3.2.4, 3.2.5, 3.2.6)
Step 4 - River Vessel Horsepower
Determine average river vessel engine horsepower
for the assumed traffic and zones. (Sections 3.2.2,
3.2.3, 3.2.4, 3.2.5)
Step 5 - Vessel Average Engine Duty Cycle
Determine average river engine throttle setting and
times of operation (utilization factors) based on
estimates made by the Corps of Engineers, for up-
bound and downbound traffic and switcher boats.
(Sections 3.2.5, 3.2.6, 3.2.7)
Step 6 - Vessel Average Speeds
Determine vessel speeds, upbound and downbound, for
the various zones of operations based on estimates
made by the Corps of Engineers. (Section 3.2.7)
Step 7 - Vessel Horsepower-Hours Per Grid Per Day
Calculate river vessel horsepower-hours per grid
per day by the expression;
Horsepower-Hours = (Number of towboats per grid per
day) x (average HP x average
throttle) x (grid distance/
average speed)
Step 8 - Emission Factors
Identify river vessel engine types by manufacturer
and model representative of the towboat population.
3-2
-------
Determine emission factors for engines identified
for the proper horsepower utilization factor. In-
corporate a frequency weighting factor for each
engine to determine the average emission factor for
the pollutants CO, NO , THC, SO and particulates.
J\ J\.
These emission factors are in grams per brake horse-
power-hour. (Section 3.3)
Step 9 - Emissions
Calculate the river vessel exhaust emissions by the
equation:
Emissions = Horsepower-Hours x Emission Factor where
emissions are in grams per day per grid
element. The emissions are calculated
separately for upbound and downbound;
through and local traffic and for the
switcher boats in the terminal areas.
Step 10 - Yearly Distribution
Determine the estimated distribution of river
traffic over the year so that the emissions calcu-
lated in Step 9 may be adjusted for time of year.
3.2 RIVER TRAFFIC
3.2.1 Daily Mississippi Towboat Traffic
Towboat operation in the SLAPS area is estimated by analyzing
a combination of (1) vessel traffic records collected by the St.
Louis Corps of Engineers at Lock 27, (2) estimates (by the Coast
Guard and the Corps of Engineers personnel at St. Louis and at
Omaha) of river traffic characteristics, and (3) from aggregate
statistics of waterborne commerce issued by the Corps of Engineers
at New Orleans.
The only actual count of river towboat activity is that taken at
Lock 27. No other traffic records are taken at any other location
in the SLAPS region. Passage through Lock 27 is recorded by the
3-3
-------
Corps of Engineers* and includes towboat name and owner, horsepower,
direction of travel, origin and destination, number of barges and
cargo tonnage, and times of passage through the lock. For this
study, lock traffic for four periods is used and is summarized in
Table 3-1.
TABLE 3-1 SUMMARY OF TRAFFIC, LOCK 27, MISSISSIPPI RIVER
Month/Year
August, 1973
Sept. , 1973
Jan. , 1974
April, 1974
Average
No. of
Days
4
4
5
6
No. of Towboats/Day
36, 29, 37, 33
30, 34, 33, 24
29, 27, 33, 34, 32
35, 31, 29, 28, 42, 38
Daily
Average
33.8
30.2
31.0
33.8
32.2
Average
HP
1958
2256
2536
2483
2336
A value of 32 towboats** per day through Lock 27 is
selected for the average river traffic above St. Louis.***
3.2.2 Towboat Route and Horsepower Size Distribution at Lock 27
The averages of traffic and horsepower in Table 3-1 were con-
>
sidered insufficiently disaggregated for estimating emissions.
Therefore, traffic through the Lock was divided by origin and
destination to provide some indication of traffic volume and
**
***
U.S. Army Corps of Engineers, St. Louis, Missouri, Form Number
68.
This does not include towboat activity at the docks and termin-
als making and breaking tows, see Sections 3.2.3, 3.2.4, 3.2.5.
The "center" of the port is assumed to be that location on the
river where half the dock facilities are above that point and
half the docks are below midpoint is called "above" St. Louis
and downstream from the midpoint is called "below" St. Louis.
3-4
-------
towboat horsepower characteristics on the various segments of the
Mississippi River and its navigable tributaries. The traffic
route and horsepower distribution for the September and April ob-
servation periods (total 10 days, 320 towboats) given in Table 3-2
TABLE 3-2. MAJOR ROUTES OF TOWBOATS THROUGH LOCK 27
SEPTEMBER 1973 (4 DAYS) AND APRIL 1974
(6 DAYS)
Location of
Upper Port
Missouri
Mississippi R.
Illinois R.
Alton area
Missouri R.
Mississippi R.
Illinois R.
Alton area
Location of
Lower Port
Below St. Louis
Below St. Louis
Below St. Louis
Below St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Percent Two -".'ay
Traffic
through Lock 27
1
12
20
9
2
17
8
31
100
Avg. HP
2667
3447
3747
2410
3000
2481
2314
1191
Examination of the towboat "horsepower distribution" for the
ten day sample indicated: (1) 1000-2000 HP class towboats are in
considerable use in the St. Louis/Alton area; (2) through traffic
consists of many sizes of towboats (3200 HP is the most frequent
size; some are in the 5000-7000 HP range); and (3) smaller towboats
of less than 2000 HP are generally used for moving small tows to
the Ohio River or short distances below the St. Louis SLAPS region.
The analysis also showed that the average size of towboats was
smaller above St. Louis than below.
In addition to the Mississippi River System traffic above St.
Louis, there are additional distinct segments of river traffic
activity not shown in the Lock 27 data. These are: (1) traffic
3-5
-------
below St. Louis; (2) Missouri River traffic; (3) St. Louis port
activity associated with barge terminals; and (4) activity
associated with Lock 26 and 27. For making the emissions calcula-
tions, a traffic flow split was established on the basis of the
distribution shown in Table 3-2 and on the contribution of traffic
from other activity described below. The traffic flow is shown
in Figure 3-1.
3.2.3 Traffic Below St. Louis
The greatest uncertainty in estimating river vessel emissions
is that portion contributed by towboats below St. Louis which do
not go through Lock 27. Since Lock 27 is the only point in the
SLAPS region where river traffic is counted, the river traffic
which stays either above or below the Lock is not counted. As
most of the St. Louis port activity is below the Lock, the assump-
tion was made that little traffic stayed above the Lock 27 and
therefore the emissions for such' traffic could be neglected.
Traffic below Lock 27 consists of: (1) traffic between ports
above the lock and the port area, (2) local switcher boat activity,
(3) traffic between ports above the lock and below St. Louis and
(4) traffic on the lower Mississippi which either originates or
terminates below Lock 27. Of these four categories, the vessels
which go through the lock are counted and averaged for this calcu-
lation and the port activity is based upon Coast Guard estimates.
The Corps of Engineers annual inland waterway statistical
summary * ' indicates that the annual number of towboats on the
Mississippi River below St. Lquis averages 20 vessels per day. For
our calculations, we have assumed that 24 vessels per day use the
Mississippi below St. Louis. The routes of sixteen of these in-
clude passage through Lock 27 and are counted. An additional
eight are assumed to either start or end their journeys below Lock
27. This is only half the number observed that either start or
end their journeys below Lock 27 and whose trips are above St.
Louis. The tows below St. Louis are normally larger than those
going above St. Louis, and below the city there is likely to be no
3-6
-------
ILLINOIS R.
CHAIN OF ROCKS
CANAL
ALTON, WOOD RIVER
above" St. Louis
V'belpw" St.
Louis
GRID 815
Figure 3-1. St. Louis Area River Vessel Traffic Estimates
(Numbers Refer to Vessels Per Day)
3-7
-------
concentration of inter port activity similar to that between the
city and the Alton area. Therefore, a smaller number of tows but
of average larger size is assumed below St. Louis.*
3.2.4 Missouri River Traffic
Traffic information on the Missouri River was provided by the
Corps of Engineers at Omaha. They estimated that during the
navigation season approximately 3 tows per day (total, both direc-
tions) are on the lower end of the Missouri River.** For this esti-
mate 1-1/2 tows, each direction per day were used. Speeds esti-
mated by the Coast Guard are 6 mph upbound and 10 mph downbound.
Average horsepower from Omaha Corps of Engineers information was
2400 HP.
3.2.5 Port Area Traffic
In the St. Louis port area, switcher boats are responsible
for "spotting" barges when making and breaking tows and moving
barges to docks and yards.
Estimates of port area activity from the U.S. Coast Guard
Office in St. Louis was that 15 towboats operating as switcher
boats were within the port area. Since this was the only informa-
tion available, switcher boat traffic density was assumed to be 15
vessels per day in the port area.
A check on the traffic estimate is given by estimating the number
of barges handled per day in the St. Louis area (see Figure 3-1).
The traffic estimate check consists of calculating total barges
for: (1) 16 tows/day between St. Louis and the upper Mississippi
River System (18 barges/tow), (2) 10 tows/day originating or
terminating in the Alton area (18 barges/tow), (3) 8 tows/day
between St. Louis and the lower reaches of the Mississippi River
(30 barges/tow). This calculation shows 708 barges per day
handled in the St. Louis port area. This total number of barges
is in agreement with that indicated by Kearney (3), who estimated
50 barges loaded and unloaded per day and 700-800 barges handled
in making and breaking tows per day.
**
See Section 3.2.8.1 for monthly variation.
3-8
-------
Switcher boats operate part of the day with their engines at
idle because of the nature of their operations; therefore, the
following duty cycle was assumed:
1/2 of the operating period at idle
1/2 of the operating period at 50 percent power setting.
The duty cycle was assumed to be applicable for all switcher boats
in the port area, and that the duty cycle for switcher boats was 8
hours for a normal working day.
Switcher boat engines usually range from 300 to 500 horse-
power. An average of 400 hp was used for this analysis. There-
fore, the 15 port area switcher boats contribute an estimated
12,000 hp-hr daily.
The distribution of port activity was determined from an Army
Corps of Engineers' publication listing Mississippi River terminals,
docks, mooring locations and warehouses which located the terminals
and docking areas within each grid element. ' Table 3-3 cites
those grids (Column 1) and gives the number of terminals within
that grid (Column 2). Column 3 lists the weighting factors arrived
at by dividing the number of terminals within each grid element by
the 67 total number of terminals. Column 4 gives horsepower-hours
contributed by switcher boats to each grid.
3.2.6 Passage through Locks 26 and 27
River traffic on the Mississippi River must traverse two locks
in the SLAPS region, Lock 26 at Alton and Lock 27 on the Chain-of-
Rocks Canal. Frequently the tow must be broken into two segments
before entering the lock and additional time is required for re-
assembling it. Corps of Engineers personnel estimate an average
combined time of delay while waiting to enter the locks, possibly
breaking the tow, lock passage, and re-making the tow at three
hours for Lock 27 and five hours for Lock 26. The emissions for
each lock are calculated on the basis of the waiting periods
(engines at idle) without consideration for short periods of
propulsion used in positioning the tows and traversing the locks.
The lock traffic data are shown in Table 3-4.
3-9
-------
TABLE 3-3. ALLOCATION OF SWITCHING BOAT HP-HR TO GRID AREAS
(1)
GRID #
1010
1073
1072
1069
1038
1032
1031
1030
999
998
955
924
849
848
847
815
Total
(2)
X
NO. OF
TERMINALS L8J
5
4
6
4
2
4
4
5
4
6
8
3
2
3
4
3
67
(3)
WEIGHTING
FACTORS
.08
.06
.08
.06
.03
.06
.06
.08
.06
.09
.12
.04
.03
.04
.06
.04
.99*
(4)
HP-HR*
PER GRID
960
720
960
720
360
720
720
960
720
1,080
1,440
480
360
480
720
480
11,880**
as % of 12,000 hp-hr.
**
1% due to rounding = 120 hp-hr.
3-10
-------
TABLE 3-4. TOWBOAT PASSAGE THROUGH LOCKS IN SLAPS REGION
Lock
26
27
Grid used for Wait
Upbound
1019
1078
Downbound
1048
1010
Wait
Time
(hrs)
5
3
Towboats (both
directions)
and HP
22 (2900 HP)
22 (2900 HP)
and
10 (1200 HP)
3.2.7 Towboat Speeds and Throttle Settings
Towboat speeds are influenced primarily by the river current
and difficulty of navigation. For this study the river system is
broken into three zones of different speed operation:
1. Chain-of-Rocks Canal,
>v
2. Missouri River,
3. Mississippi River.
The Chain-of-Rocks Canal is essentially a constant level pool
with no current. Narrowness of the waterway restricts speeds to
5-7 mph. Six mph was used for our calculations. Towboat speeds
on the Missouri River are 6 mph upbound and 10 mph downbound.
These speeds were suggested by the St. Louis Coast Guard Office.
The Mississippi River towboat speeds used are 5 mph upbound
and 10 mph downbound. This is the average speed indicated by the
American Waterways Operators, Inc. for traffic between St. Louis
and New Orleans.
Average horsepower utilization factors (throttle settings)
were obtained from personnel of the U.S. Coast Guard Marine In-
spection Office in St. Louis. Their estimates were based on
personal experience and information obtained from the towboat
industry. Throttle settings used in this study are shown in Table
3-5.
3-11
-------
TABLE 3-5. THROTTLE SETTING VALUES ON THE UPPER
MISSISSIPPI RIVER SYSTEM(4)
Waterway Segment
Mississippi River (exclusive of Chain-of-
Rocks Canal)
Missouri River
Chain-of -Rocks Canal
Large Towboats
Small Towboats
Lock 26, 27
Port Area Activity
Throttle Setting*
Upbound 0.85
Downbound 0.50
Upbound 0.75
Downbound 0.50
Upbound 0.33
Downbound 0.33
Upbound 0.75
Downbound 0.75
Idle
Switcher boat
duty cycle (See
Section 3.2.5)
Idle throttle setting: 0
Full-power throttle setting: 1.00
3.2.8 Temporal Distribution of Traffic
3.2.8.1 Daily and Monthly - The traffic data acquired for this
study displayed little variation on a daily or monthly basis. From
other information sources, it is known that ice on the Missouri
River and on the upper reaches of the Mississippi curtail vessel
movements during the winter months. Likewise, periods of high
water, i.e., flooding, also reduce (if not completely stop) tow-
boat operations. The Illinois River and the Mississippi River
throughout the SLAPS region are normally open to river traffic all
year long. Short periods of cold weather may cause ice on the
f 81
Mississippi pool above Lock 26.l J Stoppages in towboat operations
are infrequent and thus no emission reduction is assumed for this
area. The Missouri River is normally closed to navigation from
the beginning of December to the first of March. (The actual
dates are a function of the weather conditions.) Therefore,
3-12
-------
vessel emissions are considered to be zero for the Missouri River
from 1 December to 1 March each year.
3.2.8.2 Hourly - River vessel traffic cannot be estimated on an
hourly basis. Therefore, there can be no disaggregation of emis-
sion rates on an hourly time scale and have it remain meaningful.
Actual hourly emissions per grid range from zero (when no towboat
is in the grid) to as much as five times the hourly average based
on daily emission rates when a large towboat is traveling upbound.
3.2.9 Summary of River Traffic Data Used for Emissions Calculations
In Figure 3-1 values use,d for the river traffic data in calcu-
lating horsepower-hours are illustrated. Values of vessel traffic
and engine characteristics are shown in Table 3-6.
3.3 EMISSION FACTORS
As shown in section 3.1, towboat emissions are calculated by
taking the product of horsepower hours and emission factors, the
latter are expressed in mass of pollutant per horsepower-hour.
An emission factor is a statistical average, or a quantitative
estimate, of the rate at which a pollutant is emitted as a result
of a particular activity, divided by the level of that activity.
Emission factors are estimated by a variety of techniques, includ-
ing measurements of typical sources, process material balances and
engineering estimates. As such, they are not precise indicators of
single source emissions; they are more valid when estimating emis-
sions from an aggregation of sources. In addition, the accuracy of
emissions calculated by emission factors improves as the similarity
increases between the source used when establishing the emission
factors and the source(s) being estimated.
In this study, emission factors are based on measurements ta.ken
of diesel engines used on both Coast Guard vessels and on railroad
locomotives.'-9'10'11'12^ The towboat prime movers are similar to
the engines of the Coast Guard fleet and locomotives. The two
variables used in determining the emission factors are:
-------
TABLE 3-6. SLAPS TOWBOAT TRAFFIC CHARACTERISTICS
SECTION
1
2
3
4
5
6
7
8
9
10
DESCRIPTION
Missouri R.
Mississippi R. above 111. R.
Mississippi R. 111. R. to Alton
Mississippi R. Alton to Canal
Mississippi R. Alton to Canal
Chain-of-Rocks Canal
Chaln-of-Rocks Canal
Lock 27
Mississippi R. Canal to
Middle of Port
Mississippi R. Canal to
Middle of Port
Mississippi R. Middle of Port
to End of Port
Mississippi R. Below Port
Lock 26
*
Up: upward-bound vessels.
*
DOWN: downward-bound vessels.
TYPE OF
TRAFFIC
Through
Through
Through
Through
Local
Through
Local
Through
Local
Through
Local
Switcher Boats
Through
Switcher Boats
Through
Through
END GRIDS
NORTH
4(West)
141
394
1087
1087
1082
1082
1010
1010
1040
1040
1030
1234
998
1048
SOUTH
2302
242
1019
1233
1233
1079
1079
1078
1078
1030
1030
1030
815
815
1685
1019
NO. EACH
VESSLES
DIRECTION
1.5
7
11
11
5
11
5
11
5
11
5
15
12
15
12
11
AVG.
HP
2400
2900
2900
2900
1200
2900
1200
2900
1200
2900
1200
400
2900
400
2900
2900
THROTTLE SETTING
UP*
.75
.85
.85
.85
.85
.33
.75
Idle
Idle
.85
.85
Switchei
.85
Switchei
.85
Idle
DOWN*
.50
.50
.50
.50
.50
.33
.75
Idle
Idle
.50
.50
Boat Duty
.50
Boat Duty
.50
Idle
SPEED (mph)
UP* DOWN
6
5
5
5
5
6
6
5
5
Cycle
5
Cycle
5
10
10
10
10
10
6
6
10
10
10
10
-------
(1) manufacturer and type of engine, (2) the percentage of throttle
opening since emissions are non-linear with throttle load. Infor-
mation on towboat engine types was obtained from the Inland River
f 4 -i
Records, ' which documents approximately 3500 river vessels. Data
available for each vessel include: vessel size and power plant
type and size, age, manufacturer and model type.
A simple random sample of 250 observations was taken (refer-
ence, 4). Essential information about each observation was docu-
mented for analysis; e.g., total engine horsepower, engine manu-
facturer, engine type, etc.
According to reference 4, the manufacturers of towboat diesel
engines are:
56 percent manufactured by General Motors Corporation (CMC)
21 percent by Caterpillar
6 percent by Cummins
5 percent by Superior
3 percent by Copper-Bessomer
The remaining 9 percent of the engines are manufactured by compa-
nies having less than 1 percent of the market.
Further analysis of the CMC data indicated that the three most
common types of CMC engines present in the sample were:
1. CMC 567
2. CMC 645
3. CMC 71 Series.
The emission factors for these three engine series were aggre-
gated to form composite emission factors. These three engine
types propel the majority of the towboat fleet and their emission
factor data are readily available. Figure 3-2 illustrates the
total horsepower of the three engine series for the established
sample; the figures were derived by multiplying the horsepower per
engine by the number of engines in the sample. GM engines were
3-15
-------
40,000
£ 30,000
WJ
a,
H
tu
3 20,000
PJ
10,000
39,500
33,000
22,000
GM71
SERIES
GM567 GM645
ENGINE MODEL
Figure 3-2. Horsepower Times Number of Engines in Statistical Sample*-4-*
3-16
-------
used exclusively because the GM engine represents over 75 percent
of the total horsepower in the statistical sample taken from The
Inland River Record.
Table 3-7 shows the data used to determine the emission fac-
tors for CO, NO , and THC for upward bound towboats. The emission
yt
factors are for the three GM engines and are the values for 85
percent of full power which represent most of the upward-bound
traffic (see Table 3-6). Similarly Table 3-8 presents the data
used to determine the emission factors for CO, NO , and THC for
J\,
downward-bound towboats, based on 50 percent of full power. The
final emissions for CO, NO , and THC for upward-bound and downward-
J\.
bound towboats are shown in Table 3-9.
Emission factors for SO and particulates were not available
A.
from actual measurements as were the other primary pollutants.
Therefore, an EPA document^ ^ was used which gives these emission
factors for heavy-duty truck and locomotive diesel engines. Since
towboat engines are essentially the same as truck and locomotive
engines, with slight modifications, these emission factors were
considered acceptable for this study. Factors of 1.4 grams per
horsepower-hour for SO and .6 grams per horsepower-hour for
J\
particulates were derived from the calculations presented in
Table 3-10.
The emission factors for the upward-bound towboats are based on
85 percent of full power. They are assumed to be the same for the
towboats at 75 percent of full power (upward-bound on the Missouri
River and "local traffic" on the Chain-of-Rocks Canal). The emis-
sion factors for downward-bound towboats are based on 50 percent
of full power. They are assumed to be the same for those towboats
at 33 percent of full power (through traffic on the Chain-of-Rocks
Canal).
In actual measurements of exhaust emissions little difference
is noted between emissions in gm/hp-hr at 75 and 85 percent of full
power. Also, some measurements show little difference between 33
and 50 percent of full power. Therefore, while some error may be
introduced by using a single emission factor for upward-bound
3-17
-------
TABLE 3-7. EMISSION FACTORS (CO, NOX AND THC) FOR THE
MOST PROMINENT GM ENGINES (85% OF FULL POWER)
(IN GRAMS PER HORSEPOWER-HOUR)
ENGINE
EMISSION FACTORS
GM-645^9'10^
Avg. g/hp hr
GM-S67 (7)*
Avg. g/hp hr
GM-71 Series
Avg . g/hp hr
CO
2.1
2.4
2.5
3.5
2.6
g/hp-hr
NO
X
11.8
13.0
8.2
7.7
10.1
THC
.6
.8
.6
.5
.6
3.2
3.8
.9
.9
2.2
11.4
11.7
8.6
8.4
10.0
1.3
1.1
3.2
3.4
2.3
6.8
8.7
11.2
8.9
10.7
12.8
13.5
12.3
1.1
5.1
4.1
3.4
*Multiple data points.
3-18
-------
TABLE 3-8. EMISSIONS FACTORS (CO, NOX AND THC) FOR THE
MOST PROMINENT GM ENGINES (50% OF FULL POWER)
(IN GRAMS PER HORSEPOWER-HOUR)
ENGINE
EMISSION FACTORS
(7,8)*
GM-645
Avg. g/hp hr
f 71 *
GM-567 l }
Avg. g/hp hr
GM-71 Series *
Avg. g/hp hr
CO
2.0
1.0
8.2
8.3
4.9
8.6
5.5
g/hp hr
NO
X
13.0
10.0
7.6
7.3
7.5
6.8
8.7
THC
.7
.8
.4
.4
.4
.5
.5
1.9
1.8
1.3
1.6
8.1
5.0
6.1
6.4
4.5
4.1
4.1
4.2
2.6
3.0 ,
6.8
4.2
10.5
12.1
14.1
12.2
2.8
5.7
4.2
4.2
*Multiple data points
3-19
-------
TABLE 3-9. COMPOSITE EMISSIONS FACTORS (CO, NOX AND THC)
t-o
o
AT 85% OF FULL POWER SETTING
ENGINE
GM71
Series
GM567
GM645
% OF
TOTAL HP
23%
35% .
421
Emission Factors
g/hp-hr
CO N0x THC
8.9 12.3 3.4
2.2 10.0 2.3
2.6 10.1 .6
Weighted Emission
Factors
(g/hp-hr X %)
CO N0x THC
2.0 2.8 0.8
0.8 3.5 0.8
1.1 4.3 0.2
Composite Emission
Factors
g/hp-hr
CO 3.9
N0x 10.6
THC 1.8
AT 50% POWER SETTINGS
GM71
Series
GM567
GM645
23%
35%
42%
4.2 12.2 4.2
1.7 6.4 4.2
5.5 8.7 0.5
1.0 3.0 1.0
0.6 2.2 1.5
2.3 3.7 0.2
CO 3.9
N0x 8 . 9
THC 2.7
-------
TABLE 3-10. EMISSIONS FACTORS FOR SOX AND PARTICULATESfl
Emissions
in Grams per HP Hour*
Diesel truck engine Locomotive engine
(GM-71 Series)
Part .3 g/hp hr
S0x .7 g/hp hr
(GM 567 and 645)
.6 g/hp hr
1 .6 g/hp hr
Weighted Emission Factors - Particulates
Engine Types % of Total
GM-71 Series 23%
GM567 5 645 77%
Weighted
Engine Types \ of Total
GM-71 Series 234
GM 567 5 645 77%
Weighted Emission
Emission Factors Factors
.3 g/hp hr .1
.6 g/hp hr _._5_
. 6g/hp hr
Emission Factors - SO
X
Weighted Emission
Emission Factors Factors
'.7 .2
1.6 1.2
1.4 g/hp hr
Emission factors were presented in Ibs of pollutants per thousand
gallons of fuel in the reference source. Emission factors were
converted to grams per hp hour in keeping with the study metho-
dology by using the conversion factor 0.4 Ibs of fuel consumed
per horsepower-hour.
3-21
-------
traffic and a single emission factor for downward-bound traffic,
the uncertainty is less than for other factors and assumptions.
The horsepower-hour calculations are based on the actual estimated
percentages of engine loads to account for some of the possible
differences in emissions due to different loadings.
The emission rates in grams per hour for an engine at idle
are given in Table 3-11.
'TABLE 3-11. EMISSIONS FACTORS AT iDLEflo'12)
400-HP Diesel Engine
CO - 1560 grams/hour
NO., - 95 grams/hour
535 grams/hour
SO., - 27 grams/hour
13 grams/hour
x
THC
x
Part -
3-22
-------
4, EMISSIONS CALCULATIONS
This section presents the emission estimates in tabular form.
Table 4-1 is an index of the tables showing the table numbers for
the various sections of waterway.
Tables 4-2 through 4-17 show the estimated river vessel horse-
power-hours and emissions. The emissions are given for CO, THC,
N0x, SOX and particulates for each grid and are in units of grams
per day.
Table 4-18 summarizes the river vessel emissions for all the
sections of waterway and for the entire SLAPS area. The emissions
associated with the switcher boats at idle were calculated for a
400 hp GM-71 Series engine. This engine is considered representa-
tive of the total switcher boat population operating in the port
area. Emissions of the larger towboats at idle waiting at the locks
are taken in proportion to their horsepower and to the values shown
in Table 3-11.
4-1
-------
TABLE 4-1. EMISSIONS CALCULATION OUTLINE
SECTION
1
2
3
4
5
6
7
8
9
10
DESCRIPTION
Missouri R.
Upper Mississippi
to 111. R.
111. R. to Alton
111. R. to Chain-
of-Rocks C.
Chain-of -Rocks
Canal
Lock 27
St. Louis Port
Area - North
St. Louis Port
Area - South
Below Port Area
Lock 26
TOTAL SLAPS
TYPE OF
TRAFFIC
Through
Through
Through
Through
Local
Through
Local
Through
Local
Through
Local
Switcher Boats
TOTAL (ZONE 7)
Through
Switcher Boats
TOTAL (ZONE 8)
Through
Through
TABLE NO.
HP-
HOURS
4-2
4-4
4-4
4-4
4-4
4-6
4-6
3-4
3-4
4-8
4-8
3-3
4-12
3-3
4-15
3-4
EMISSIONS
4-3
4-5
4-5
4-5
4-5
4-7
4-7
4-17
4-17
4-9
4-9
4-10
4-11
4-13
4-10
4-14
4-16
4-17
4-18
4-2
-------
TABLE 4-2. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY),
MISSOURI RIVER
(HERMANN TO MISSISSIPPI RIVER - SECTION 1)
(2400 HP AVG., UPBOUND 6 mph, 0.75 FULL
POWER DOWNBOUND 10 mph, 0.50 FULL POWER,
MARCH 1 TO NOV. 30)
GRID
NO.
(42
GRIDS)
2,302
2,295
2,287
914
737
733
569
568
392
309
308
288
287
2%
2.149
2,127
2,1.26
2,105
2,104
2.078
2,085
2,03-
2,083
192
160
2,045
135
106
105
87
74
73
6S
59
52
47
2.011
22
14
15
9
4
TOTALS
GRID
DIST.
(KM)
1.3
2.3
2.4
5.7
4.0
2.8
6.5
2.9
6.0
3.7
3.3
3.0
1.1
1.0
.4
1.9
1.985
.885
1.731
2.231
1.038
1.038
1.154
4.692
3.462
2. Oil
5.346
4.769
4.438
5.8
3.7
2.6
5.7
5.6
1.2
1.2
3.5
10.5
3.2
5.2
4.2
4.8
4U.34ft
GRID
DIST.
(MILES)
.807
1.428
1.490
3.540
2.484
1.739
4.037
1.801
3.726
2.298
2.049
1.863
.683
.621
.248
1.180
1.233
.550
1.075
1.385
.645
.645
.717
2.914
2.150
1.290
3.320
2.962
2.756
3.602
2.298
1.615
3.540
3.478
.745
.745
2.174
6.521
1.987
3.229
2.608
2.981
87.159
AVG.
DAILY
VESSELS
UPBOUND
DOWN-
BOUND
1.5
1
i:
i
5
TIME
(HOURS
UP-
BOUND
.135
.238
.248
.590
.414
.290
.673
.300
.621
.383
.342
.311
.114
.104
.041
.197
.206
.092
.179
.231
.108
.108
.120
.486
.358
.215
.553
.494
.459
.600
.383
.269
.590
.580
.124
.124
.362
1.087
.331
.538
.435
.497
HP-
HR
UPBOUND
364.5
642.6
669.6
159.3
1117.8
783.0
1817.1
810.0
1676.7
1034.1
923.4
839.7
307 . 8
280.8
110.7
531.9
556.2
248.4
483.3
623.7
291.6
291.6
324.0
1312.2
966.6
580.5
1493.1
1333.8
1239.3
1620.0
1034.1
726.3
1593.0
1566.0
334.8
334.8
977.4
2934.9
893.7
1452.6
1174.5
11341.9
TIME
(HOURS)
DOWN-
BOUND
.0807
.1423
.1490
.3540
.2484
.1739
.4037
.1801
.3726
.2298
.2049
.1863
.0683
.0621
.0248
.1180
.1233
.0550
.1075
.1385
.0645
.0645
.0717
.2914
.2150
.1290
.3320
.2962
.2756
.3602
.2298
.1615
.3540
.3478
.0745
.0745
.2174
.6521
.1987
.3229
.2608
.2981
HP-
HR
DOWN-
BO fMO
145.26
257.04
268.20
637.20
447.12
313.02
726.66
324. 1£
670.68
413.64
368.82
335.34
122.94
111. 78
44.64
212.40
221.94
99.00
193.50
249.30
116.10
116.10
129.06
524.54
387.00
232.20
597. 60
533.16
496.08
648.36
413.64
290.70
637.20
626.04
134.10
134.10
391.32
L173.78
357.66
581.22
469,44
536.53
4-3
-------
TABLE 4-3. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSOURI RIVER
(HERMANN TO MISSISSIPPI RIVER - SECTION 1, MARCH 1 TO NOV. 30)
GRID
NO.
2,302
2,295
2,287
914
737
738
569
568
392
309
308
288
287
286
2,149
2,127
2,126
2.105
2 104
2,078
2,085
2,084
2,083
192
160
2 045
135
106
105
87
74
73
68
59
52
47
2,011
22
14
15
9
4
TOTALS
THC
1.8 G
PER
HP-HR
UP
656
1,157
1,205
287
2,012
1,409
3,271
1,458
3,018
1,861
1,662
1,511
554
505
199
957
1,001
447
870
1,123
525
525
583
2,362
1,740
1,045
2,688
2,401
2,231
2,916
1,861
1,307
2.867
2,818
603
603
1,760
5,283
1,609
2,615
2,114
2,415
68,035
T!ir
2.7
PER
HP-HR
DOWN
392
694
724
1,720
1,207
845
1,962
875
1,811
1,117
996
905
332
302
121
573
599
267
522
673
313
313
348
1,416
1,045
627
1,614
1,440
1,339
1,751
1,117
785
1,720
1,690
362
362
1,056
3,169
966
1,569
1,267
1,449
42,359
\ox
10.6 G
PER
H°-HR
UP
3,864
6,811
7,098
1,689
11,849
8,300
19,261
8,586
17,773
10,961
9,788
8,901
3,263
2,976
1,173
5,638
5,896
2,633
5,123
6,611
3,091
3,091
3,434
13,909
10,246
6,153
15,827
14,139
13,137
17,172
10,961
7,699
16,886
16,600
3,549
3,549
10,360
31,110
9,473
15,398
12,450
14,224
400,651
N0r
8.9 G
PER
HP-HR
DOWN
1 ,293
2,288
2,387
5,671
3,979
2,786
6,467
2,885
5,969
3,681
3,282
2,984
1,094
995
397
1,890
1,975
881
1,722
2,219
1,033
1,033
1,149
4,668
3,444
2,067
5,319
4,745
4,415
5,770
3,681
2,587
5,671
5,572
1,193
1,193
3,483
10,447
3,183
5,173
4,178
4,776
139,629
CO
3.9 G PER HP-HR
UP DOWN
1,422 567
2,506 1,002
2,611 1,046
621 2,485
4,359 1,744
3,058 1,221
7,087 2,834
3,159 1,264
6,539 2,616
4,003 1,613
3,601 1,438
3,275 1,308
1,200 479
1,095 436
432 174
2,074 828
2,169 866
969 386
1,885 755
2,432 972
1,137 453
1,137 453
1,264 503
5,118 2,046
3,770 1,509
2,264 906
5,823 2,331
5,202 2,079
4,833 1,935
6,318 2,529
4,033 1,613
2,833 1,134
6,213 2,485
6,107 2,442
1,306 523
1,306 523
3,812 1,526
11,446 4,578
3,485 1,395
5,665 2,267
4,581 1,831
5,233 2,093
147,409 61,186
sov
X
1 .4 GR PER HP-HR
UP DOWN
510
900
937
223
1,565
1,096
2,544
1,134
2,347
1,448
1,293
1,176
431
393
155
745
779
348
677
873
408
408
454
1,837
1,353
813
2,090
1,867
1,735
2,268
1,448
1,017
2,230
2,192
469
469
1,368
4,109
1,251
2,034
1,644
1,879
52,916
203
360
375
893
626
438
1,017
45-t
939
579
516
469
172
156
62
297
311
139
271
349
163
163
181
734
542
325
837
746
695
908
579
407
892
876
188
187
548
1,643
501
814
657
751
21,964
PART
.6 G PER HP-HR
UP DOWN
219
386
402
96
671
470
1,090
466
1,006
620
554
504
185
168
66
319
334
149
290
374
175
175
194
787
580
348
896
800
744
972
620
436
956
940
201
201
586
1,761
536
872
705
805
22,678
87
154
161
382
269
188
436
195
402
248
221
201
74
67
27
127
133
59
116
150
70
70
77
315
232
139
359
320
298
389
248
174
382
376
80
80
235
704
215
349
282
322
9,413
-------
TABLE 4-4. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY) MISSISSIPPI RIVER (ALTON TO
CHAIN-OF-ROCKS CANAL - SECTION 4; ILLINOIS RIVER TO ALTON - SECTION 3;
PERUQUE ISLAND TO ILLINOIS RIVER - SECTION 2)
(LOCAL TRAFFIC 1200 HP AVG., THROUGH TRAFFIC 2900 HP AVG.) (UPBOUND -
5 MPH .85 FULL POWER) (DOWNBOUND - 10 MPH, 50 FULL POWER!
SEC.
I
L
4
1
1
i
3
i
i
i
2
\
r
GRID
NO.
1,233
2,302
1,235
1,236
1,237
2,300
1,167
1,133
1,108
1,109
1,086
1,087
1,019
915
977
916
917
739
2,234
394
242
197
166
141
GRID
DIST.
(KM)
2.769
2.077
1.038
1.231
.462
1.654
.615
1.154
.308
1.0
.8
.4
1.1
.3
1.1
1.8
.9
5.3
2.8
3.8
8.9
1.8
.7
2.1
GRID
DIST.
(MILES)
1.720
1.290
.645
.764
.287
1.027
.382
.717
.191
.621
.497
.248
.683
.186
.683
1.118
.559
3.291
1.739
2.360
5.527
1.118
.435
1.304
AVG. DAILY
VESSELS
NORTH & SOUTH
o
M
fe
11 5 2
\
r
{
r-»
5J
U
O
F ^
11
1
r
7
1
r
TIME
(HOURS)
NORTH
.342
.258
.130
.152
.058
.206
.076
.144
.038
.124
.100
.050
.136
.038
.136
.224
.112
.658
.348
.472
1.106
.224
.088
.260
HP-HR
NORTH
11,018
8,311.47
4,187.95
4,896.68
1,868.47
6,636.29
2,448,34
4,638,96
1,224.17
3,994.66
3,221.50
1,610.75
3,687.64
1,030.37
3,687.64
6,073.76
3,036.88
17,841.67
9.436.02
12,798.28
19,084.03
3,865.12
1,518.44
4,486.30
TIME
(HOURS)
SOUTH
.172
.129
.065
.076
.029
.103
.038
.072
.019
.062
- .050
.025
.068
.019
.068
.112
.056
.329
.174
.236
.553
.112
.044
.130
HP-HR
SOUTH
3,259
2,444.55
1,231.75
1,140.20
549.55
1,951.85
720.10
1,364.40
360.05
1,174.90
947.50
473.75
1,084.60
303.05
1,084.60
1,786.40
893.20
5,247.55
2,775.30
3,764.20
5,612.95
1,136.80
446.60
1,319.50
-------
TABLE 4-5. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI RIVER (ALTON
TO CHAIN-OF-ROCKS CANAL - SECTION 4; ILLINOIS RIVER TO ALTON - SECTION
3; PERUQUE ISLAND TO ILLINOIS RIVER - SECTION 2) (LOCAL TRAFFIC 1200 HP
AVG., THROUGH TRAFFIC 2900 HP AVG.) (UPBOUND - 5 MPH, .85 FULL POWER)
(DOWNBOUND - 10 MPH, .50 FULL POWER)
GRID
NO.
1,233
2,302
1,235
1,236
1,237
2,300
1,167
1,133
1,108
1,109
1,086
1,087
1,019
915
977
916
917
739
2,234
394
242
197
166
141
THC
1.8 G
PER
HP-HR
NORTH
19,832
14,961
7,538
8,814
3,363
11,945
4,407
8,350
2,204
7,190
5,799
2,899
6,638
1,855
6,638
10,933
5,466
32,115
16,985
23,036
34,351
6,957
2,733
8,075
THC
2.7 G
PER
HP-HR
SOUTH
8,800
6,600
3,326
3,079
1,484
5,270
1,944
3,684
972
3,172
2,558
1,279
2,928
818
2,928
4,823
2,412
14,168
7,493
10,163
15,155
3,069
1,206
3,563
NOX
10.6 G
PER
HP-HR
NORTH
116,786
88,102
44,392
51,905
19,806
70,345
25,952
49,173
12,976
42,343
34,148
17,074
39,089
10,922
39,089
64,382
32,191
189,122
100,022
135,662
202,291
40,970
16,095
47,555
NOV
8,9 G
PER
HP-HR
SOUTH
29,009
21,756
10,963
10,148
4,891
17,371
6,409
12,143
3,204
10,457
8,433
4,216
9,653
2,697
9,653
15,899
7,949
46,703
24,700
33,501
49,955
10, 118
3,975
11,744
CO
3.9 G PER HP-HR
NORTH SOUTH
42,968
32,415
16,333
19,097
7,287
25,882
9,549
18,092
4,774
15,579
12,564
6,282
14,382
4,019
14,382
23,688
11,844
69,583
36,800
49,913
74,428
15,074
5,922
17,497
12,712
9,533
4,804
4,447
2,143
7,612
2,808
5,321
1,404
4,582
3,695
1,848
4,230
1,182
4,230
6,967
3,483
20,465
10,824
14,680
21,891
4,434
1,742
5,146
SOX
1.4 G PER HP-HR
NORTH SOUTH
15,425
11,636
5,863
6,855
2,610
9,291
3,428
6,495
1,714
5,593
4,510
2,255
5,163
1,443
5,163
8,503
4,252
24,978
13,210
17,918
26,718
5,411
2,126
6,281
4,563
3,422
1,724
1,596
769
2,733
1,008
1,910
504
1,650
1,326
663
1,518
424
1,518
2,501
1,250
7,347
3,885
5,270
7,858
1,592
625
1,847
PART
.6 G PER HP-HR
NORTH SOUTH
6,611
4,987
2,513
2,938
1,121
3,982
1,469
2,783
734
2,397
1,933
966
2,213
618
2,213
3,644
1,822
10,705
5,662
7,679
11,450
2,319
911
2,692
1,956
1,766
739
684
330
1,171
432
819
216
705
568
284
651
182
651
1,972
536
3,149
1,665
2,259
3,368
682
263
792
-------
TABLE 4-6. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY),
MISSISSIPPI RIVER (CHAIN-OF-ROCKS CANAL
TO LOCK 27 LOCALE E - SECTION 5)
(LOCAL TRAFFIC: 1200 AVG. HP, 6 MPH,
.85 FULL POWER)
(THROUGH TRAFFIC: 2900 AVG. HP, 6 MPH,
.33 FULL POWE'R)
GRID
NO.
1,082
1,195
1,166
1,165
1,164
1,081
1,132
1,131
1,080
1,079
GRID
DIST.
(KM)
2,423
.5
.654
1.154
.308
.692
1.192
.269
.846
2.192
GRID
DIST.
(MILES)
1.505
.311
.406
. 717
.191
.430
.740
.167
.525
1.361
AVG. DAILY
VESSELS
NORTH & SOUTH
EACH
1
u
M
fe
t§
1 H I
a
fc
^
i P
11 S 5 Sj
1
t-^
9
P5
E
6"*
r i
w
g
ij
r
TIME
(HR)
NORTH &
SOUTH
.251
.052
.068
.120
.032
.072
.123
.028
.088
.227
HP-HR
85%
NORTH &
SOUTH
2,560.2
530.4
693.6
1,224.0
326.4
734.4
1,254.6
285.6
897.6
2,315.4
HP-HR
33%
NORTH &
SOUTH
5,337.93
1,105.87
1,446.13
2,552.00
680.53
1,531.20
2,615.80
595.47
1,871.47
4,827.53
4-7
-------
TABLE 4-7. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI RIVER (CHAIN-
OF-ROCKS CANAL TO LOCK 27 LOCALE E - SECTION 5)
(LOCAL TRAFFIC: 1200 AVG. HP, 6 MPH, .85 FULL POWER) (THROUGH TRAFFIC:
2900 AVG. HP, 6 MPH, .33 FULL POWER)
GRID
NO.
1082
1195
1166
1165
1164
1081
1132
1131
1080
1079
THC
1.8
G
PER
HP-HR
(.85)
-f
4,608
955
1,248
2,203
588
1,322
2,258
514
1,616
4,168
THC
2.7
G
PER
HP-HR
(.33)
14,412
2,986
3,905
6,890
1,837
4,134
7,063
1,608
5,053
13,034
NOX
10.6
G
PER
HP-HR
(.85)
27,138
5,622
7,352
12,974
3,460
7,785
13,299
3,027
9,515
24,543
NOV
8.5
G
PER
HP-HR
(.33)
47,508
9,842
12,871
22,713
6,057
13,628
23,281
5,300
16,656
42,965
CO
3.9 G PER HP-HR
(.85)
9,985
2,069
2,705
4,774
1,273
2,864
4,893
1,114
3,501
9,030
(.33)
20,818
4,313
5,640
9,953
2,654
5,972
10,202
2,322
7,299
18,827
SOY
X
1.4 G PER HP-HR
(.85)
3,584
743
971
1,714
457
1,028
1,756
400
1,257
3,242
(.33)
7,473
1,548
2,025
3,573
953
2,144
3,662
834
2,620
6,759
PART
.6 G PER HP-HR
(.85)
1,536
318
416
734
196
441
753
171
539
1,389
(.33)
3.203
664
868
1,531
408
919
1,569
357
1,123
2,897
-------
TABLE 4-8. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY),
MISSISSIPPI RIVER (CHAIN-OF-ROCKS CANAL
TO MONSANTO - SECTION 7)
(LOCAL TRAFFIC: 1200 HP AVG. THROUGH
TRAFFIC 2900 HP AVG.)
(UPBOUND - 5 MPH, FULL POWER)
(DOWNBOUND - 10 MPH, FULL POWER)
GRID
NO.
1,040
1,039
1,038
1,074
1,073
1,072
1,071
1,070
1,069
1,068
1,067
1,031
1,030
GRID
DIST.
(KM)
1.0
1.115
.5
.538
1.077
1.0
1.0
1.1
1.0
1.038
.192
.923
1.192
GRID
DIST.
(MILES)
.621
.692
.311
.334
.669
.621
.621
.621
.621
.645
.119
.573
.740
AVG. DAILY
VESSELS
NORTHBOUND
& SOUTHBOUND
u
M C.
f*t h*
£ S
ft Q
I
B "
2 I
B -
3
^
14
\
H
^
3
j
11 5
I \
^
TIME
(HR)
NORTH
.124
.138
.062
.066
.134
.124
.124
.124
.1.24
.130
.024
.114
.148
HP-HR
NORTH
3,994.66
4,445.67
1,997.33
2,126.19
4,316.81
3,994.66
3,994.66
3,994.66
3,994.66
4,187.95
773.16
3,672.51
4,767.82
TIME
(HR)
SOUTH
.062
.069
.031
.033
.067
.062
.062
.062
.062
.065
.012
.057
.074
HP-HR
SOUTH
1,174.90
1,307.55
587.45
625.35
1,269.95
1,174.90
1,174.90
1,174.90
1,174.90
1,231.75
227.40
1,080.15
1,402.30
4-9
-------
TABLE 4-9. TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY), MISSISSIPPI RIVER (CHAIN
-OF-ROCKS CANAL TO MONSANTO - SECTION 7)
(LOCAL TRAFFIC: 1200 HP AVG. THROUGH TRAFFIC: 2900 HP AVG.)
(UPBOUND: 5 MPH, FULL POWER) (DOWNBOUND - 10 MPH, FULL POWER)
GRID
NO.
1,040
1,039
1,038
1,074
1,073
1,072
1,071
1,070
1,069
1,068
1,067
1,031
1,030
THC
1.8 G
PER
HP-HR
NORTH
7,190
8,002
3,595
3,827
7,770
7,190
7,190
7,190
7,190
7,538
1,392
6,611
8,582
THC
2.7 G
PER
HP-flR
SOUTH
3,172
3,530
. 1,586
1,688
3,428
3,172
3,172
3,172
3,172
3,326
614
2,916
3,786
NOX
10.6 G
PER
HP-HR
NORTH
42,343
47,1-24
21,172
22,538
45,758
42,343
42,343
42,343
42,343
44,392
8,195
38,929
50,539
NOX
8.9 G
PER
HP-HR
SOUTH
10,457
11,637
5,228
5,566
11,300
10,457
10,457
10,457
10,457
10,963
2,024
9,613
12,480
CO
3.9 G PER HP-HR
NORTH SOUTH
15,579
17,338
7,790
8,292
16,836
15,579
15,579
15,579
15,579
16,333
3,015
14,323
18,594
4,582
5,099
2,291
2,439
4,952
4,582
4,582
4,582
4,582
4,804
887
4,213
5,469
SO
x
1.4 G PER HP-HR
NORTH SOUTH
5,593
6,224
2,796
2,977
6,044
5,593
5,593
5,593
5,593
5,863
1,082
5,142
6,675
1,645
1,831
822
875
1,778
1,645
1,645
1,645
1,645
1,724
318
1,512
1,963
PART
.6 G PER HP-HR
NORTH SOUTH
2,397
2,667
1,198
1,276
2,590
2,397
2,397
2,397
2,397
2,513
464
2,204
2,861
705
785
352
375
762
705
705
705
705
739
136
648
841
-------
TABLE 4-10.
TOTAL EMISSIONS FOR SWITCHBOATS GRAMS
PER GRID PER DAY
Grid
1010
1073
1072
1069
1038
1032
1031
1030
999
998
955
924
849
848
847
815
CO
11,520
8,640
11,520
8,640
4,320
8,640
8,640
11,520
8,640
12,960
17,280
5,760
4,320
5,760
8,640
5,760
NOX
12,168
9,126
12,168
9,126
4,563
9,126
9,126
12,168
9,126
13,689
18,252
6,084
4,563
6,084
9,126
6,084
THC
6,600
4,950
6,600
4,950
2,475
4,950
4,950
6,660
4,950
7,425
9,900
3,300
2,475
3,300
4,950
3,300
S0x
957
718
957
718
359
718
718
957
718
1,077
1,436
479
359
479
718
479
Part
425
319
425
319
159
319
319
425
319
478
638
213
159
213
319
213
4-11
-------
TABLE 4-11.
TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY)
MISSISSIPPI RIVER - ST. LOUIS PORT AREA,
NORTH (CHAIN-OF-ROCKS CANAL TO MONSANTO -
SECTION 7)
EMISSIONS SUM OF: LOCAL TRAFFIC AND
THROUGH TRAFFIC (TABLE 4-9)
AND SWITCHER BOATS (TABLE 4-10)
GRID
NO.
1,040
1,039
1,038
1,074
1,073
1,072
1,071
1,070
1,069
1,068
1,067
1,031
1,030
1,032
999
THC
10,362
11,532
7,656
5,515
16,148
16,963
10,362
10,362
15,313
10,864
2,006
14,477
19,028
4,950
4,950
NOX
52,800
58,761
30,963
28,104
66,184
64,968
52,800
50,800
61,926
55,355
10,219
57,'768
75,187
9,126
9,126
CO
20,161
22,437
14,401
10,731
30,427
31,681
20,161
20,161
28,801
21,137
3,902
27,175
35,583
8,640
8,640
S°x .
7,238
8,055
3,978
3,852
8,539
8,194
7,238
7,238
7,955
7,587
1,400
7,372
9,595
718
718
PART.
3,102
3,452
1,710
1,651
3,671
3,527
3,102
3,102
3,421
3,252
600
3,171
4,127
319
319
4-12
-------
TABLE 4-12.
TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY),
MISSISSIPPI RIVER (MONSANTO TO SUGAR LOAF -
SECTION 8)
(2900 AVG. HP, UPBOUND 5 MPH, .85 FULL POWER)
DOWNBOUND 10 MPH, .50 FULL POWER)
NO.
998
956
955
925
924
923
888
887
849
848
847
815
GRID
DIST.
(KM)
1.423
.115
1.231
.385
1.192
.269
1.0
.962
.231
1.154
.923
.231
GRID
DIST.
(MILES)
.884
.071
.764
.239
.740
.167
.621
.597
.143
.717
.573
.143
AVG. DAILY
VESSELS
NORTHBOUND &
SOUTHBOUND
12
1
r
TIME
(HOURS)
NORTH
.176
.014
.152
.048
.148
.034
.124
.120
.028
.144
.114
.028
HP-HR
NORTH
5,206.08
414.12
4,496.16
1,419.84
4,377.84
1,005.72
3,667.92
3,549.60
828.24
4,259.52
3,372.12
828.24
(HOURS)
SOUTH
.088
.007
.076
.024
.074
.017
.062
.060
.014
.072
.057
.014
HI>-HR
SOUTH
1,530.59
121.75
1,321.87
417.43
1,287.08
295.68
1,078.37
1,043,58
243.50
1,252.30
991.40
243.50
4-13
-------
TABLE 4-13.
TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY), MISSISSIPPI RIVER
(MONSANTO TO SUGAR LOAF - SECTION 8)
(2900 AVG. HP, UPBOUND 5 MPH, .85 FULL POWER) (DOWNBOUND 10 MPH,
.50 FULL POWER)
GRID
NO.
998
956
955
925
924
923
888
887
849
848
847
815
N0x.
10.6 G
PER
HP-HR
UP
55,184
4,390
47,659
15,050
46,405
10,661
38,880
37,626
8,779
45,151
35,744
8,779
THC
1.8 G
PER
HP-HR
UP
9,371
745
8,093
2,551
7,880
1,810
6,602
6,389
1,491
7,667
6,070
1,491
NOX
8.9 G
PER
HP-HR
DOWN
13,622
1,084
11,765
3,715
11,455
2,632
9,597
9,288
2,167
11,145
8,823
2,167
THC
2.7 G
PER
HP-HR
DOWN.
4,133
329
3,569
1,127
3,475
798
2,912
2,818
657
3,381
2,677
657
CO
3.9 G PER HP-HR
UP DOWN
20,304
1,615
17,535
5,537
17,074
3,922
14,305
13,843
3,230
16,612
13,151
3,230
5,969
475
5,155
1,628
5,020
1,153
4,206
4,070
950
4,884
3,866
950
S0x
1.4 G PER HP-HR
UP DOWN
7,289
580
6,295
1,988
6,129
1,408
5,135
4,969
1,160
5,963
4,721
1,160
2,143
170
1,851
584
1,802
414
1,510
1,461
341
1,753
1,388
341
PART
.6 G PER HP-HR
UP DOWN
3,124
248
2,698
852
2,627
603
2,201
2,130
497
2,556
2,023
497
918
73
793
250
772
177
647
626
146
751
595
146
-------
TABLE 4-14.
TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY)
MISSISSIPPI RIVER - ST. LOUIS PORT AREA.
SOUTH (MONSANTO TO SUGAR LOAF - SECTION 8)
(EMISSIONS SUM OF: THROUGH TRAFFIC (TABLE 4-13)
AND SWITCHER BOATS (TABLE 4-10)
GRID
NO.
998
956
955
925
924
923
888
887
849
848
847
815
THC
20,929
1,074
21,562
3,678
14,655
13,293
9,514
9,207
4,623
14,348
13,697
5,448
NOX
82,496
5,474
77,676
18,765
63,944
2,608
48,477
46,914
15,509
62,380
53,694
17,030
CO
39,233
2,090
39,970
7,165
27,853
5,075
18,511
17,913
8,500
27,256
25,658
8,990
S0x
10,508
750
9,581
2,572
8,410
1,822
6,645
6,430
1,859
8,196
6,827
1,979
PART
4,520
321
4,129
1 ,102
3,612
780
2,857
2,756
802
3,520
2,937
856
4-15
-------
TABLE 4-15.
TOWBOAT TRAFFIC (HP-HR PER GRID PER DAY),
MISSISSIPPI RIVER (SUGAR LOAF TO ROCKWOOD
ISLAND - SECTION 9)
(2900 AVG. HP UPBOUND 5 MPH, .85 FULL POWER)
(DOWNBOUND 10 MPH, .50 FULL POWER)
GRID
NO.
814
813
2,260
2,261
699
2,248
2,237
2,236
2,218
2,233
2,232
2,203
355
503
529
528
527
877
1,203
1,579
2,410
2,409
2,414
2,413
2,417
2,412
2,419
2,430
2,429
2,437
1,685
GRID
DIST.
(KM)
1.077
2.038
3.077
2.038
5.385
1.692
4.7
1.2
1.0
1.0
1.6
.5
5.1
1.1
4.2
6.2
8.1
5.638
3.692
12.7
.4
1.9
.4
.9
.7
.7
2.3
1.2
.9
2.4
8.331
GRID
DIST.
(MILES)
.699
1.266
1.911
1.266
3.344
1.051
2.919
.745
.621
.683
.994
.311
3.167
.683
2.608
3.850
5.03
3.501
2.293
7.887
.248
1.180
.248
.559
.435
.435
1.428
.745
.559
1.490
5.174
AVG.
DAILY
VESSELS
UPBOUND,
DOWNBOUND
12
'*!
12
i
r
TIME
(HPURS)
UPBOUND
.134
.254
.382
.254
.668
.210
.584
.150
.124
.136
.198
.062
.634
.136
.522
.770
1.006
.700
.458
1.578
.050
.236
.050
.112
.088
.088
.286
.150
.112
.298
1.034
HP-HR
UPBOUND
3,963.72
7,513.32
11,299.56
7,513.32
19,759.44
6,211.80
17,274.72
4,437.00
3,667.92
4,022.88
5,856.84
1,833.96
18,753.72
4,022.88
15,440.76
22,776.60
29,757.48
20,706.00
13,547.64
46,677.24
1,479.00
6,980.88
1,479.00
3,312.96
2,603.04
2,603.04
8,459.88
4,437.00
3,312.96
8,814.84
30,585.72
TIME
(HOURS)
DOWNBOUND
.067
.127
.191
.127
.334
.105
.292
.075
.062
.068
.099
.031
.317
.068
.261
.385
.503
.350
.229
.789
.025
.118
.025
.056
.044
.044
.143
.075
.056
.149
.517
HP-HR
DOWNBOUND
1,165.33
2,208.92
3,322.07
2,208.92
5,809.28
1,826.27
5,078.77
1,304.48
1,078.37
1,182.73
1,721.91
539.18
5,513.59
1,182.73
4,539.58
6,696.32
8,752.20
6,090.00
3,983.01
13,728.60
435.00
2,053.20
435.00
974.40
765.60
765.60
2,488.20
1,305.00
974.40
2,592.60
8,995.8
4-16
-------
TABLE 4-16.
TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY) MISSISSIPPI RIVER (SUGAR LOAF TO
ROCKWOOD ISLAND - SECTION 9) 2900 AVG. HP, UPBOUND 5 MPH, .85 FULL
POWER DOWNBOUND 10 MPH, .50 THROTTLE FULL POWER.
GRID
NO.
814
813
2,260
2,261
699
2,248
2,237
2,236
2,218
2,233
2,232
2,203
355
503
529
528
527
877
1,203
1,579
2,410
2,409
2,414
2,413
2,417
2,412
2,419
2,430
2,429
2,437
1,685
THC
1.8 G
PER
HP-HR
UP
7,135
13,524
20,339
13,524
35,567
11,181
31,095
7,987
6,602
7,241
10,542
3,301
33,757
7,241
27,793
40,998
53,563
37,271
24,386
84,019
2,662
12,566
2,662
5,963
4,685
4,685
15,228
7,987
5,963
15,867
55,054
THC
2.7 G
PER
HP-HR
DOWN
3,146
5,964
8,970
5,964
15,685
4,931
13,713
3,522
2,912
3,193
4,649
1,456
14,887
3,193
12,257
18,080
23,631
16,443
10,754
37,067
1,175
5,544
1,175
2,631
2,067
2,067
6,718
3,524
2,631
7,000
24,289
NO
10.6 G
PER
HP-HR
UP
42,015
79,641
119, ^75
79,641
209,450
65,845
183.112
47,032
38,880
42,643
62,083
19,440
198.789
47,643
163,672
241,432
315,429
219,484
143,605
494,779
15,677
73,997
15,677
35,117
27,592
27,592
89,675
47,032
35,117
93,437
324,209
NOX
8.9 G
PER
HP-HR
DOWN
10,371
19,659
29,566
19,659
51,703
16,254
45,201
11,610
9,597
10,526
15,325
4., 799
49,071
10,526
40,402
59,597
77,895
54,201
35,449
122,184
3,872
18,273
3,872
8,672
6,814
6,814
22,154
11,615
8,672
23,047
80,063
CO
3.9 G PER HP-HR
UP DOWN
15,459
29,302
44,068
29,302
77,062
24,226
67,371
17,304
14,305
15,689
22,847
7,152
73,140
15,689
60,219
88,829
116,054
80,753
52,836
182,041
5,768
27,225
5,768
12,921
10,152
10,152
32,994
17,304
12,921
34,378
119,284
4,545
8,615
12,956
8,615
22,656
7,122
19,807
5,087
4,206
4,613
6,715
2,103
21,503
4,613
17,704
26,116
34,134
23,751
15,534
53,542
1,697
8,007
1,697
3,800
2,986
2,986
9.704
5,090
3,800
10,111
35,084
S0x
1.4 G PER HP-HR
UP DOWN
5,549
10,519
15,819
10,519
27,663
8,697
24,185
6,212
5,135
5,632
8,200
2,568
26,255
5,632
21,617
31,887
41,660
28,988
18,967
65,348
2,071
9,773
2,071
4,638
3,644
3,644
11,844
6,212
4,638
12,341
42,820
1,631
3,092
4,651
3,092
8,133
2,557
7,110
1,826
1,510
1,656
2,411
755
7,719
1,656
6,355
9,375
12,253
8,526
5,576
19,220
609
2,874
609
1,364
1,072
1,072
3,483
1,827
1,364
3,630
12,594
PART
.6 G PER HP-HR
UP DOWN
2,378
4,508
6,780
4,508
11,856
3,727
10,365
2,662
2,201
2,414
3,514
1,100
11,252
2,414
9.264
13,666
17,854
12,424
8,129
28,006
887
4,189
887
1,988
1,562
1,562
5,076
2,662
1,988
5,289
18,351
699
1,325
1,993
1,325
3,486
1,096
3,047
783
647
710
1,033
324
3,308
710
2,724
4,018
5,251
3,654
2,390
8,- 237
261
1,232
261
585
459
459
1,493
783
585
1,556
5,397
-------
TABLE 4-17. TOWBOAT EMISSIONS (GRAMS PER GRID PER DAY)
FOR LOCK 26 - SECTION 10, AND LOCK 27 -
SECTION 6, ENGINES AT IDLE
SECTION
10
10
6
6
GRID
1019
1048
1010
1078
LOCK
26
26
27
27
N0x
37 ,895
37,895
27,012
27,012
THC
213,290
213,290
152,049
152,049
CO
622,050
622,050
443,430
443,430
S0x
10,780
10,780
7,683
7,683
PART
5,170
5,170
3,687
3,687
4-18
-------
TABLE 4-18.
RIVER VESSEL EMISSIONS IN ST. LOUIS AIR POLLUTION STUDY
REGION, BY ZONE (GRAMS PER GRID PER DAY)
Q-PPTTflN
OCiL> J- -LwH
1
2
3
4
5
6
7
8
9
10
TOTALS
N0»
X
UP DOWN TOTAL
400,651
306,911
571,389
573,002
157,767
27,012
490,364
354,309
3,594,513
37,895
6,513,814
139,629
75,791
141,103
139,000
157,767
27,012
121,094
87,461
887,430
37,895
1,814,235
540,280
382,702
712,492
712,002
315,534
54,024
611,458
441,770
4,481,996
75,790
8,328,049
THC
UP DOWN TOTAL
68,035
52,117
97,028
97,302
40,201
152,049
83,269
60,166
610,389
213,290
1,473,847
42,359
22,992
42,807
42,169
40,201
152,049
36,737
26,533
269,238
213,290
888,375
110,394
75,109
139,835
139,471
80,403
304,098
120,006
86,699
879,627
426,580
2,362,222
SECTION
1
2
3
4
5
6
7
8
9
10
TOTALS
sox
UP DOWN TOTAL
52,916
40,535
75,466
75,679
23,370
7,683
64,765
46,796
478,748
10,780
876,830
21,964
11,922
22,196
21,865
23,370
7,683
19,049
13,753
139,602
10,780
292,189
74,880
52,458
97,662
97,545
46,741
15,366
83,814
60,553
618,350
21,560
1,169,019
UP DOWN TOTAL
22,678
17,372
32,343
32 ,434
10,016
3,687
27,756
20,055
203,463
5,170
374,975
9,413
5,110
9,513
9,371
10,016
3,687
8,164
5,896
59,831
5,170
126,170
32,092
22,482
41,855
41,805
20,032
7,374
35,920
25,951
263,294
10,340
501,145
CO
UP
147,409
112,920
210,228
210,821
65,103
443,430
180,417
130,359
1,322,509
622,050
3,445,247
DOWN TOTAL
61,186
33,212
61,832
60,910
65,103
443,430
53,064
38,325
388,898
622,050
1,828,010
208,595
146,132
272,060
271,732
130,206
886,860
233,481
168,685
1,711,407
1,244,100
5,273,257
-------
5, SUMMARY AND CONCLUSIONS
5.1 DATA BASE
Vessel exhaust emissions estimates in this study are consid-
ered to be sufficiently comprehensive for the specific area in-
volved. Limitations encountered which influence the conclusions
include the availability of river vessel traffic information,
appropriate vessel duty cycles, and operational characteristics.
Towboat traffic estimates south of St. Louis were based on
aggregate estimates of traffic on the Mississippi between the
Missouri and Ohio Rivers, and are not as accurate as actual vessel
counts. Corps of Engineers Lock Number 27 has served as the source
of information for vessel traffic north of St. Louis. Port traffic
allocations are based upon the total port traffic estimates from
industry spokesmen and the fact that traffic density within each
port grid element is based on the number of terminals within that
grid. If these assumptions differ substantially from the actual
conditions, the grid elements emission estimates will be erroneous.
It was the author's intent to show the significance of switchboat
operations, and the methodology and data used were considered the
best available.
The critical assumption affecting the calculated emissions are
the towboat power setting (duty cycles). Any major deviation from
the assumed values of power settings for towboats will cause an
almost direct proportional change in exhaust emissions. Similarly,
any change in the assumed duty cycle for port traffic will affect
the calculated emissions for the respective grids.
5.2 RESULTS
In Table 5-1 vessel emissions per mile of the various zones
of waterway in the SLAPS region are shown. The effects of waiting
periods with the towboat engine at idle and time going through the
locks result in high THC and CO emissions for zone 6 (Lock 27) and
zone 10 (Lock 26). The emission rates per mile allow quick
5-1
-------
comparison with other point and area emission sources.
In Table 5-2, vessel emissions relative to other emission
sources are shown. On a percentage basis for the entire AQCR,
towboat emissions are minor. Upon the completion of the EPA-RAPS
emission inventory, the relative towboat emissions on a per-grid
will be available.
5-2
-------
TABLE 5-1. RIVER VESSEL EMISSIONS (GRAMS PER RIVER
MILE PER DAV)
SECTION
1
2
3
4
5
6
7
8
9
10
NCI
A
6,199
45,639
71,364
84,764
49,773
36,478
85,024
78,064
78,064
55,332
THC
1,267
8,957
14,006
16,646
12,683
205,333
16,687
15,320
15,320
312,257
CO
2,393
17,427
27,250
32,372
20,539
598,825
32,466
29,808
29,808
910.806
S0x
859
6,256
9,782
11,621
7 ,37^
10,375
11,654
10,700
10,700
15,784
PART
368
2,681
4,192
4,980
2,266
4,979
4,995
4,586
4,586
7,570
TABLE 5-2.
ANNUAL EMISSIONS FOR ST. LOUIS AIR QUALITY
CONTROL REGION (#70)2
Emissions Source
Towboats*
Transportation^ '
Total Emission'15'
Pollutant
Tons/Year
N0x
' 3.2P7
105,932
433,637
THC
939
198,063
295,124
CO
2,101
980,944
3,852,753
S0x
4<>:
7,887
1,234,395
Part
198
8,940
354,672
This study.
-'Emissions for period of towboat activity on Missouri River
(Zone 1), March 1 to November 30.
'Annual towboat emissions based on 9 months towboat activity on
Missouri River and 12 months activity for rest of region.
5-3
-------
6, REFERENCES
1. Inland Waterborne Commerce Statistics, 1971, The American
Waterways Operators, Inc. Washington DC, 1972.
2. St. Louis Region 1971 Annual Transportation Report, East-West
Gateway Coordinating Council, St. Louis, Missouri, 1972.
3. Study of the Port of Metropolitan St. Louis, Phase One,
Final Report, East-West Gateway Coordinating Council,
St. Louis Missouri, February 1974.
4. Inland River Record, 1972, The Waterways Journal, St. Louis,
Missouri, 1972.
5. Waterborne Commerce of the United States, Part 2 "Waterways
and Harbors - Gulf Coast, Mississippi River System and
Antilles," U.S. Army Corps of Engineers, Vicksburg MS, 1970.
6. "List of Mississippi River Terminals, Docks, Mooring Lo-
cations, and Warehouses," U.S. Army Corps of Engineers,
St. Louis District, St. Louis MO December 1972.
7. Naviagation Conditions for 1975, Division Bulletin No. 2,
Corps of Engineers, North Central Division, Chicago, Illinois,
March, 1973.
8. Walter, Robert A., U.S. Coast Guard Pollution Abatement Pro-
gram: A Preliminary Report on the Emissions Testing of Boat
Diesel Engines, Report No. CG-D-21-74, Transportation Systems
Center, November 1973.
9. Souza, Anthony F., A Study of Stack Emissions From Coast
Guard Cutters, Scott Research Laboratories Inc., Plumstead-
ville PA, September 1973.
10. Hare, Charles T., Karl J. Springer, Exhaust Emissions From
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines, Part I "Locomotive Diesel Engines and
Marine Counterparts," Southwest Research Institute,
San Antonio TX, October 1972.
6-1
-------
11. Report on Exhaust Emissions of Selected Railroad Diesel
Locomotives, Southern Pacific Transportation Company,
San Francisco, CA, March 1972.
12. Compilation of Air Pollutant Emission Factors, U.S.
Environmental Protection Agnecy, Research Triangle Park,
NC, February 1972.
13. National Emissions Data Service print-out for the ACQR 70
(St. Louis), dated April 2, 1974, National Air Branch, EPA
Research Triangle Park, NC 27711 (1974).
6-2
-------
EPA-450/3-75-048
EMISSION INVENTORY
by
R. M. Patterson, R. D. Wang,
and F. A. Record
GCA Corporation
GCA/Technology Division
Bedford, Massachusetts 01730
Contract No. 68-02-0041
Task 18
EPA Project Officer: Charles C. Masser
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
December 1974
-------
December 1974
AIRPORT EMISSION INVENTORY METHODOLOGY
by
Robert M. Patterson
Richard D. Wang
Frank A. Record
GCA CORPORATION
GCA/TECHNOLOGY DIVISION
Bedford, Massachusetts
Contract No. 68-02-0041
Task Order No. 18
U.S. ENVIRONMENTAL PROTECTION AGENCY
Research Triangle Park
North Carolina 27711
-------
"This report has been reviewed by the Office of Research and Monitoring,
EPA, and approved for publication. Approval does not signify that the
contents necessarily reflect the views and policies of the Environmental
Protection Agency, nor does mention of trade names or commercial products
constitute endorsement or recommendation for use."
-------
ABSTRACT
This report describes a methodology for performing emission inventories
at airports, with specific focus on the airports in the St. Louis
AQCR. This work was performed in support of EPA's RAPS program.
Within the basic methodology, three submethodologies are presented
corresponding to municipal, military, and civilian airports. Data col-
lection and handling requirements are discussed, and data for the air-
ports in the St. Louis AQCR are presented. The sensitivity of emission
estimates to improved knowledge of data inputs is discussed.
ii
-------
CONTENTS
Page
Abstract ii
List of Figures iv
List of Tables v
Sections
I Introduction 1
II Emission Inventory Needs for RAPS 3
III Factors Contributing to Airport Emissions 5
IV Levels of Emission Inventory Detail 18
V Emission Estimation Methodology for Lambert Field 22
VI Scott Air Force Base 51
VII Civilian Airports 59
VIII Methodology Summary 86
DC Improving Estimates 98
X References 100
iii
-------
FIGURES
No. Page
1 Interacting Factors Affecting Emissions Production 7
at a Municipal Airport
2 Hourly Percent of Total Daily LTO Volume for a Typical 9
Municipal Airport
3 Interacting Factors Affecting Emissions Production at 12
a Civilian Airport
4 Lambert - St. Louis International Airport 34
5 Runway Layout and Grid Element Overlay for Scott AFB 54
6 Diagram of Civic Memorial Airport Showing Grid Element 63
Overlay
.? Diagram of Spirit of St. Louis Airport showing grid e 65
element overlay
8 Diagram of Bi-State Parks Airport Showing Grid Element 67
Overlay
9 Diagram of St. Clair Airport Showing Grid Element 69
Overlay
10 Diagram of Creve Coeur Airport Showing Grid Element 71
Overlay
11 Diagram of Sparta Airport - Grid Element 1633 73
12 Diagram of Wentzville Airport - Grid Element 76 75
13 Diagram of Arrowhead Airport - Grid Element 2102 76
14 Diagram of St. Charles Airport - Grid Element 241 77
15 Diagram of Weiss Airport - Grid Element 2161 78
16 Diagram of Festus Airport - Grid Element 467 79
17 Diagram of St. Charles Smartt Airport - Grid Element 242 80
18 Diagram of Highland Airport - Grid Element 1709 81
19 Diagram of Gebhardt Airport - Grid Element 883 82
20 Diagram of Greenville Airport - Grid Element 1815 83
iv
-------
TABLES
No^ Page
1 Aircraft Operating Modes 6
2 Aircraft Classifications and Representative Aircraft 10
3 Service Vehicles Used at a Municipal Airport 15
4 Percent Emissions Contribution by Source at O'Hare 21
Airport - 1970
5 FAA Classification of Daily Air Traffic Operations 24
6 Average Hourly Air Traffic Volumes at Lambert Field, 24
St. Louis, for May and November, 1972
7 Monthly Air Traffic at Lambert Field, St. Louis, for 26
December 1972 and January - November 1973
8 Air Traffic Volumes by Day of Week at Lambert Field, 26
St. Louis, for December 1972 and January - November 1973
9 Percent of Total Annual Air Traffic by Month at Lambert 27
Field
10 Percent of Total Air Traffic by Day of Week for Lambert 27
Field, St. Louis
11 Percent of Total Daily Movements by Hour at Lambert 28
Field
12 Percent of Departures and Arrivals by Air Carrier 30
Traffic by Hour of the Day
13 Operating Modes for Each Grid by Active Runway at 32
Lambert Field, St. Louis
14 Times in Mode by Grid by Mode for Air Traffic Using 36
Runway 30L (seconds)
15 Times in Mode by Grid by Mode for Air Traffic Using 37
Runway 12R (seconds)
16 Times in Mode by Grid by Mode for Air Traffic Using 38
Runway 30R (seconds)
17 Times in Mode by Grid by Mode for Air Traffic Using 38
Runway 12L (seconds)
-------
TABLES
No. Page
18 Times in Mode by Grid by Mode for Air Traffic Using 39
Runway 35 (seconds)
19 Times in Mode by Grid by Mode for Air Traffic Using 39
Runway 17 (seconds)
20 Times in Mode by Grid by Mode for Air Traffic Using 40
Runway 6 (seconds)
21 Times in Mode by Gri'd by Mode for Air Traffic Using 40
Runway 24 (seconds)
22 Aircraft and Engine Volumes for Lambert Field, St. Louis 41
23 Emission Factors by Engine Type and Mode for Air 42
Carriers (kg/hr)
24 Composite Emission Factors for Air Taxi, General 43
Aviation, and Military Aircraft at Lambert Field (kg/hr)
25 Service Times of Aircraft Ground Service Vehicles 45
26 Ground Service Vehicle Fuel Consumption Rates 47
27 Ground Service Vehicle Emission Factors 48
28 Ninety-Four Year Average High, Medium, and Low 48
Temperatures for St. Louis (°F)
29 Working Loss Factors for the Three Time Periods, for 50
Each Month
30 Five-Month Air Traffic Volumes, Means, and Standard 51
Deviations at Scott AFB, 1973 - 1974
31 Percent of Air Traffic by Day of Week at Scott AFB 52
32 Percent of Air Traffic by Hour at Scott AFB 53
33 Time in Mode by Grid and Mode for Aircraft Using Runway 55
13, Scott AFB (seconds)
34 Time in Mode by Grid and Mode for Aircraft Using Runway 55
31, Scott AFB (seconds)
35 Emission Factors for Scott AFB (kg/hr) 56
vi
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TABLES
No. Page
36 Ground Service Vehicles Used at Scott AFB 57
37 Percent of Air Traffic by Month at Civic Memorial 60
Airport
38 Percent of Air Traffic by Day of Week at Civic 60
Memorial Airport
39 Percent of Air Traffic by Hour of the Day at Civic 61
Memorial Airport
40 Annual Air Traffic Volumes at Civilian Airports in the 61
St. Louis AQCR
41 Operating Modes for Each Grid by Active Runway at Civic 64
Memorial Airport
42 Operating Modes for Each Grid by Active Runway at 66
Spirit of St. Louis Airport
43 Key Operating Modes for Each Grid for Each Runway at 68
Bi-State Parks Airport
44 Key Operating Modes for Each Grid for Each Runway .at 70
St. Clair Airport
45 Key Operating Modes for Each Grid for Each Runway at 72
Creve Coeur Airport
46 General Aviation Airports Contained in One Grid, 74
St. Louis
47 Times in Mode for General Aviation Aircraft at Civilian 84
Airports
48 Annual Volumes of Fuel Sales at the General Aviation 84
Airports
vii
-------
SECTION I
INTRODUCTION
Under the charges of the Clean Air Act of 1970, the Environmental Pro-
tection Agency is assisting state and local pollution control agencies
in developing implementation strategies to meet the established air
quality standards. A basic premise of these efforts is that operation-
ally a cause and effect relationship between pollution sources and air
quality can be accurately specified. The EPA is conducting the Regional
Air Pollution Study (RAPS) in St. Louis to determine the current relia-
bility of this premise, and.to provide for improvements where accuracy
is less than adequate.
To achieve this goal, RAPS will engage in extensive analysis of the at-
mospheric dispersion and transformation process modeling links between
emissions and air pollution levels. The cause and effect data required
to analyze these modeling links include detailed temporal and spatial
emission inventories; atmospheric data such as wind fields and tempera-
ture profiles for dispersion calculations, and insolation data for
transformation process modeling; and air pollutant concentration data
against which modeling results will be compared.
A crucial phase of this program is the adequate and accurate specifica-
tion of emission inventories at least to the level of detail engaged by
the models - the results of these deterministic links can be no more
comprehensive and consistent than the initial input values.. Emissions
inventories have been made by county in the St. Louis Air Quality Con-
trol Region according to the Nation Emissions Data System (NEDS). Air-
-------
Aircraft operations were surveyed for yearly landing and takeoff
cycle volumes for each type of airport, and a single emission factor
based on type of airport was applied to each to calculate annual
emissions. The spatial and temporal detail involved is insufficient
for uses other than trend estimates of emissions. This report
describes techniques for inventorying airport emissions from air-
craft and ground support vehicles and processes as an aid to achieve
the RAPS goals.
2
-------
SECTION II
EMISSION INVENTORY NEEDS FOR RAPS
The St. Louis Interstate Air Quality Control Region is subdivided into
a grid system for the RAPS study. The smallest grid side is 1 km, so
that an airport may not be wholly enclosed in a single grid. This, and
the requirement of hourly average emissions data, dictates the develop-
ment of more spatially and temporally detailed emission inventory data
and methodologies than are currently available.
This report describes the available data and techniques and outlines
further refinements of methodologies for inventorying airport emissions.
The sources involved include aircraft operations and engine maintenance
testing, ground support vehicles, and fuel storage and handling. For
these sources there needs to be described:
emission rate
emission location
emission duration
The task of developing and analyzing emission inventory methodologies
for these sources can be divided into three sub-tasks based on the type
of airport in question; that is, inventories for municipal, civilian,
and military airports. The methods for inventorying each are similar
and reduce to finding the three factors listed above. However, the
types of sources and their significance is a function of the type of
airport. The municipal airport has principally commercial jets, ground
support vehicles for servicing and fueling these aircraft, jet fuel
handling and storage, and testing of jet engines. Civil airports pri-
marily carry private and charter piston aircraft, ground support to the
-------
extent of fueling trucks (absent at the smaller airports), gasoline
storage and handling (with some jet fuel at larger airports), and test-
ing of piston engines. The military airport operations consist mainly
of jet aircraft, fueling trucks, jet fuel handling and storage, and jet
engine testing.
-------
SECTION III
FACTORS CONTRIBUTING TO AIRPORT EMISSIONS
The purpose of this section is to outline the factors contributing to
airport emissions and to discuss how they are interrelated. This is
presented to provide an overview of the inventory problem for airports
and to provide a basis from which to examine alternative levels of
inventory detail.
^
The factors contributing to airport emissions are those involved with
the previously listed sources of aircraft operation, ground support
vehicles, fuel storage and handling, and engine maintenance testing.
These factors are discussed for each type of airport in the following
sections.
FACTORS AFFECTING FLIGHT OPERATION EMISSIONS
Flight operations consist of the modes listed in Table 1. To determine
emissions, two basic factors must be known: (1) the time spent in each
mode, and (2) the emission rate for each mode. The interacting factors
determining these basic factors are outlined below.
i
Municipal Airport Flight Operations
Figure 1 is a diagram showing the interactions of factors affecting
emission production at a municipal airport. These will be sorted accord-
ing to significance of contribution and availability of information
when levels of emission inventory detail and relative emission contribu-
tions are discussed.
5'''
-------
Table 1. AIRCRAFT OPERATING MODES2
Mode
Engine operating
time included in mode
Taxi
Idle
Landing
Takeoff
Approach
Climb-out
Transit times between ramp and apron, apron
and runway and time required for turning and
alignment between taxiway and runway.
Push back from gate; waiting for signal to
begin taxiing; waiting at taxiway intersec-
tions; runway queuing; gate queuing.
Touchdown to beginning of taxi on taxiway.
After alignment with runway to liftoff.
3000 ft altitude to touchdown.
Liftoff to 3000 ft altitude.
-------
I TIME OF DAY \-
| DAY OF WEEK f
MONTH
PASSENGER DEMAND VOLUME
LTO VOLUME
AIRCRAFf MIX
QUEUING
TIME IN MODE
EMISSIONS
FREIGHT DEMAND
ORIGIN-DESTINATION
REQUIREMENTS
AIRLINE
TERMINAL
LOCATION
TERMINAL-
RUNWAY
DISTANCE
SPECIAL LTO
PROCEDURES
AND PATHS
POWER
REQUIREMENTS
EMISSIONS PER
TIME IN MODE
Figure 1. Interacting factors affecting emissions production
at a municipal airport
-------
The major impetus to flight operations is the passenger demand volume.
Fluctuations in demand volume occur with time of day, the day of the
week, and the month. Figure 2 shows the hourly percent of the total
LTO volume for a typical airport. Airline schedules and schedule changes
reflect these fluctuations. Freight demands are shown in Figure 1 as
being secondary to passenger demands as cargo needs are usually accom-
modated on passenger flights.
Landing and takeoff cycle volume is then determined by passenger demand.
Origin-destination require'ments and LTO volume determine the mix of
equipment, which in turn feeds back to LTO volume. Short, low passenger
demand trips will be made by medium and short range aircraft; longer,
high demand trips will use long range and jumbo jets. The aircraft
classes and representative aircraft within each class are listed in
Table 2.
If the LTO volume is greater than some number characteristic of the air-
port and runway in use queues will form. The EPA report, "Air Pollu-
2
tion Impact Methodology for Airports," (APTD-1470) recommends adding
extra idle time due to queuing as T = (N-30)/10 when the LTO volume
exceeds 30 per hour. T is the time queued in minutes and N is the LTO
volume. This relationship assumes the use of two parallel runways and
is empirically based on experience at Chicago's O'Hare airport. For
more nearly accurate idle mode emission calculations, similar relation-
ships should be determined for St. Louis, since in addition to LTO
volumes, queue times can depend on airport configuration and runway in
use, approach path radio aids, weather, and even the air traffic con-
troller.
The LTO volume affects queuing which affects the time spent in the idle
mode. Other "times in modes" are influenced by additional factors as
shown in Figure 1.
-------
o
u.
o
o
UJ
U
Q£
UJ
a.
I I I I I
12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 1112
MIDNIGHT NOON MIDNIGHT
Figure 2. Hourly percent of total daily LTO volume for
a typical municipal airport
-------
Table 2. AIRCRAFT CLASSIFICATIONS AND REPRESENTATIVE AIRCRAFT'
Aircraft class
Representative aircraft
Jumbo jet
Long-range jet
Medium-range jet
Air carrier turboprop
Business jet
General aviation
turboprop
General aviation
piston
Piston transport
Helicopter
Military turboprop
Military jet
Military piston
Boeing 747
Lockheed L-1011
McDonald Douglas DC-10
Boeing 707
McDonald Douglas DC-8
Boeing 727
Boeing 737
McDonald Douglas DC-9
Convair 580
Electra L-188
Fairchild Hiller FH-227
Gates Learjet
Lockheed Jetstar
Cessna 210
Piper 32-300
Douglas DC-6
Sikorksy S-61
Vertol 107
10
-------
Meteorological conditions of wind direction and visibility determine
the runway in use. Generally, it will be the longest runway or the one
most nearly, parallel to wind direction, although low visibility or
night flight might require the use of an instrument landing system
equipped or lighted runway which does not give the best alignment with
wind direction. The terminal location and the runway in use determine
the time spent in taxi mode before takeoff and after landing.
Time in takeoff and landing modes is affected by the component of wind
velocity parallel to the runway and by the temperature as well as type
of aircraft. Takeoff and landing times are shorter the higher the wind
speed and the lower the temperature. These times become longer as the
aircraft carries more mass to be accelerated and lifted, or decelerated
after landing.
The same factors affect climbout and approach, with potential modifica-
tions if nearby populated areas require special noise reduction pro-
cedures and flight paths. These may include techniques such as climbing
at reduced power and immediate turns away densely populated areas.
After the time spent in the different modes are determined they can be
multiplied by the modal emission rates to calculate emissions. The
emission rates depend basically on power requirements and engine type,
which are in turn related to passenger and freight volume, amount of
fuel carried for origin-destination requirements, weather, and special
LTO procedures.
Civilian Airport Flight Operations
Figure 3 is a diagram of the interacting factors involved in emission
production at a civilian airport. Weather is a dominant factor in
civilian flight operations. .The level of activity falls as the weather
deteriorates, since much of the flying done is for instruction and
pleasure, and since pilot qualifications often exclude flying during
11
-------
WEATHER
TYPE OF USE
ORIGIN-
DESTINATION
REQUIREMENTS
TIME OF DAY
DAY OF WEEK
MONTH OF YEAR
NUMBER OF PASSENGERS
<*
EMISSIONS PER TIME
IN MODE
EMISSIONS
Figure 3. Interacting factors affecting emissions production
at a civilian airport
12
-------
inclement weather. Further, the airport may not be equipped with the
radio navigation aids needed for foul weather flying.
The traffic volume also varies with time of day, day of week, and month.
It is generally heavier in the evening, on weekends, and in the summer
when private pilots have the time and weather offers more incentive to
fly.
These factors determine the air traffic volume and, to some extent,
the mix. Commercial charter and business flights are less affected by
weather than private flights. When there is a large percentage of
private flights the mix will have a greater percentage of small, single
engine aircraft.
Time in mode is affected by the same factors as at the municipal air-
port, only in this case the aircraft are at ramp or tie-down locations.
Aircraft rental, instruction, and charter companies generally have a
specific portion of the ramp area for their use which is rented from
the airport authority. These locations can be determined from a
visit to the airport.
Time in mode, and also power requirements, are further affected by the
number of passengers, especially in light, two or four-place planes
where passenger weight is a significant fraction of aircraft weight.
Climbout time is reduced with fewer passengers, reducing time in this
mode, while approach time is increased because of reduced downward
weighting force.
When emission rates as functions of mode and power.requirements are
known, they can be multiplied by the times in the various modes to find
emissions.
13
-------
Military Airport Flight Operations
These operations are influenced by the factors common to all airport
operations; however, they are not dependent on passenger demands, as
at the municipal airport, nor are they strongly affected by weather,
as for civilian flights. The level of activity is determined mainly by
training, proficiency, and defense requirements. Weather determines
the runway used and taxi times.
FACTORS AFFECTING GROUND SUPPORT VEHICLE EMISSIONS
Municipal Airport
The municipal airport has by far the most ground service vehicle opera-
tions. A listing of the types of service vehicles is given in Table
3. The extent of use of each vehicle is directly related to LTO volume
and aircraft mix. Emissions can be calculated from published data on
service times and emission rates. Emissions from service vehicle travel
around the airport can be found knowing the airport layout, the activity,
and the proper emission factors.
Civilian Airports
Service vehicles at civilian airports are almost exclusively fueling
trucks, and even these may be absent at the smaller airports. Other
support vehicles may include tractors, etc. for grass cutting and snow
removal.
Military Airport
Service vehicles at the military airport also include fueling trucks
and many of the vehicles found at municipal airports. Emissions from
these sources are dependent on flight activity.
14
-------
Table 3. SERVICE VEHICLES USED AT A MUNICIPAL AIRPORT2
Vehicle
1. Tractor
2. Belt loader
3. Container loader
4. Cabin service
5. Lavatory truck
6. Water truck
7. Food truck
8. Fuel truck
9. Tow tractor
10. Conditioner
11. Airstart
Transporting engine
Diesel power unit
12. Ground power unit
Transporting engine
Gasoline power unit
Diesel power unit
13. Transporter
15
-------
FUEL HANDLING AND STORAGE EMISSIONS
These emissions are of two types: (1) working losses, and (2) breathing
losses. The former type occurs when vapors in fuel tanks are displaced
during fueling, and when there is spillage and evaporation. The latter
type is due to diurnal temperature variations, wind speeds, and fuel
vapor pressure among other factors. It may be controlled by tank vapor
recovery systems.
Municipal Airport
By far the largest use is of jet fuel with a much smaller volume of
gasoline used for service vehicles and piston aircraft. Actual volumes
of each are a function of LTO activity, passenger volumes, and origin-
destination distances.
Civilian Airport
Here, gasoline comprises the larger volume of fuel use. .Jet fuel is
available at the larger airports. Gasolines of different octane ratings
at these airports will have slightly different emission characteristics
because of volatility differences. Actual use will be determined by LTO
activity and the factors affecting it.
Military Airport
Jet fuel comprises the larger use for military airport operations. Gaso-
line is used for service vehicles.
ENGINE MAINTENANCE TESTING
The emissions from this source depend on the test cycle power settings
and times spent at each setting for the various types of engines. The
municipal and military airports will handle mostly jet engine testing,
16
-------
while the civilian airports will test piston engines. Emissions from
this source at the smaller civilian airports will be negligible, if not
non-existent.
17
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SECTION IV
LEVELS OF EMISSION INVENTORY DETAIL
The ideal emissions inventory would consider all the interrelating fac-
tors described in Section III. Of course, the time and economic costs
would be prohibitive. The purpose of this study is to consider altern-
ative levels of detail for making inventories on a "cost-benefit" basis,
determining the significance of emissions sources and the sensitivity of
an inventory in.return for added data collection and analysis efforts.
In this section, levels of detail are outlined with comments on efforts
and benefits.
EMISSIONS INVENTORY FROM PUBLISHED DATA
Features:
NEDS data on annual LTO volumes by airport type
and county
Uses average emission factor for LTO cycle based
on type of airport
Annual LTO activity data available from FAA
Time resolution - annual average
Spatial resolution - countywide area source
Comments:
Easily calculated from readily available data
Insufficient resolution and specificity by source.
18
-------
TYPE OF AIRCRAFT DETAIL
Features:
Emission factor for type of aircraft
Percent of total LTO's for type of aircraft
Emission factor for service vehicle use by
type of aircraft
' Emission factor for fuel handling and storage
by aircraft mix and total LTO's
Emission factor for maintenance testing by type
of aircraft
Data available from FAA, airport records, airline
schedules
Time resolution - by type of data collected
Spatial resolution - by source locations at airport
Comments:
v « More extensive data collection effort
Easily calculated once necessary data are known
Time resolution variable
TIME-IN-MODE DETAIL
Features:
Emission factors by mode required
Average times in mode required
Comments:
Average time in.mode data available from EPA
publication "An Air Pollution Impact Methodology
for Airports - Phase I," (APTD-1470)
No additional data collection needed
Relatively little added effort over type of
aircraft detail
19
-------
HOURLY EMISSION ESTIMATES
Features: .
LTO activity by time of day, day of week, month,
type of aircraft
Data available from airline schedules, airport
records, FAA
Comments:
Extensive effort required for data collection and
analysis over time-in-mode detail
Hourly resolution for aircraft operations, support
vehicles, fuel handling
REFINED HOURLY EMISSIONS ESTIMATES
Features:
To include meteorological effects, special LTO
procedures, aircraft loading, terminal-runway
distances, queuing
Comments:
Long-term, extensive effort required for data
collection
Computer analysis of data
Degree of refinement not initially required
RELATIVE EMISSIONS CONTRIBUTIONS BY SOURCE AT AIRPORTS
Table 4 shows the relative emissions contributions of the airport sources
at O'Hare airport in 1970. Aircraft operations account for well over
60 percent of carbon monoxide and hydrocarbon emissions, and about
90 percent of these emissions occurs during taxi and idle modes. This
indicates a good sensitivity return for improved data on time in mode
and emission rates for these modes.
20
-------
Table 4. PERCENT EMISSIONS CONTRIBUTION BY SOURCE AT
O'HARE AIRPORT - 1970
Source
Aircraft
Service vehicles
Fuel handling
CO
69
31
0
HC
79
13
8
NOX
86
14
0
Particulate
96
4
0
Aircraft operations contribute 86 percent to total NOV emissions,
X
indicating good leverage from improved information on the factors
involved. Most of these emissions occur during the high power
operations of takeoff and climbout.
21
-------
SECTION V
EMISSION ESTIMATION METHODOLOGY FOR LAMBERT FIELD
This section and the following two sections describe the hourly emission
estimation techniques. This section applies to Lambert Field, the next
one applies to Scott AFB, and Section VII describes the methodology for
the civilian airports. The three types of airports are discussed
separately, since data availability and the complexity of the required
methodology is different for each. Four emission sources are included
in the methdologies:
Aircraft flight operations
Ground service vehicles
Fuel handling and storage
Engine testing and maintenance.
EMISSIONS FROM AIRCRAFT FLIGHT OPERATIONS
To estimate hourly emissions from aircraft flight operations five
parameters must be known:
Temporal activity patterns
Spatial activity patterns
Percent volume distribution of
aircraft types
Time spent in the different
operating modes
Emission factors.
22
-------
The following sections discuss the availability of data for each .of
these five parameters, and the use of these data in a methodology for
emission estimation.
Temporal Activity Patterns
Ideally, hourly landing and takeoff volumes and type of equipment would
be known for the best predictions of emissions. However, this informa-
tion is not compiled and estimates must be made from available data.
For Lambert Field there are three sources of data:
Federal Aviation Administration Air Traffic Control Tower,
Mr. Jerome C. Moonier
Lambert Field, Manager's Office, Mr. Arthur K. Muchmore,
Assistant Airport Manager, Operations and Maintenance
4
Official Airline Guide listings for air carrier traffic
at St. Louis
The FAA maintains daily totals of traffic volumes under the classifica-
tions shown in Table 5. Local traffic has its origin and destination at
Lambert, and it mainly involves "touch and go" landing and takeoff
practice. Itinerant operations have their origin or destination at
another airport. These classifications are further divided for itinerant
traffic into air carrier, air taxi, general aviation, and military
categories. For local operations they are subdivided into civilian and
military categories. The FAA also compiles average hourly activity
totals for May and November. The November totals are presented in Table
6.
The airport manager's office receives its flight activity information
from the FAA in the form described. This office' is an alternative
source of this information.
23
-------
Table 5. FAA CLASSIFICATION OF DAILY AIR TRAFFIC OPERATIONS
DAY
Itinerant air traffic
AIR AIR
CARRIER IAXI
GENERAL
AVIATION MILITARY TOTAL
Local air traffic
CIVIL MILITARY TOTAL
Total air
traffic
Table 6. AVERAGE HOURLY AIR
TRAFFIC VOLUMES AT
LAMBERT FIELD, ST.
LOUIS, FOR MAY AND
NOVEMBER, 1972
Hour
0000-0100
0100-0200
0200-0300
0300-0400
0400-0500
0500-0600
0600-0700
0700-0800
0800-0900
0900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2100
2100-2200
2200-2300
2300-2400
Volume
12
11
10
3
4
5 .
13
36
55
64
68
66
64
66
65
57
67
64
50
43
40
26
30
13
24
-------
The Official Airline. Guide lists flight schedules semi-monthly for the
air carriers. These listings show scheduled departure and arrival times
and type of aircraft used. Scheduled flight activity to St. Louis is
listed in one section of the Guide, while flights from St. Louis to
other cities are listed under the destination city headings.
Table 6 lists hourly totals of flight activity at Lambert Field. To
complete the temporal data, the total volumes by month and by the day of
the week for the four aircraft categories are given in Tables 7 and 8.
The itinerant and local volumes have been combined for both general
aviation and military flights in these Tables.
In order to prepare a methodology for estimating emissions, the volumes
given in Tables 6, 7, and 8 were converted to percentages totaling 100
percent for each category of aircraft. The computed percentages for
monthly, daily, and hourly air traffic are given in Tables 9, 10, and
11.
To compute the volume of traffic for category i for a given hour, day,
and month we start from the relationship:
/
A. ' M. ,' D. H.
.
± (ODJ (106)
/
where i indicates the category (e.g. air carrier), A. is the annual
volume, and M., D., and H. are the percents of the annual volume for
the month, day, and hour of interest. The factor OD is the average
m
occurrence of the day of the week for the month. It equals 4.43 for
months having 31 days, 4.29 for 30 day months, and 4 for February (4.14
in a leap year). The factor of 10 converts the percentages to decimals
This relationship can be entered at any point. For example, if the
monthly total is known ,
25
-------
Table 7. MONTHLY AIR TRAFFIC AT LAMBERT FIELD, ST. LOUIS,
FOR DECEMBER 1972 AND JANUARY - NOVEMBER 1973
Month
January
February
March
April
May
June
July
August
September
October
November
December
Category
Total
Air
carrier
16,006
14,316
15,655
13,955
12,236
12,363
15,703
16,721
15,934
16,658
11,004
15,234
175,785
Air
taxi
1,985
1,744
2,052
2,078
2,606
2,648
2,492
2,812
2,474
2,724
2,488
1,590
27,693
General
aviation
9,112
8,957
9,300
11,305
13,106
12,618
12,137
12,420
10,509
11,985
11,110
6,691
129,250
Military
1,008
1,013
1,-071
1,363
1,576
1,293
963
1,187
1,106
1,353
878
861
13,672
Total
28,111
26,030
28,078
28,701
29,524
28,922
31,295
33,140
30,023
32,720
25,480
24,376
346,400
Table 8. AIR TRAFFIC VOLUMES BY DAY OF WEEK AT LAMBERT FIELD,
ST. LOUIS, FOR DECEMBER 1972 AND JANUARY - NOVEMBER
1973
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Category
Total
Air
carrier
23,385
25,037
25,499
25,965
26,259
26,718
22,922
175,785
Air
taxi
1,907
3,229
4,801
5,115
5,040
5,280
2,321
27,693
General
aviation
15,851
15,369
18,424
20,772
21,326
20,499
17,009
129,250
Military
1,096
1,219
2,266
2,532
2,390
2,505
1,664
13,672
Total
42,239
44,854
50,990
54,384
55,015
55,002
43,916
346,400
26
-------
Table 9. PERCENT OF TOTAL ANNUAL AIR TRAFFIC
BY MONTH AT LAMBERT FIELD
Month
January
February
March
April
May
June
July
August
September
October
November
December
Air
carrier
9.11
8.14
8.91
7.94
6.96
7.03
8.93
9.51
9.06
9.48
6.26
8.67
Air
taxi
7.17
6.30
7.41
7.50
9.41
9.56
9.00
10.15
8.93
9.84
8.98
5.74
General
aviation
7.05
6.93
7.20
8.75
10.14
9.76
9.39
9.61
8.13
9.27
8.60
5.18
Military
7.37
7.41
7.83
9.97
11.53 .
9.46
7.04
8.68
8.09
9.90
6.42
6.30
Total
8.18
7.57
8.16
8.07
8.52
8.38
8.98
9.64
8.73
9.52
7.40
6.86 .
Table 10. PERCENT OF TOTAL AIR TRAFFIC BY. DAY OF WEEK
FOR LAMBERT FIELD, ST. LOUIS
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Air
carrier
13.30
14.24
14.51
14.77
14.94
15.20
13.04
Air
taxi
6.89
11.66
17.34
18.47
18.20
19.07
8.38
General
aviation
12.26
11.89
14.25
16.07
16.50
15.86
13.16
Military
8.02
8.92
16.57
18.52
17.48
18.32
12.17
Total
12.19
12.95
14.72
15.70
15.88
15.88
12.68
27
-------
Table 11. PERCENT OF TOTAL DAILY MOVEMENTS BY HOUR
AT LAMBERT FIELD
Hour
0000-0100
0100-0200
0200-0300
0300-0400
0400-0500
0500-0600
0600-0700
0700-0800
0800-0900
0900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2100
2100-2200
2200-2300
2300-2400
Air
carrier
1.15
1.00
1.00
0.26
0.42
0.42
1.36
3.67
6.02
6.81
6.97
6.86
6.71
7.12
6.60
5.92
7.23
7.18
5.97
5.40
4.71
3.04
2.88
1.31
Air
taxi
2.05
1.33
1.33
0.24
0.24
0.00
0.97
2.54
4.83
5.92
6.88
7.49
6.52
6.88
9.42
8.33
6.76
6.52
5.43
4.35
4.23
3.62
2.66
1.45
General
aviation
2.05
1.33
1.33
0.24
0.24
0.00
0.97
2.54
4.83
5.92
6.88
7.49
6.52
6.88
9.42
8.33
6.76
6.52
5.43
4.35
4.23
3.62
2.66
1.45
Military
2.05
1.33
1.33
0.24
0.24
0.00
0.97
2.54
4.83
5.92
6.88
7.49
6.52
6.88
9.42
8.33
6.76
6.52
5.43
4.35
4.23
3.62
2.66
1.45
28
-------
M. D. H.
V. -
1 (ODJ (10*)
where M. is the monthly total volume of aircraft category i.
Suppose it is required to estimate the air carrier activity between
10 am. and 11 am. on a Wednesday in June. The arrival total A. = 175,785;
from Table 9, M. = 7.03; D. = 14.77 from Table 10, and H. = 6.97 from
' i i ' i
Table 11. Hence
v = (175.785) (7.03) (14.77) (6.97)
1 (4.29) (106)
= 30 air carrier movements (takeoff plus landing).
This method assumes equal numbers of landings and takeoffs, and also
that the distribution of activity by month, day of the week, and hour
remains constant from year to year. The exact landing and takeoff
split by hour for air carriers can be extracted from the Official Airline
Guide. This has been done for "average" day, and the results are shown
in Table 12. For other categories it is assumed that half the movements
are takeoffs and half are landings.
The relationship for calculating hourly volumes can be entered at any
point for which a volume is known. The actual volume for the year,
month, or day of interest can be used when making an emission inventory
retrospectively. These data are available from the FAA Air Traffic
Control Tower at Lambert.
The percent volumes presented in Tables 9, 10 and 11 are for December
1972 and January-November 1973. In future years the latest figures
could be used to revise these tables either by replacement, or by
averaging. The former revision of replacement may become especially
29
-------
Table 12. PERCENT OF DEPARTURES AND ARRIVALS FOR
AIR CARRIER TRAFFIC BY HOUR OF THE DAY4
Hour
0000-0100
0100-0200
0200-0300
0300-0400
0400-0500
0500-0600
0600-0700
0700-0800
0800-0900
0900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2100
2100-2200
2200-2300
2300-2400
Departures
(%)
25.00
66.67
100.00
0
0
100.00
33.33
58.33
52.94
37.84
60.53
40.63
43.59
32.26
62.86
32 . 00
42.11
50.00
40.63
51.43
41.67
15.38
20.00
57.14
Arrivals
(%)
75.00
33.33
0
100.00
0
0
66.67
41.67
47.06
62.16
39.47
59.37
56.41
67.74
37.14
68.00
57.89
50.00
59.37
48.57
58.33
84.62
80.00
42.86
30
-------
important if economic or other factors change the distribution as well
as the total volume of air traffic.
Spatial Patterns of Aircraft Flight Activity
Aircraft flight activity consists of the six modes described in Table 1.
These modes occur at different locations on the airport, and the emis-
sions estimates must reflect this spatial variation. Lambert Field lies
in eight grid elements of 1 kilometer squared according to the grid
network designed for RAPS. Table 13 displays the grid numbers, the grid
coordinates, and the aircraft operations in each grid according to the
runway being used. Figure 4 shows the grids overlaid on the airport.
No grids are listed for climbout or approach; these are listed later
with times in mode. Lambert Field requests that aircraft maintain a
constant heading away from or towards the runway when flying below 1500
feet. The approach glide path is about 2.5 or 3 degrees, so an approach
heading is maintained within a distance of about 5 miles from the air-
port. The FAA indicates good compliance with this request. Above 1500
feet the aircraft may fly in any direction. '
As an example, consider the emissions from the hourly volume of 30 air
carrier movements. For the 1000-1100 hour, approximately 60 percent of
these aircraft are departing. Generally these aircraft will move from
the terminal area to the runway by the most direct taxiway when depart-
ing, and they will move from the runway to the terminal area by the
shortest taxi distance after landing. In this example, it is assumed
that runway 30L is the active runway. Referring to Figure 4, two thirds
of the terminal area, or ramp, is in grid 523, while the other third is
in grid 492. Aircraft will taxi out to take off in grids 492, 523, and
559, and take off in grids 560, 523, and 559. (See Table 13.) Of the
18 aircraft taking off, 12 will have idle mode emissions in grid 523,
and 6 will emit during idle in grid 492. The simplest method of dis-
tributing the taxi and takeoff emissions would be to divide them equally
31
-------
Table 13. OPERATING MODES FOR EACH GRID BY ACTIVE RUNWAY AT LAMBERT FIELD, ST. LOUIS
Runway
30L
30R
12R
12L
35
17
Grid
560
559
523
493
492
452
560
524
523
493
492
452
560
523
493
492
452
560
524
523
493
492
524
523
493
524
523
493
X-Coord
4291
4290
4291
4292
4291
4292
4292
Y-Coord
730
730
729
728
728
111
729
Size
1
1
1
1
1
1
1
1
1
1
1
1
Idle
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Takeoff
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Climbout
s
Approach
-
Landing
X
X
X
X
X
X
X
X
*
X
X
X
x ,
fc'
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
N>
-------
Table 13 (continued). OPERATING MODES FOR EACH GRID BY ACTIVE RUNWAY AT LAMBERT FIELD,
/ ST. LOUIS
Runway
6
24
Grid
524
523
493
492
452
451
524
523
493
492
451
X-Coord
4291
Y-Coord
727
Size
Idle
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
X
Takeoff
X
X
X
X
X
Climbout
Approach
Landing
X
X
X
X
X
Taxi
X
X
X
X <
X
X
-------
#493
MCDONNELL -
MARK AER
-------
among the grids involved. A more refined yet still simple method would
be weight them according to the percent of the total taxi or takeoff
time spent in each grid. This method is described here.
Table 14 shows the times in mode in each grid for air carriers taxiing
to and taking off from runway SOL. To calculate emissions for each
grid, the idle emissions are added to grids 492 and 523 for the six and
12 aircraft, respectively, the taxi emissions are distributed by the >
times in Table 14 for the six aircraft starting from grid 492 and the
12 starting from 523, and the takeoff emissions from all 18 aircraft are
distributed in the grids containing runway 30L. This same method is
used for landing and taxiing to the terminal area and for other runways
and ramp destinations. Tables 15 through 21 list the times in mode by
grid and by mode for the aircraft categories using the remaining runways.
A complicating factor is the queuing of aircraft waiting to take off
during periods of heavy volume. The EPA report APTD-1470 recommends
adding extra idle time due to queuing as T = (N-30)/10 when the landing
and takeoff volume exceeds 30 per hour. T is the time queued (minutes)
and N is the LTO volume. This relationship is based on data from
Chicago's O'Hare airport and assumes the use of two parallel runways.
Observation at Lambert Field indicate, however, that no extensive queuing
occurs ever during periods of heaviest volume.
Emission Rates
Most emission rate data for aircraft have been gathered by the Cornell
Aeronautical Laboratory. These are compiled in the report "Analysis of
Aircraft Exhaust Emission Measurements," (PB-204-879) and summarized in
3
the EPA report "Compilation of Air Pollutant Emission Factors," (AP-42)-
Emission rates for SO- are not given, possibly because of variation with
fuel sulfur content, but they can be estimated by the product of the
fuel use rate and the percent of sulfur in the fuel.
35 .
-------
Table 14. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 30L
(seconds)
Grid
2187
683
654
653
616
589
588
560
559
524
523
522
493
492
452
451
389
Air carrier
Idle
0
0
0
0
0
0
0
20
45
0
540
0
0
520
0
0
0
Taxi
0
0
0
0
0
0
0
0
15
0
130
0
.0
230
0
0
0
Take-
off
0
0
0
0
0
0
0
10
2
0
30
0
0
0
0
0
0
Land-
ing
0
0
. 0
0
0
0
0
0
0
0
20
0
0
15
0
0
0
Climb-
out
35
0
0
0
0
0
0
0
0
0
3
0
15
5
15
0
35
Approach
0
26
26
26
26
26
26
3
7
0
0
0
0
0
0
0
0
Military
Idle
0
0
0
-0
0
0
0
0
130
0
150
0
0
200
0
, 0
0
Taxi
0
0
0
0
0
0
0
0
30
0
120
0
0
150
0
0
0
Take-
off
0
0
0
0
0
0
0
10
0
0
14
0
0
0
0
0
0
Land-
ing
0
0
0
0
0
0
0
4
0
0
20
0
0
0
0
0
0
Climb-
out
10
0
0
0
0
0
0
0
0
0
0
0
8
0
2
0
10
Approach
0
15
15
15
15
15
15
2
4
0
0
0
0
0
0
0
0
LO
-------
Table 15. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 12R
(seconds)
Grid
2187
683
654
653
616
589
588
560
559
524
523
522
493
492
452
451
389
Air carrier
Idle
0
0
0
0
0
0
0
0
0
0
524
0
15
520
45
0
0
Taxi
0
0
0.
0
0
0
0
0
0
0
210
0
45
110
15
0
0
Take-
off
0
0
0
0
0
0
0
0
0
0
2
0
20
10
10
0
0
Land-
ing
0
0
0
0
0
0
0
0
0
0
10 .
0
5
20
0
0
0
Climb-
out
0
13
13
13
13
13
13
4
10
0
15
0
0
0
0
0
0
Approach
68
0
0
0
0
0
0 .
0
0
0
0
0
5
0
5
0
68
Military
Idle
0
0
0
0
0
0
0
0
0
0
0
0
60
420
0
0
0
Taxi
0
0
0
0
0
0
0
0
0
0
40
0
70
50
o.
0
0
Take-
off
0
0
0
0
0
0
0
0
0
0
8
0
8
8
0
0
0
Land-
ing
0
0
0
0
0
0
0
0
0
0
8
0
8
8
0
0
0
Climb-
out
0
0
6
0
6
6
0
2
4
0
6
0
0
0
0
0
0
Approach
45
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
45
-------
Table 16. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 30R
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
150
0
0
0
0
600
0
0
0
Taxi
0
0
80
80
0
320
0
0
0
Take-
off
0
0
13
20
0
0
0
0
0
Land-
ing
15
0
0
18
0
0
0
0
0
Climb-
out
0
0
0
0
0
200
0
25
0
Approach
273
0
0
0
0
0
0
0
0
General aviation
Idle
100
0
0
0
0
500
0
0
0
Taxi
0
0
80
80
0
320
0
0
0
Take-
off
0
0
8
20
0
0
0
0
0
Land-
ing
9
0
0
18
0
0
. 0
0
0
Climb-
out
0
0
0
0
0
250
0
50
0
Approach
360
0
0
0
0
0
0
0
0
00
Table 17. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 12L
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
0
0
0
0
0
750
0
0
0
Taxi
0
0
90
90
0
300
0
0
0
Take-
off
0
0
17
16
0
0
0
0
0
Land-
ing
0
0
17
16
0
0
0
0
0
Climb-
out
225
0
0
0
0
0
0
0
0
Approach
0
0
0
0
0
123
0
150
0
General aviation
Idle
0
0
0
0
0
600
0
0
0
Taxi
0
0
90
90
0
300
0
0
0
Take-
off
0
0
10
8
0
0
0
0
0
Land-
ing
0
0
9
9
0
0
0
0
0
Climb-
out
300
0
0
0
0
0
0
0
0
Approach
0
0
0
0
0
160
0
200
0
-------
Table 18. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 35
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
0
0
0
250
0
500
0
0
0
Taxi
0
0
160
160
44
300
0
0
0
Take-
off
0
0
0
33
0
0
0
0
0
Land-
ing
0
0
0
33
0
0
0
0
0
Climb-
out
0
0
225
0
0
0
0
0
0
Approach
0
0
0
0
273
0
0
o
0
General aviation
Idle
0
0
0
50
0
450
0
0
0
Taxi
0
0
160
160
44
300
0
0
0
Take-
off
0
0
0
18
0
0
0
0
0
Land-
ing
0
0
0
18
0
0
0
0
0
Climb-
out
0
0
300
0
0
0
0
0
0
Approach
0
0
0
0
360
0
0
0
0
VO
Table 19. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 17
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
0
0
250
0
0
500
0
0
0
Taxi
0
0
100
100
0
380
0
0
0
Take-
off
0
0
30
3
0
0
0
0
0
Land-
ing
0
0
30
3
0
' 0
0
0
0
Climb-
out
0
0
0
40
185
0
0
0
0
Approach
. 0
0
273
0
0
0
0
0
0
General aviation
Idle
0
0
50
0
0
450
0
0
0
Taxi
0
0
100
100
0
380
0
0
0
Take-
off
0
0
15
0
0
0
0
0
0
Land-
ing
0
0
15
0
0
0
0
0
0
Climb-
out
0
0
0
50
250
0
0
0
0
Approach
0
0
360
0
0
0
0
0
0
-------
Table 20. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 6
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
0
0
0
0
0
300
0
300
150
Taxi
0
0
0
0
0
290
10
10
10
Take-
off
0
0
0
0
0
13
. 10
0
10
Land-
ing
0
0
0
0
0
11
11
0
11
Climb-
out
0
0
125
0
0
100
0
0
0
Approach
0
0
0
0
0
0
0
0
273
General aviation
Idle
0
0
0
0
0
250
0
250
100
Taxi
0
0
0'
0
0
290
10
10
10
Take-
off
0
0
0
0
0
8
5
0
5
-Land-
ing
0
0
0
0
0
6
6
0
6
Climb-
out
0
0
175
0
0
125
0
0.
0
Approach
0
0
0
0
0
0
0
0
360
Military
Idle
0
0
0
0
0
0
400
0
80
Taxi
0
0
50
0
0
0
200
0
50
Take-
off
0
0
0
0
0
20
2
0
2
Land-
ing
0
0
0
0
0
20
2
0
2
Climb-
out
0
0
25
0
0
5
0
0
0
Approach
0
0
0
0
0
0
0
0
96
Table 21. TIMES IN MODE BY GRID BY MODE FOR AIR TRAFFIC USING RUNWAY 24
(seconds)
Air taxi
Grid
560
559
524
523
522
493
492
452
451
Idle
0
0
150
0
0
300
0
0
300
Taxi
0
0
150
0
0
230
0
0
0
Take-
off
0
0
23
0
0
10
0
0
0
Land-
ing
0
0
23
0
0
10
0
0
0
Climb-
out
0
0
0
0
0
40
0
0
185
Approach
0
0
273
0
0
0
0
0
0
General aviation
Idle
0
0
100
0
0
250
0
0
250
Taxi
0
0
150
0
0
230
0
0
0
Take-
off
0
0
12
0
0
6
0
0
0
Land-
ing
0
0
12
0
0
6
0
0
0
Climb-
out
0
0
0
0
0
40
0
0
260
Approach
0
0
360
0
0
0
0
0
0
Military
Idle
0
0
80
0
0
0
400
0
0
Taxi
0
0
120
100
0
0
100
0
0
Take-
off
0
0
24
0
0
0
0
0
0
Land-
ing
0
0
24
0
0
0
0
0
0
Climb-
out
0
0
0
0
0
5
0
0
25
Approach
0
0
96
0
0
0
0
0
0
-------
Table 22 lists the air carrier aircraft and engines used at Lambert
Field as compiled from the Official Airline Guide. The numbers of each
type of engine were used to weight the emission factors for each to
prepare a single, composite set of weighted emission factors for air
carriers. The emission factors for each engine type and the weighted
factors are presented in Table 23.
Table 22. AIRCRAFT AND ENGINE VOLUMES FOR LAMBERT FIELD, ST. LOUIS
Aircraft
DC 9
727
707
CVS
320
880
DC10
737
BAClll
TOTAL
Engine type
Number
110
76
24
25
2
2
3
5
5
252
JT3D
96
96
JT4A
8
8
JT8D
220
228
10
458
CJ805
8
8
JT9D
9
9
T56-A7
50
V
50
RRMK511
10
10
Composite emission factors were also prepared for the other three air-
craft categories. For air taxi the weighting factors are 100 T56-A7
engines and 20 RRMK511 engines. General aviation was given an equal
distribution of 0-320, 0-360, and 0-200 engines. Military aircraft were
half J79 and half J57 engines. The composite emission factors by pol-
lutant and by mode are listed in Table 24. The S0? emission factors
presented in this report are for an assumed 0.05 percent sulfur content
fuel.3
41
-------
Table 23. EMISSION FACTORS BY ENGINE TYPE AND MODE FOR AIR CARRIERS
(kg/hr)
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
Pollutant
CO
HC
NOx
so2
Particulate
CO
HC
NOX
S02
Particulate
CO
nr
NOx
S02
Particulate
CO
HC
NOX
S02
Particulate
CO
HC
NOx
S02
Particulate
CO
HC
NOx
S02
Particulate
JT3D
49.4
44.7
0.649
0.396
0.20
49.4
44.7
0.649
0.396
0.2
5.6
911
67.1
4.915
3.7
33.9
27.9
18.1
0.662
1.6
6.94
2.23
43.6
4.062
3.9
18.0
3.56
9.89
1.877
3.6
JT4A
28.5
29.4
1.23
0.631
0.54
28.5
29.4
1.23
0.631
0.54
8.53
0 ^Ofi
107.0
7.051
95
21.1
18.0
29.0
0.545
3.0
8.30
0.576
70.3
5.939
9.1
11.9
1.74
16.3
2.724
2.7
JT8D
15.2
3.71
1.32
0.435
0.16
15.2
3.7
1.32
0.435
0.16
3.40
0 "^S"}
89.8
3.971
1.7
11.3
2.4
24.6
0.248
0.61
4.03
0.418
59.4
3.328
1.2
8.26
0.794
14.0
1.546
0.68
CJ805
28.9
12.4
0.712
0.454
0.59
28.9
12.4
0.712
0.454
0.59
13.2
0 ?S7
50.3
4.518
6.8
23.6
7.7
13.8
0.275
2.4
13.1
0.264
33.6
3.760
6.8
19.4
1.10
8.07
1.713
2.3
JT9D
46.3
12.4
2.75
0.788
1.0
46.3
12.4
2.75
0.788
1.0
3.76
1 -i/.
327
7.735
1.7
31.1
8.0
84.1
0.379
1.2
5.31
1.20
208
6.494
1.8
14.8
1.36
24.5
2.361
1.0
T56-A7
6.94
2.93
0.98
0.249
0.73
6.94
2.93
0.98
0.249
0.73
0.975
o i QS
10.40
1.943
1.7
4.66
1.84
3.649
0.076
1.07
1.37
0.216
9.62
0.865
1.4
1.66
0.235
3.53 '
0.478 .
1.4
RRMK511
27.3
30.0
0.385
0.300
0.077
27.3
30.0
0.385
0.300
0.077
6.44
69.4
3.459
7.3
20.76
18.31
19.10
0.222
1.91
6.94
0.110
52.2
2.883
4.5
17.7
1.91
13.8
1.384
0.68
Weighted
factors
20.66
10.77
1.19
0.42
0.23
20.66
10.77
1.19
0.42
0.23
3.78.
n fii
82.92
3.97
2.25
14.88
6.78
22.66
0.30
0.88
4.49
0.68
54.13
3.32
1.85
9.63
1.21
12.66
1.54
1.23
to
-------
Table 24. COMPOSITE EMISSION FACTORS FOR AIR TAXI, GENERAL AVIATION,
AND MILITARY AIRCRAFT AT LAMBERT FIELD
(kg/hr)
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
Pollutant
HC
CO
NOx
S02
Particulate
HC
CO
NOX
S02
Particulate
HC
CO
NOx
S02
Particulate
HC
CO
NOx
S02
Particulate
HC
CO
N0x
S02
Particulate
HC
CO
NOx
S02
Particulate
Air
taxi
7.442
10.333
0.881
0.258
0.621
7.442
10.333
0.881
0.258
0.621
0.195
1.886
20.233
2.196
2.633
4.585
7.343
6.224
0.100
1.210
0.198
2.298
16.717
1.201
1.917
0.514
4.333
5.242
0.629
1.280
General
aviation
0.428 .
4.779
0.006
0.005
0.0
0.428
4.779
0.006
0.005
0.0
0.642
32.923
0.143
0.031
0.0
0.451
12.764
0.044
0.012
0.0
0.488
28.393
0.157
0.027
0.0
0,249
12.471
0.037
0.012
0.0
Military
24.0
36.0 .
1.5
0.509
10.0
24.0
36.0
1.5
0.509
10.0
3.5
224.0
56.0
8.916
73.5
24.0
36.0
1.5
0.509
10.0
1.5
6.0
42.5
3.898
48.0
1.5
7.5
30.5
3.548
54.5
43
-------
GROUND SERVICE VEHICLE OPERATIONS
The activity of ground service vehicles and the vehicles used depend
on the type of aircraft being serviced. For Lambert Field, a composite
time for servicing was computed for each of the various types of ve-
hicles for the air carrier equipment mix. Table 25 presents a summary
of ground service vehicle usage and times for the different types of
aircraft. The composite service time was computed using the aircraft
volumes that were also used to compute composite emission factors.
Table 26 lists the fuel consumption rates, while Table 27 gives the
o
emission factors for each. Emission factors for S0~ can be computed
from Table 27 and the sulfur content of the fuel. The hourly emissions
from ground service vehicles are found by multiplying the times in
Table 25, the consumption rates in Table 26, and the emission factors
in Table 27 by half the hourly volume computed for aircraft activity.
FUEL HANDLING AND STORAGE
The Allied Aviation Fueling Company of St. Louis, Inc., is the major
supplier of fuel at Lambert. The fuel is stored on a hill outside the
airport boundary and is piped underground to the ramp area of the air-
line terminal. The majority of aircraft fueling is done directly from
outlets on the ramp, although fueling trucks are used at a few locations,
The fuel storage tanks are equipped with vapor recovery systems and the
cartridges are serviced regularly. Any fuel spillage during fueling is
promptly washed away.
Approximately 12 million gallons of fuel are pumped per month. The
working loss of hydrocarbons varies with temperature, and Table 28
lists the 94 year average high, medium, and low temperatures for each
month as compiled in the Climatic Atlas of the U. S. The average low
44
-------
Table 25. SERVICE TIMES OF AIRCRAFT GROUND SERVICE VEHICLES
^"~"--\^ Aircraft
Vehicle ^^-\^^
1. Tractor
2. Belt Loader
3. Container Loader
4. Cabin Service
5. Lavatory Truck
6. Water Truck
7. Food Truck
8. Fuel Truck
9. Tow Tractor
10. Conditioner
11. Airstart
Transporting
Engine
Diesel
Power Unit
Time in vehicle-minutes
DC- 10
148
40
80
25
18
10
20
45
10
0
o
0
B-707
66
37
12
12
15
0
20
37
10
30
10
8
B-727
66
28
6
12
15
0
17
20
10
0
0
0
DC- 9
48
15
0
0
15
10
17
15
5
0
0
0
B-737
85
30
0
15
15
0
20
15
5
0
0
0
C-880
40
40
0
0
20
0
20
20
15
0
15
11
F-227
55
0
0
0
10
10
10
10
5
0
0
0 .
C-580
50
25
0
0
10
10
10
20
5
0
0
0
Composite
times
56
23
3
5
15
5
17
19
8
3
2
2
Ui
-------
Table 25 (continued). SERVICE TIMES OF AIRCRAFT GROUND SERVICE VEHICLES
^""^^^^ Aircraft
^^"*s»»^^
Vehicle ^*"^->»^^
12. Ground Power Unit
Transporting
Engine
Gasoline
Power Unit
Diesel
Power Unit
13. Transporter
14. Auxiliary
Power Unit
Time in vehicle-minutes
DC- 10
0
0
0
0
Yes
B-707
9
4
4
10
No
B-727
0
0
0
3
Yes
DC- 9
0
0
0
0
Yes
B-737
0
0
0
0
Yes
C-880
35
15
15
0
No
F-227
0
0
0
0
No
C-580
0
0
0
0
No
Composite
times
4
2
2
2
30
cr>
-------
Table 26. GROUND SERVICE VEHICLE FUEL CONSUMPTION RATES
Vehicle
Rate of fuel consumption
(gal/hr)
1. Tractor
2. Belt Loader
3. Container Loader'
4. Cabin Service
5. Lavatory Truck
6. Water Truck
7. Food Truck
8. Fuel Truck
9. Tow Tractor
10. Conditioner
11. Airstart
Transporting Engine
Diesel Power Unit
12. Ground Power Unit
Transporting Engine
Gasoline Power Unit
Diesel Power Unit
13. Transporter
14. Auxiliary Power Unit
1.80
0.70
1.75
1.50£
1.50£
1.50£
2.00£
1.70*
2.35
1.75'
1.40
8.20
2.00
5.00
7.10
1.50
7.10
Estimated values
47
-------
Table 27. GROUND SERVICE VEHICLE EMISSION FACTORS
Vehicle
Gasoline Engines
Diesel Engines
Pollutant emissions
(grams/gal)
CO
999.0
147.6
UC
223.2
29.5
NOX
57.0
154.4
Particulates
1.8
11.4
Table 28. NINETY-FOUR YEAR AVERAGE HIGH, MEDIUM, AND
LOW TEMPERATURES FOR ST. LOUIS
Month
January
February
March
April
May
June
July
August
September
October
November
December
High
40
44
53
66
75
85
89
87
81
70
59
43
Medium
32
35
43
55
64
74
78
77
70
59
49
35
Low
23
25
32
44
53
63
67
66
58
47
35
27
48
-------
temperature is used for the 8 p.m. to 8 a.m. period, the average medium
is used for 8 a.m. to 1 p.m. and 3 p.m. to 8 p.m., and the average high
is used for 1 p.m. to 3 p.m.
The hydrocarbon emission factors are calculated by using the method from
the American Petroleum Institute publication API 2513.8 Table 29 lists
\
the working loss factors computed for each month for each time period.
The gallons of fuel pumped in any hour are computed by the same method
used for aircraft volumes. Hence,
where G is gallons/month (12 million) and the factor of 1/2 assumes an
m
even distribution of landings and takeoffs. The mass emissions are then
calculated by multiplying G, by. the emission factor appropriate to the
hour of the day (Table 29), and then multiplying the result by the volume
to mass factor of 2.8 kg/gal. These emissions are restricted to grids
492 (one third) and 523 (two thirds).
ENGINE TESTING AND MAINTENANCE
Engine testing and maintenance is done by McDonnell Douglas in associa-
tion with their manufacturing facilities at Lambert. The details of
their testing and maintenance are classified, since their production
consists of military aircraft. Their production rate is "about" two
aircraft per day, and hence this emission source will be excluded from
the inventory
49
-------
Table 29. WORKING LOSS FACTORS FOR THE THREE TIME PERIODS
FOR EACH MONTH
Month
January
February
March
April
May
June
July
August
September
October
November
December
Working loss (gallons/1000 gallons throughput)
0800-1300
1500-2000
0.87
0.94
1.12
1.42
1.61
2.10
2.17
2.16
1.92
1.52
1.17
0.94
1300-1500
1.03
1.17
1.38
1.65
2.11
2.58
2.76
2.64
2.37
1.92
1.41
1.12
2000-0800
0.71
0.72
0.87
1.17
1.38
1.60
1.81
1.80
1.51
1.20
0.94
0.78
50
-------
SECTION VI
SCOTT AIR FORCE BASE
Scott Air Force Base is an Air Medical and Airlift Wing of the Military
Airlift Command. The air traffic is light; it averages approximately
40 flights per day.
AIRCRAFT FLIGHT ACTIVITY
9
The five months of data available from Scott were not sufficient to
determine the percent of traffic by month. Therefore, a monthly mean
and standard deviation was calculated from the five months of data.
This is used with the day of week and hour of the day percentages to
find the hourly traffic. Since the flights are predominantly by
military aircraft, the categories of jet and piston aircraft are used.
Table 30 lists the monthly volumes and the five month means and stan-
dard deviations for the two categories.
Table 30. FIVE-MONTH AIR TRAFFIC VOLUMES, MEANS,
AND STANDARD DEVIATIONS AT SCOTT AFB,
1973 - 1974
Month
Sept.
Oct.
Nov.
Dec.
Jan.
Mean
Standard Deviation
Jet
1208
1088
788
461
617
832
313
Piston
451
470
, 400
281
285
379
87
Total
1659
1558
1188
742
912
1212
397
51
-------
Percentages by the day of the week are given in Table 31. Percentages
by the hour of the day were obtained from percentages for 6-hour
periods beginning at 0400. Thus, all hours within a 6-hour block are
given the same percent of total daily traffic. These are shown in
Table 32.
Table 31. PERCENT OF AIR TRAFFIC BY DAY
OF WEEK AT SCOTT AFB
Day .
Sunday
Monday
Tuesday
Wednesday
Thursday.
Friday
Saturday '
Jet
13.61
13.86
12.29
15.16
14.58
15.75
14.75
Piston
15.97
13.86
12.50
12.08
15.93
14.11
15.54
Scott Field lies in four grid elements as shown in Figure 5. There are
only two runways at Scott, runways 13 and 31 (Figure 5). Hence the
grid elements used for the different modes are easily defined, and
these are implicit in Tables 33 and 34 which give the time in the
various modes for each grid by runway and type of aircraft.
EMISSION FACTORS
Jet flights at Scott AFB are predominantly by C-9 and C-141 aircraft.
The C-9 has two JT8D engines, while the C-141 has four TF-33 engines. ^
The ratio of activity of the C-9 to C-141 is about 3.75 to 1.0, so the
weighting by engine type is about 1.87 (JT8D) to 1.0 (TF-33). Composite
emission factors based on these engines were calculated, and these are
given in Table 35.
52
-------
Table 32. PERCENT OF AIR TRAFFIC BY HOUR
AT SCOTT AFB.
Hour
0000-0100
0100-0200
0200-0300
0300-0400
0400-0500
0500-0600
0600-0700
0700-0800
0800-0900
0900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2100
2100-2200
2200-2300
2300-2400
Jet
5.367
5.367
5.367
5.367
0.841
0.841
0.841
0.841
0.841
0.841
4.054
4.054
4.054
4.054
4.054
4.054
6.404
6.404
6.404
6.404
6.404
6.404
5.367
5.367
Piston
5.226
5.226
5.226
5.226
0.298
0.298
0.298
0.298
0.298
0.298
3.799
3.799
3.799
3.799
3.799
3.799
7.344
7.344
7.344
7.344
7.344
7.344
5.226
5.226
53
-------
Figure 5. Runway layout and grid element overlay for Scott AFB
54
-------
Table 33. TIME IN MODE BY GRID AND MODE FOR AIR-
CRAFT USING RUNWAY 13, SCOTT AFB
(seconds)
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
2388
Jet
0
0
0
0
0
110
Piston
0
0
0
0
0
100
1637
Jet
20
40
0
0
108
0
Piston
0
50
0
0
300
0
1621
Jet
40
50
20
15
0
56
Piston
80
50
16
16
0
176
1620
Jet
420
510
22
20
0
0
Piston
720
600
20
20
0
0
Table 34. TIME IN MODE BY GRID AND MODE FOR AIR-
CRAFT USING RUNWAY 31, SCOTT AFB
(seconds)
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
2388
Jet
0
0
0
0
70
0
Piston
0
0
0
0
200
0
1637
Jet
40
20
10
5
0
166
Piston
80
50
16
10 -
0
276
1621
Jet
20
20
0
0
30
0
Piston
0
50
20
26
100
0
1620
Jet
420
800
32
30
8
0
Piston
720
700
0
0
0
0
55
-------
Table 35. EMISSION FACTORS FOR SCOTT AFB
(kg/hr)
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
Pollutant
CO
HC
NOX
S02
Particulate
CO
HC
NOX
S02
Particulate
CO
HC
NOX
S02
Particulate
CO
HC
NOX
S02
Particulate
CO
HC
NOX
S02
Particulate
CO
HC
NOX
S02
Particulate
Jet
31.70
22.51
1.17
0.470
1.60
31.70
22.51
1.17
0.470
1.60
3.18
0.60
77.09
3.949
20.16
21.56
15.38
21.29
0.695
7.26
4.07
0.71
50.91
3.388
18.19
10.95
10.96
12.96
1.600
9.15
Piston
59.00
10.30
0.08
0.07
NA
64.50
13.20
0.06
0.07
NA
417.70
9.23
2.15
0.40
NA
160.62
8.96
0.67
0.16
NA
305.80
5.43
1.85
0.30
NA
156.10
3.50
0.64
0.15
NA
56.
-------
Piston aircraft flights are largely by T-29, C-118, and C-131 aircraft,
all of which use Pratt Whitney R-2800 engines. Emission factors for
piston aircraft flights are also listed in Table 35.
GROUND SERVICE VEHICLE OPERATIONS
There are eight petroleum, oil, and lubricants trucks, or POL trucks
used to service aircraft at Scott Field. These run an average of 3
hours 25 minutes each per
grid element number 1620.
9
hours 25 minutes each per day, or a total of 27 hours 20 minutes in
In addition to the POL trucks, the fleet service vehicles listed in
Table 36 are used. Their combined use accounts for approximately 15
9
gallons of fuel per day. Emission factors for these vehicles are
given in the previous Table 27. The hourly emissions are computed by
distributing the daily emissions according to the average hourly per-
cent of daily activity for piston and jet aircraft.
Table 36. GROUND SERVICE VEHICLES
USED AT SCOTT AFB
Service vehicle
Fork lift
Water truck
Multi-stop
High lift
Lavatory truck
Warehouse tug
Step van
Number
2
1
2
1
2
2
2
Emissions from the POL trucks are found using the fuel consumption
rate for fuel trucks given in Table 26 (1.70 gallons/hour) and the
emission factors from Table 27. These total emissions are also
57
-------
distributed by the hourly percent of daily activity to find the
emissions for a particular hour.
FUEL HANDLING AND STORAGE
The volume of fuel stored at Scott AFB is classified. The average use
is 724,000 gallons of jet fuel and 82,000 gallons of avgas per month.
Hourly volumes of fuel pumped can be calculated using the day of week
and hour of day percentages used to find activity. The emissions are
then calculated using the factors given in Table 29 for jet fuel and a
factor of 5 kg/1000 gallons pumped for avgas.
ENGINE TESTING AND MAINTENANCE
Engine testing and maintenance activity does not follow a prescribed
schedule and hence cannot be accurately accounted for on an hourly
basis. Emissions could be computed as an average value for each hour,
but the number of engine runups is so small (about 14 per week) that
the emissions would be lost on an hourly basis.
58
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SECTION VII
CIVILIAN AIRPORTS
INTRODUCTION
Civilian airports can be divided into those with control towers and those
without. This division also applies to the degree of data availability,
and to the volume and type of traffic. Two civilian airports in the
St. Louis AQCR, Spirit of St. Louis and Civic Memorial, have control towers;
the remainder do not.
FLIGHT ACTIVITY
More extensive data are available for Civic Memorial Airport from the FAA
control tower. These data have been reduced in the same manner as those
for Lambert Field. Table 37 gives the monthly percentages of annual traf-
fic, Table 38 gives the percentages by the day of the week, and the per-
centages by the hour of the day are listed in Table 39. These data are
used to compute hourly traffic by the same method described for Lambert
Field.
Uncontrolled airports do not record air traffic volumes. However, FAA
Forms 5010-1 list estimated annual volumes which can be used with the dis-
tribution of traffic found at Civic Memorial. Table 40 presents the annual
volumes from FAA Forms 5010-1. Two exceptions are Civic Memorial and Spirit
of St. Louis for which the volumes were obtained from control tower records.
59
-------
Table 37. PERCENT OF AIR
TRAFFIC BY MONTH AT
CIVIC MEMORIAL
AIRPORT
Month
January
February
March
April
May
June
July
August
September
October
November
December
Monthly percent
7.86
7.95 .
6.98
9.26
9.48
8.88
8.44 :
9.90
7.78
9.48
8.49
5.50
Table 38.
PERCENT OF AIR
TRAFFIC BY DAY
OF WEEK AT
CIVIC MEMORIAL
AIRPORT
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Day percent
17.47
11.14
12.54
13.30
12.76 .
14.10
18.69
-------
Table 39. PERCENT OF AIR
TRAFFIC BY HOUR
OF THE DAY AT
CIVIC MEMORIAL
AIRPORT
Hour
0700 -
0800 -
0900 -
1000 -
1100 -
1200 -
1300 -
1400 -
1500 -
1600 -
1700 -
1800 -
1900 -
2000 -
2100 -
2200 -
0800
0900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
2100
2200
2300
Hourly percent
1.15
4.63
7.20
7.08
8.82
9.51
10.51
10.83
12.45
12.91
6.17
3.19
2.23
2.40
0.56
0.34
Table 40. ANNUAL AIR TRAFFIC VOLUMES AT CIVILIAN
AIRPORTS IN THE ST. LOUIS AQCR
Airport
St. Clair
Wentzyille
Arrowhead
Creve Coeur
St. Charles
St. Charles Smartt
Weiss
Festus
Gelhardt
Sparta
Highland
Greenville
Bi-State Parks
Civic Memorial
Spirit of St. Louis
Annual volume
14,400
27,000
60,500
63,100
63,000
27,000
130,000
15,000
14,183
8,012
28,000
38,734
192,030
156,607
114,426
-------
SPATIAL DETAIL
Five of the general aviation airports lie in more than one grid element.
These are Civic Memorial, Spirit of St. Louis, Bi-State Parks, St. Glair,
and Creve Coeur Airports. The remaining ten are contained in one grid.
Figures 6 through 10 show the layout of the multi-grid airports. The
grids and the key operating modes for each grid for each active runway
are listed in Tables 41 through 45.
The airports which lie in only one grid element are listed, along with the
grid numbers and sizes, in Table 46. Figures 11 through 20 depict the
layout of these remaining airports.
EMISSIONS FROM FLIGHT ACTIVITY
The emission factors for the civilian airports are those for general avia-
tion listed in Table 24 for a mix of general aviation aircraft types. The
average times in mode are given in Table 47. When an airport lies in more
than one grid, the time for each mode is distributed equally among the
grids identified for the mode (Tables 41 through 45). The emissions are
computed by the same multiplication of volume, emission factors, and times
in mode as described for Lambert Field.
GROUND SERVICE VEHICLES
Ground service vehicles at civilian airports are limited to fueling trucks,
and even these are absent at all but the large civilian airports. Most of
these airports provide fueling as at a gas station; airplanes are taxied
to the gas pump for filling.
The fueling truck operation is erratic, depending on the amount of traffic,
and it is not possible to pin down the actual operating characteristics.
62
-------
N
1401
Figure 6. Diagram of Civic Memorial Airport showing grid element overlay
63
-------
Table 41. OPERATING MODES FOR EACH GRID BY ACTIVE RUNWAY AT CIVIC MEMORIAL AIRPORT
Runway
11
29
17
35
Grid
1402
1403
1424
1444
1402
1403
1424
1444
1401
1402
1403
1401
1402
1403
X-coord
4308
4309
4308
4309
4307
Y-coord
755
755
756
757
755
-
Size
1
1
1
2
1
Idle
Taxi
X
X
X
X
X
X
X
X
X
Takeoff
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Climb out
Approach
Landing
x'
X
X
X
X
X
X
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
X
-------
0 500 1000 2000
^ i i
SCALE IN FEET
Figure 7. Diagram of Spirit of St. Louis Airport showing grid
element overlay
65
-------
Table 42. OPERATING MODES FOR EACH GRID BY ACTIVE RUNWAY AT
SPIRIT OF ST. LOUIS AIRPORT
Runway
8
26
Grid
135
160
135
. 160
X-coord
4280
4280
Y-coord
700
705
Size
5
5
Idle
Taxi
X
X
X
X
Takeoff
X
X
X
X
Climb out
Approach
Land ing
X
X
X
. X
Taxi
X
X
X
X
-------
2266
2289
Figure 8. Diagram of Bi-State Parks Airport showing grid element overlay
-------
Table 43. KEY OPERATING MODES FOR EACH GRID FOR EACH RUNWAY AT
BI-STATE PARKS AIRPORT
Runway
4
22
12
30
Grid
2286
2293
2289
2286
2293
2286
2292
2293
2297
2292
2293
2297
Size
2
1
1
2
1
2
1
1
1
1
1
1
Idle
X
X
X
X
X
Taxi
X
X
X
X
X
X
Takeoff
X
X
X
X
X
X
X
X
X
X
X
Land ing
X
X
X
X
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
68
-------
2024
SCALE IN FEET
Figure 9. Diagram of St. Clair Airport showing grid element overlay
69
-------
Table 44. KEY OPERATING MODES FOR EACH GRID FOR EACH RUNWAY AT
ST. CLAIR AIRPORT
Runway
2
20
Grid
2021
2024
2021
2024
Size
2
3
2
3
Idle
X
X
Taxi
X
X
X
X
Takeoff
X
X
Landing
X
X
Taxi
X
X
X
X
70
-------
0 500 1000 1500
I L I I
SCALE IN FEET
Figure 10. Diagram of Creve Coeur Airport showing
grid element overlay
71
-------
Table 45. KEY OPERATING MODES FOR EACH GRID FOR EACH RUNWAY AT
CREVE COEUR AIRPORT
Runway
16
34
7
25
Grid
2103
2104
2103
2104
2103
2104
2125
2103
2104
2105
Size
2
2
2
2
2
2
2
2
2
2
Idle
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
Takeoff
X
X
X
X
X
X
X
X
Landing
X
X
X
X
X
X
X
X
Taxi
X
X
X
X
X
X
X
X
72
-------
Figure 11. Diagram of Sparta Airport - grid element 1633
73
-------
Table 46. GENERAL AVIATION AIRPORTS CONTAINED IN ONE
GRID, ST. LOUIS AQGR
Airport
Wentzville
Arrowhead
St. Charles
St. Charles Smartt
Weiss *
Festus
Gebhardt
Sparta
Highland
Greenville
Grid number
76
2102
241
242
2161
467
883
1633
1709
1815
Grid size (km)
10.0
2.0
5.0
10.0
4.0
2.0
2.0
10.0
10.0
10.0
74
-------
36
N
0 500 1000
i i i
SCALE IN FEET
Figure 12. Diagram of Wentzville Airport - grid element 76
75
-------
0 500 1000
I i f
SCALE IN FEET
Figure 13. Diagram of Arrowhead Airport - grid element 2102
76
-------
T T T T t T
0200 600 1000
i i i i i i
SCALE IN FEET
Figure 14. Diagram of St. Charles Airport - grid element 241
77
-------
+ t i- -r f r
SCALE IN FEET
Figure 15. Diagram of Weiss Airport - grid element 2161
78
-------
SCALE IN FEET
Figure 16. Diagram of Festus Airport - grid element 467
79.
-------
N
500 1000 1500
i | |
SCALE IN FEET
Figure 17. Diagram of St. Charles Smartt Airport - grid element 242
80
-------
N
1000
SCALE IN FEET
Figure 18. Diagram of Highland Airport - grid element 1709
81
-------
N
0
L
600
1200
SCALE IN FEET
Figure 19. Diagram of Gebhardt Airport - grid element 883
82
-------
N
0 600
SCALE IN FEET
L
81
36
Figure 20. Diagram of Greenville Airport - grid element 1815
83
-------
Table 47. TIMES IN MODE FOR GENERAL
AVIATION AIRCRAFT AT
CIVILIAN AIRPORTS
Mode
Idle
Taxi
Takeoff
Landing
Climbout
Approach
Time (minutes)
8.0
8.0
0.3
0.3
4.98
6.00
Table 48. ANNUAL VOLUMES OF FUEL SALES AT THE
GENERAL AVIATION AIRPORTS
Airport
Annual fuel sales
(1000 gallons)
Sparta
Greenville
Gebhardt
Highland
Bi-State Parks
Civic Memorial
Festus
Weiss
Creve Coeur
St. Charles
St. Charles Smartt
St. Clair
Arrowhead
Wentzville
Spirit of St. Louis
36
15
15
26
350
350
42
48
26
48
20
48
48
15
200
84
-------
The emissions from one or two fueling trucks are negligible compared to -
emissions from automobile traffic, and hence ground service vehicle emis-
sions (where they exist) will be excluded from the methodology for civil-
ian airports.
FUEL STORAGE AND HANDLING
Fuel storage "and handling losses are calculated as at Lambert and Scott.
The working loss factor is 5 kg/1000 gallons. The general aviation air-
ports were surveyed to determine their fuel sales. Table 48 shows the
annual gallons of fuel pumped at each airport. These annual figures are
converted to hourly volumes by applying the aircraft volume distributions
of Tables 37, 38, and 39 as described previously for Lambert Field.
ENGINE TESTING AND MAINTENANCE
Engine testing and maintenance at the small airports is limited and some-
times non-existent. Predicting the occurrence or frequency of this emis-
sion source with any accuracy is unreasonable on an hourly basis. Because
of this, and because this source is such a small contributor to emissions
at these airports, emissions from engine testing and maintenance will not
be considered.
85
-------
SECTION VIII
METHODOLOGY SUMMARY
This section presents a "step-by-step" methodology for computing emis-
sions at the airports in the St. Louis AQCR. It is based on the
results of data collection from the individual airports. It is also
\
based on an assessment of the amount of detail which can be extracted
from available data and on the extent to which additional data can be
reasonably and reliably collected in the field. The basic method by
which emissions are estimated is to construct matrices and vectors of
the applicable data and then to add and multiply these matrices and
vectors so that the result is hourly emissions for each of the grid
elements involved.
LAMBERT. FIELD
Emissions From Aircraft Flight Operations
Step 1. Identify the month, day of the week, and hour of the
day for which emissions are to be estimated.
Step 2. Determine the active runway from the wind direction
and/or aircraft category.
Step 3. For the time identified (Step 1), determine the
activity factors for the different aircraft types;
M. = percent of annual volume occurring during
the month (aircraft category i),
D. = percent of monthly"volume occurring on the
given day of the week (aircraft category i),
86
-------
H. = percent of daily volume occurring during
the hour of interest (aircraft category i).
Step 4. For the active runway determined in Step 2, locate"
the k grid elements through which aircraft pass.
Step 5. Determine the percent of activity due to takeoffs
and the percent due to landings for the different
aircraft types for the hour:
to. = percent taking off (aircraft category i),
1 = percent landing (aircraft category i).
Step 6. Compute the takeoff and landing volumes for the hour
for each category as follows:
A'M.-D.'H.. -ton.
VTO. =
(ODM)(108)
where:
VTO. = number of takeoffs for aircraft category i
A = annual air traffic volume
OD = average occurrence of the day of the week
during the month
= 4.43 for 31-day months
= 4.29 for 30-day months
= 4.00 for February (4.14 in a leap year)
Q
10 = factor to convert percentages to decimals.
Likewise,
A-M.-D.-H.'l.
1 (ODH)(108)
Step 7. For the different aircraft categories, activity
volumes, and grid elements identified above,
determine the j engine operating modes for each
grid element.
87
-------
Step 8. Determine the time-in-mode for each aircraft
category for each mode in each grid.
= time-in-mode j for aircraft category i in grid k.
Step 9. Identify the emission rates of the 1 pollutants,
EFji , of the different aircraft categories for
the various engine operating modes.
Step 10. Estimate hourly emissions for aircraft category
i for each pollutant and grid element as follows
EAFO., , = (VTO. EF., . T.M) + (VL. EF... T...)
ikl i ilj jik' v i ilj jik'
Step 11. Compute hourly emissions from all aircraft in
each grid element by repeating Step 10 for all
categories:
EAFO, , = 2 E.. ,
kl ikl
Emissions From Ground Service Vehicles
Step 12. Identify ground service vehicle requirements for
each aircraft type in each grid.
*
CSV. , = ground service vehicle of type m which
is required by aircraft type i in grid
k.
Step 13. Determine service times for each vehicle for each
aircraft.
ST.- = service time of ground support vehicle
type m for aircraft type i.
Step 14. Determine fuel consumption rates for the different
ground service vehicles.
FC = fuel consumption rate for ground service
vehicle type m.
Step 15. For each type of ground service vehicle, identify
the emission rate as a function of fuel consump-
tion.
88
-------
ER = emission rate of ground service vehicle
m type m of pollutant 1.
Step 16. Locate the k grid elements in which ground service
vehicles operate.
Step 17. Compute hourly emissions in each grid from activity
of ground service vehicle type m servicing aircraft
type i.
EGSV. . n = -| V. ' CSV. , ST. FC ER . ,
imkl 2 i imk im- m ml
where:
EGSV., = emissions of pollutant 1 from ground ser-
vice vehicle type m in grid k, and
V. = hourly volume of aircraft type i
= VTO., + VL± .
Step 18. Compute the total ground service vehicle emissions by
grid by pollutant summing over all types of aircraft
and ground service vehicles;
EGSV, - = Z Z EGSV. , , .
kl . imkl
i m
Emissions From Fuel Storage and Handling
Step 19. Locate grid elements in which fuel is stored or
handled.
Step 20. Identify types of fuel and volumes stored.
Step 21. Determine the mean daily high, low, and medium tempera-
ture for the month of interest.
Step 22. Determine the working loss factors for each of the
three temperatures. ^
Step 23. Determine the daily volume of fuel pumped.
Step 24. Distribute the daily volume over 24 hours according
to the diurnal flight activity pattern.
89
-------
Step 25. Compute working losses for the hour of interest
according to the volume of fuel pumped and the
temperature applicable to the time of day as:
EFSH, , = emissions of pollutant 1 in grid k due
to fuel storage and handling.
Emissions From Engine Testing and Maintenance
The data required to compute these emissions are classified. However;
they are judged to be negligible and are neglected.
SCOTT AIR FORCE BASE
Emissions From Aircraft Flight Operations
Step 26. Identify the day of the week and the hour of the day
for which emissions are to be estimated.
Step 27. Determine the active runway from the wind direction
and/or aircraft category.
Step 28. For the time identified (Step 26), determine the
activity factors for the different aircraft categories
(jet and piston):
D. = percent of monthly volume occurring on the
given day of the week (aircraft i),
H. = percent of daily volume occurring during
the hour of interest (aircraft category i).
Step 29. For the active runway determined in Step 27, locate
the k grid elements through which aircraft pass.
Step 30. Compute the total takeoff and landing volumes during
the hour as follows (assume 1/2 landing and 1/2 take-
off):
M'D.-H.
v
1 (ODM) (104)
90
-------
where:
M = mean monthly air traffic volume.
Step 31. For the different aircraft categories, activity
volumes, and grid elements identified above,
determine the j engine operating modes for each
grid element.
Step 32. Determine the time -in -mode for each aircraft
category for each mode in each grid.
T.., = time-in-mode j for aircraft category i in grid k.
Step 33. Identify the emission rates of the 1 pollutants,
E^il-p of the different aircraft categories for
the various engine operating modes.
Step 34. Estimate hourly emissions for aircraft type i
for each grid element as:
Step 35. Compute hourly emissions from all aircraft in
each grid element by repeating Step 34 for all
categories :
Emissions From Ground Service Vehicles
Step 36. Identify ground service vehicle requirements for
each aircraft type in each grid. .
CSV. , = ground service vehicle of type m which
is required by aircraft type i in grid
k.
Step 37. Determine service times for each vehicle for each
aircraft.
ST. = service time of ground support vehicle
type m for aircraft type i.
91
-------
Step 38. Determine fuel consumption rates for the different
ground service vehicles.
FC = fuel consumption rate for ground service
vehicle type m.
Step 39. For each type of ground service vehicle, identify
the emission rate as a function of fuel consump-
tion.
ER - = emission rate of ground service vehicle
type m of pollutant 1.
Step 40. Locate the k grid elements in which ground service
veh icles operates.
Step 41. Compute hourly emissions in each grid from activity
of ground service vehicle type m servicing aircraft
type i.
EGSV. ,, = 1/2 V. CSV. . ST. FC ER - ,
imkl _ i imk im m ml
where:
EGSV., = emissions of pollutant 1 from ground ser-
vice vehicle type m in grid k, and
V.- = hourly volume of aircraft type i
= VTO.. + VL±.
Step 42. Compute the total ground service vehicle emissions
by grid by pollutant summing over all types of air-
craft and ground service vehicles:
EGSV, . = Z 2 EGSV. . , .
kl £ m imkl
Emissions From Fuel Handling and Storage
Step 43. Locate grid elements in which fuel is stored or
handled.
Step 44. Identify types of fuel and volumes stored.
(Actual volume stored is classified. Assumed
storage volume equals one month's supply at cur-
rent usage rates.)
92
-------
Step 45. Determine the mean daily high, low, and medium
temperature for the month of interest.
Step 46. Determine the working loss factors for each of
the three temperatures.
Step 47. Determine the daily volume of fuel pumped.
Step 48. Distribute the daily volume over 24 hours accord-
ing to the diurnal flight activity pattern.
Step 49. Compute working losses for the hour of interest
according to the volume of fuel pumped and the
temperature applicable to the time of day as:
EFSH, . = emissions of pollutant 1 in grid k due
to fuel storage and handling.
Emissions from Engine Testing and Maintenance
Step 50. Locate the grid elements in which engine testing
occurs.
Step 51. Determine the testing schedule (frequency and times
of occurrence) for the different types of engines
tested.
Step 52. Determine testing cycle for each engine type.
XT. = time-in-mode j for engine type n.
Step 53. Apply the emission factors for the engine and modes
to determine the emissions from engine testing:
EETn - TT.nEFjn,
where:
EET = emissions from testing engine type n.
Step 54. Determine the total emission factors from engine
testing by summing over all engine types tested.
EET = 2 EET .
n n
If engine testing occurs during specific hours,
apply the emissions to these hours.
93
-------
If it occurs randomly over a longer time period
(e.g., an 8-hour working day, 24 hours, or a
week), distribute the emissions equally over the
time period.
CIVILIAN AIRPORTS
Emissions from Flight Operations at Controlled Airports
Step 55. Identify the month, day of the week, and hour of
the day for which emissions are to be estimated.
Step 56. Determine the prevailing wind direction for the
month .
Step 57. Determine the active runway from the wind direc-
tion and/or aircraft category.
Step 58. For the time identified (Step 1), determine the
activity factors for the different aircraft
types :
M. = percent of annual volume occurring during
the month (aircraft category i) ,
D. = percent of monthly volume occurring on the
given day of the week (aircraft category
H. = percent of daily volume occurring during
the hour of interest (aircraft category
i).
Step 59. For the active runway determined in Step 2, locate
the k grid elements through which aircraft pass.
Step 60. Compute the air traffic volume for the hour from:
A'M.-D.'H.
(ODM) (10)
where the factors are defined in Steps 3 and 6 and
the subscript i refers only to general aviation
aircraft.
94
-------
Step 61. For the different aircraft categories, activity
volumes, and grid elements identified above,
determine the j engine operating modes for each
grid element.
Step 62. Determine the time-in-mode for each aircraft
category for each mode in each grid.
T.., = time-in-mode j for aircraft category i in grid k.
J1K
Step 63. Identify the emission rates of the 1 pollutants,
EFiij, of the different aircraft categories for
the various engine operating modes.
Step 64. Estimate hourly emissions for aircraft type i for
each grid element:
EAFO.kl = VlEP1]LJ T..k
Emissions from Flight Operations at Uncontrolled Airports
Step 65. Determine the annual volume of air traffic from
FAA Form 5010 and discussions with airport per-
sonnel.
Step 66". Determine the prevailing wind direction for the
month .
Step 67. Determine the active runway from the wind direc-
tion and/or aircraft category.
Step 68. For the time identified for estimating emissions,
determine the activity factors for the different
aircraft types:
M. = percent of annual volume occurring during the
the month (aircraft category i) ,
D. = percent of monthly volume occurring on the
given day of the week (aircraft category
H. = percent of daily volume occurring during
the hour of interest (aircraft category
95
-------
Step 69. For the active runway determined in Step 67,
locate the k grid elements through which air-
craft pass.
Step 70. Compute the air traffic volume for the hour from:
..
V = - - - -
1 (ODM) (106) '
Step 71. For the different aircraft categories, activity
volumes, and grid elements identified above,
determine the j engine operating modes for each
grid element.
Step 72. Determine the time-in-mode for each aircraft
category for each mode in each grid.
= time-in-mode j for aircraft category i in grid k.
Step 73. Identify the emission rates of the 1 pollutants,
EF.,. of the different aircraft categories for
the various engine operating modes.
Step 74. Estimate hourly emissions for aircraft type i
for each grid element as:
Step 75. Compute hourly emissions from all aircraft in
each grid element by repeating Step 74 for all
categories:
EAFOkl = fEAFO.kl
Step 76. Locate grid elements in which fuel is stored or
handled.
.Step 77. Identify types of fuel and volumes stored.
Step 78. Determine the mean daily high, low, and medium
temperature for the month of interest.
Step 79. Determine the working loss factors for each of
the three temperatures .
Step 80. Determine the daily volume of fuel pumped.
96
-------
Step 81. Distribute the daily volume over 24 hours
according to the diurnal flight activity
pattern.
Step 82. Compute working losses for the hour of
interest according to the volume of fuel
pumped and the temperature applicable to
the time of day as:
EFSH, .. = emissions of pollutant 1 in grid
k due to fuel storage and han-
dling.
97
-------
SECTION IX
IMPROVING ESTIMATES
Table 4 (page 2) displays the percent of emissions contribution by source
at 0'Hare Airport. Except for CO, aircraft are the predominate source of
emissions, and even for CO they account for greater than two thirds. It
is immediately evident then that an improved knowledge of aircraft opera-
tions will offer the most improvement in emissions estimation. There is
the added benefit that a better knowledge of aircraft operations will im-
prove the estimates for ground service vehicles and fuel handling and
storage, since these depend ultimately on aircraft for their employment.
The first step would be to find precisely the volume and makeup of air
traffic for a given hour. However, it is essentially impossible to pre-
dict accurately what will occur in a given hour. Since this probably
accounts for the greatest uncertainty in the hourly emissions estimate,
the greatest improvement would come about from actually gathering data
during the period of interest.
After volume and makeup are known, the next important factor is time in
mode, since this is the multiplying factor for a relatively constant emis-
sion rate for a given mode. There is not likely to be much variation in
takeoff, climbout, approach, or landing times for a given type of aircraft,
more variation will arise from idle and taxi time differences although
even these were found to be fairly standard upon observation.
On this level of detail the actual pollutant for which emissions are being
estimated becomes important. Idle and taxi modes have a relatively high
98
-------
emission rate for CO and hydrocarbons; NO., emissions are higher during
X
takeoff, climbout, and approach; and CO and NOX emissions are high during
land ing.
Airport emissions cannot be precisely estimated due to all the influencing
factors described in Section III. It is felt that the methodology given
in this report strikes a good balance between maximum potential accuracy
and the rapidly increasing level of effort required as estimates become
incrementally more precise»
99
-------
SECTION X
REFERENCES
1. Allen, P. W. Regional Air Pollution StudyAn Overview. Paper 73-
21, 66th Annual Meeting of the Air Pollution Control Association,
Chicago, June 1973.
2. An Air Pollution Impact Methodology For AirportsPhase I. U.S.
Environmental Protection Agency Report APTD-1470, January 1973.
3. Compilation of Air Pollutant Emission Factors. 2nd Ed., U.S.
Environmental Protection Agency, April 1973.
4. Official Airline Guide. The Reuben H. Donnelley Corporation,
Oak Brook, Illinois.
5. Bogdan, L. et al. Analysis of Aircraft Exhaust Emission Measure-
ments. Cornell Aeronautical Laboratory, Incorporated, Buffalo,
New York, October 1971.
6. Elliott, George. Allied Aviation Fueling, Lambert Field, St. Louis.
Personal Communication.
7. Climatic Atlas of the United States. U.S. Department of Commerce,
Environmental Science Services Administration, Environmental Data'
Service, 1968.
8. Bulletin on Evaporation Loss in the Petroleum IndustryCauses and
Control. American Petroleum Industry, Bull. 2513, Washington, D.C.,
1973.
9. Dziuban, Lt. Scott AFB, Personal Communcation.
10. Naugle, D. F. and B. T. Delaney. United States Air Force Aircraft
Pollution Emissions. U.S. Air Force Report AFWL-TR-73-199, 1973.
11. Pratt Whitney, Hartford, Connecticut. Personal Communication.
100
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OFF-HIGHWAY MOBILE SOURCE INVENTORY
-------
EPA-450/3-75-002
METHODOLOGY
FOR ESTIMATING EMISSIONS
FROM OFF-HIGHWAY
MOBILE SOURCES
FOR THE RAPS PROGRAM
by
Charles T. Hare
Southwest Research Institute
8500 CuJebra Road
San Antonio. Texas 78284
Contract No. 68-02-1397
EPA Project Officer: Charles C. Masser
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, N. C. 27711
October 1974
-------
FOREWORD
The work described in this report was performed by Southwest
Research Institute for the U.S. Environmental Protection Agency under
Contract No. 68-02-1397, "Methodology for Estimating Emissions from i
Off-Highway Sources for the RAPS Program. " The project grew out of
RFP No. DU-74-A041 and SwRI's responding Proposal No. 11-9962,
dated January 7, 1974. The project was initiated on March 12, 1974;
and the technical effort was completed on September 30, 1974. It was
identified at SwRI as Project No. 11-3916.
The project leader for SwRI has been Charles T. Hare, Manager,
Advanced Technology, Department of Emissions Research. Overall
supervision has been provided by Karl J. Springer, Director, Depart-
ment of Emissions Research. Project Officer for the Environmental
Protection Agency has been Charles C. Masser, National Air Data
Branch. j
111
-------
ABSTRACT
Emissions, population, and usage data existing in the technical
literature have been collected and organized for the following unregulated
sources: outboard motors, snowmobiles, motorcycles, lawn and garden
equipment, construction equipment, industrial equipment, and farm
equipment. The investigation has been limited to mobile sources utilizing
internal combustion engines and thus has not included plant processes or
stationary engines.
Sources of data for individual counties have been compiled,
mostly items which may have some correlation with equipment popu-
lation, usage, or emissions. Data found in these sources have been
restated only where necessary to other phases of the program.
Methodologies for estimating emissions and fuel consumption
on a county basis have been developed for the sources noted above.
They have been demonstrated for counties in the St. Louis Metropolitan
Air Quality Control Region (AQCR 070), and their strengths and weak-
nesses have been discussed. Methods have also been developed to ap-
portion county emissions estimates to grid elements, but they have not
been demonstrated. The exhaust constituents assessed include hydro-
carbons (HC), carbon monoxide (CO), oxides of nitrogen (NOX), parti-
culate, aldehydes (RCHO), and oxides of sulfur (SOX). For outboard
motors, neither particulate nor aldehyde data were available; but carbon
dioxide (CC^) emissions were included.
IV
-------
TABLE OF CONTENTS
FOREWORD
ABSTRACT iv
LIST OF TABLES vii
I. INTRODUCTION 1
II. SUMMARY OF NATIONAL AND STATE DATA ON
EQUIPMENT POPULATIONS, USAGE, AND EMISSIONS 3
A. OUTBOARD MOTORS 3
B. SNOWMOBILES 5
C. MOTORCYCLES 6
D. LAWN AND GARDEN EQUIPMENT 10
E. CONSTRUCTION EQUIPMENT 13
F. INDUSTRIAL EQUIPMENT 15
G. FARM EQUIPMENT 16
III. SOURCES OF DATA ON COUNTIES 21
IV. METHODOLOGY FOR COUNTY EMISSIONS ESTIMATES 23
A. OUTBOARD MOTORS 23
B. SNOWMOBILES 25
C. MOTORCYCLES 26
D. LAWN AND GARDEN EQUIPMENT 27
E. CONSTRUCTION EQUIPMENT 28
F. INDUSTRIAL EQUIPMENT 28
G. FARM EQUIPMENT 29
V. DEMONSTRATION OF COUNTY EMISSIONS ESTI-
MATION METHODOLOGIES 31
A. OUTBOARD MOTORS 31
B. SNOWMOBILES 32
C. MOTORCYCLES 34
D. LAWN AND GARDEN EQUIPMENT 34
E. CONSTRUCTION EQUIPMENT 37
F. INDUSTRIAL EQUIPMENT 39
G. FARM EQUIPMENT 42
-------
TABLE OF CONTESTS (continued)
Page
VI. METHODOLOGY FOR GRID ELEMENT EMISSIONS
ESTIMATES 45
A. OUTBOARDS 47
B. SNOWMOBILES 48
C. MOTORCYCLES 48
D. LAWN AND GARDEN EQUIPMENT 49
E. CONSTRUCTION AND INDUSTRIAL EQUIPMENT 49
F. FARM EQUIPMENT 50
VII. SUMMARY 51
REFERENCES 53
APPENDIXES
A. TABULAR DATA ON POPULATION, USAGE,
AND EMISSIONS OF SELECTED MOBILE SOURCE
CATEGORIES A-l
B. LIST OF COUNTY DATA SOURCES B-l
C. DOCUMENTATION OF COUNTY METHODOLOGY
DEVELOPMENT C-l
D. UTM TO GEOGRAPHIC COORDINATE CONVERSION
PROGRAM D-l
VI
-------
LIST OF TABLES
Table Page
1 Factors to Correct 1973 State Outboard Boat Regis-
trations for Exemption of Smaller Craft 4
2 Air Pollutant Emission Factors and Fuel Consump-
tion for Outboard Motors 5
3 Emission Factors and Fuel Consumption for Snow-
mobiles with 2-stroke Engines 6
4 Emission Factors and Fuel Consumption for Rotary-
Engine Snowmobiles 7
5 Annual Mileage Data for Motorcycles by Engine Type
and Size 8
6 Mileage Estimates Recommended for Motorcycles
and Population Breakdowns 8
7 Generalized Motorcycle Emission Factors and Fuel
Consumption by Engine Type 9
8 Motorcycle Exhaust Emission Factors and Fuel Con-
sumption per Unit Distance by Engine Type and Size 10
9 Motorcycle Annual Exhaust Emission Factors and
Fuel Consumption by Engine Type and Size 11
10 Assumed Populations of Lawn and Garden Equip-
ment (10/31/74) .11
11 Emission Factors for Lawn and Garden Equipment
by Type of Engine and Source of Information 12
12. Recommended Emission Factors and Fuel Usage-
for Lawn and Garden Equipment 13
13 Estimates of Construction Machinery Populations,
Usage, and Rated Horsepower 14
14 Estimates of National Construction Equipment Emis-
sions and Fuel Consumption 14
15 Estimates of Heavy-Duty Industrial Engine Popu-
lation, Rated Power, and Annual Usage 15
vn
-------
LIST OF TABLES (continued)
Table Page
16 Emissions and Fuel Consumption of Industrial
Engines 16
17 Summary of Motorized Farm Equipment Annual
Usage Estimates 17
18 Emission Factors and Fuel Consumption for Farm
Equipment 19
19 Major Sources of Data on Counties 21
20 County Data to be Used in Determining Outboard
Motor Emissions Impact 32
21 Emissions and Fuel Consumption of Outboard
Motors for Counties in AQCR 070 33
22 Snowmobile Emissions and Fuel Consumption,
Counties in AQCR 070 35
23 Computation of Emission Factors and Fuel Con-
sumption for Motorcycles in AQCR 070 34
24 Emissions and Fuel Consumption of Motorcycles,
Counties in AQCR 070 36
25 Lawn and Garden Engine Emissions and Fuel Con-
sumption for Counties in AQCR 070 38
26 Computation of Illinois and Missouri Construction
Equipment Emissions as Percentages of National
Totals 37
27 Construction Equipment Emissions and Fuel Con-
sumption lor Counties in AQCR 070 40
28 Computation of Industrial Equipment Population
Percentages for Counties in AQCR 070 39
29 Industrial Engine Emissions and Fuel Consumption
for Counties in AQCR 070 41
30 Farm Equipment Populations for Counties in AQCR 070 42
Vlll
-------
LIST OF TABLES (continued)
Table Page
31 Farm Equipment Emissions and Fuel Consumption
for Counties in AQCR 070 43
32 Summary of Emissions from Engine Categories
Under Study 44
33 Impact of Off-Highway Sources on Emissions in
AQCR 070 52
IX
-------
I. INTRODUCTION
This study is an extension of previous work by SwRI on emissions
from uncontrolled mobile sources using internal combustion engines, with
emphasis on estimates for counties and smaller areas. Prior studies
conducted at SwRI under Contract No. EHS 70-108 have been responsible
for the development and/or publication of a substantial fraction of avail-
able data for a number of engine categories. These categories include
locomotives, outboard motors, motorcycles, small utility engines,
farm equipment, construction equipment, industrial equipment, gas
turbine electric utility powerplants, and snowmobiles.
Of the categories noted above, seven were studied during this
project (outboards, snowmobiles, motorcycles, lawn and garden, con-
struction, industrial, and farm). The first objective was to compile and
summarize all available data on emissions, population, and usage of
engines in these categories. Sources consulted were reports for EPA
and other agencies, technical papers, state motor vehicle registration
departments, statistical publications, and others.
Another objective was to compile a list of data sources for coun-
ties and other small areas, and the results of this effort appear as Ap-
pendix B. Although a great deal of direct information on engine emissions,
population, and usage is not available for counties, sufficient data were
uncovered which are relatable to the desired variables to have made the
effort worthwhile. The final objectives of this study were to derive
methodologies for estimating emissions down to the county and grid
element levels,and to demonstrate the county methodologies for AQCR
070 (St. Louis Metropolitan). Even having accomplished these tasks,
the problem remains that no data are available against which the derived
results can be checked.
-------
II. SUMMARY OF NATIONAL, AND STATE DATA ON
EQUIPMENT POPULATIONS, USAGE, AND EMISSIONS
Engines for which emission estimation methodologies have been
developed under the subject program are used in a. wide variety of leisure
and utilitarian applications. They represent all major non-automotive
engine markets up to about the 500 horsepower class. As a consequence
of this diversity in size, type, and field of application, data relating to
population, usage, and emissions of the engines are widely scattered
in the literature. This section of the report will summarize pertinent
data found for each engine category, as a matter of convenience and for
future reference,,
A. OUTBOARD MOTORS
Data on the population of outboard motors or outboard boats and
their distribution by state are available through the U.S. Coast Guard(^)
and The Boating Industry magazine(^). Calendar year 1973 outboard boat
registrations in the 48 contiguous states plus the District of Columbia
totalled 4. 98 million, but some states did not register all outboard boats
operating on their waters. All registration exemptions for small boats
ran out at the end of 1973, however, so the 1974 registration total
(available in 1975) should show a strong increase due to inclusion of a
number of previously unregistered craft. The 1973 Boating Industry
total for outboard motors in the same states was 70 51 million, but the
exact basis for this figure is not known.
Breakdowns of the USCG^1) 1973 outboard boat registrations and
the Boating Industry^2' 1973 outboard motor population by state are given
in Appendix A, Tables A-l and A-2, respectively. The reliability of
boat population figures for 14 states will be in doubt until 1974 registra-
tion figures become available, but a correction for unregistered boats
can be estimated using an analysis of the total U.S. outboard motor
population by rated power category' »'. Assuming that boats in the
power categories 0-6.9 hp and 700-19»9 hp are uniformly distributed
within the categories, the correction factors shown in Table 1 can be
used with corresponding state registrations to come up with more
*Numbers.in parentheses indicate list of References at the end of this
report.
-------
Table 1. FACTORS TO CORRECT 1973 STATE OUTBOARD BOAT
REGISTRATIONS FOR EXEMPTION OF SMALLER CRAFT
Exemption
5 hp or below
7. 5 hp or below
8 hp or below
9. 9 hp or below
10 hp or below
State (s)
TN, WV, WY
MD, MO
MT
ND
AR, FL, GA, LA,
ME, MS, NC
Calculated
% Unregistered
22,5
32.5
33.6
37.6
37.8
Correction factor
1.29
1.48
1.51
1.60
1.61
representative values. Performing this correction for all the states
exempting very small craft yields an additional 0. 51 million outboard
boats, making the estimated current total about 5. 5 million0
Relatively little good information is available on usage of out-
board motors or outboard boats, so estimates have been used previously(3)
to compute the national impact of outboards. It is expected that climatic
conditions have a strong influence on outboard usage, so the usage aspect
will be handled with the emissions estimation methodology in Section IV.
Several studies have been conducted on outboard motor emissions^""),
but only the first one (References 3 and 4) has been published at this
time. In examining'outboard motor emissions data from all investiga-
tions, attention must be paid to segregating emissions computed or
measured to be ending up in the atmosphere from those ending up in the
water. Depending on the exhaust constituent of interest, fractions going
through the water to the atmosphere range from 40 or 50 to nearly 100 per-
cent. Emission factors for use in making small-scale atmospheric emission
estimates are presented in Table 2, along with fuel consumption factors.
It is anticipated that emission factors in grams per motor hour and fuel
consumption factors in gallons per motor hour will be the most useful
of those given, but factors are also given in other units for convenience.
A number of states keep data on registration of outboard boats
and/or outboard motors by county, but requests for such data were not
sent to all states. In the course of looking for socioeconomic data,
however, a number of state statistical publications were obtained which
contained boat registration data. County registrations were obtained in
this manner for New "₯ork(7), Ohio(^), South Carolina(9), and Wiscon-
sin(10/; but some of these data were out of date by as much as seven
years.
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Table 20 AIR POLLUTANT EMISSION FACTORS AND
FUEL CONSUMPTION FOR OUTBOARD MOTORS
Fuel consumption
Units
g/ rated hp hr
gal/ rated hp hr^
kg /motor hra
gal/motor hra» b
Value
174.
0.0622
4.28
1.53
Atmospheric exhaust emissions
Units
g/ rated hp hr
g /motor hra
g/gal fuelb
HC
31.3
769.
503.
CO
92.7
2,280.
1,490.
C02
1430
3,510.
2,300.
NOX
0. 18
4.5
3.0
SOX
0.18
4,4
2.9
aBasedon average motor rated horsepower of 24.6.
bBasedon fuel density of 6. 17 lbm/gal = 0. 739 g/ml.
B. SNOWMOBILES
Registration of snowmobiles by state is summarized at least
annually by the International Snowmobile Industries Association (ISIA),
and the latest data available are dated March 1, 1974(11). The total of
all U.S. registrations as given in the ISIA data is 1. 71 million, and an
additional 00 86 million are listed for Canada. Tabulation of all the state
registrations is given in Table A-3 of Appendix A. In addition to the ISIA
data, the only information found on snowmobile population was registra-
tion data by county for New York'^'.
Annual usage of snowmobiles is not a well-defined quantity, and
all values used in the literature to date have been estimated'^-14) ^ The
estimates used in the referenced publications were 60 hr/yr and 100 hr/
yr, respectively, the former figure being based on qualitative information
from a number of sources. For lack of information to the contrary, the
60 hr/yr estimate will be adopted for the purposes of this report.
Data on exhaust emissions from snowmobile engines are available
from three sources(12-16)) although information from Reference 12 (and
Reference 13, which is based on Reference 12) is much more comprehen-
sive and complete than that from the others. Reference 16 contains
rudimentary raw data (one speed, several loads and mixtures) on one
engine, and Reference 14 develops an emission factor based on the same
engine. Reference 15 contains basic data (one speed, one load, several
mixtures) on one engine and no real attempt at computing an emission
factor. Reference 12 includes data on four engines (three 2-strokes and
one rotary), each of which was operated at 29 speed/load conditions
using manufacturers' recommended carburetor settings. Where pos-
sible, all these data will be taken into consideration; but it is obvious
that data from Reference. 12 will be relied upon most heavily,,
-------
Emission factors and fuel consumption for 2-stroke snowmobiles
have been computed in several sets of units, and they are presented in
"Table 3. The emissions values obtained for a rotary snowmobile engine^2)
Table 3. EMISSION FACTORS AND FUEL CONSUMPTION
FOR SNOWMOBILES WITH 2-STROKE ENGINES
Units
g/ rated hp hr
g/unit hr
g/gal fuel
kg/unit yr
Emission factors^2'
HC
23.1
630.
670.
37.8
CO
35o9
978.
1000.
58.7
NCV
.A.
0.367
10.0
11.
0.60
Part.
1.02
27.9
30.
1.67
RCHO
0.34
9.2
9.8
0.55
SOX
0.031
0.85
0.90
0.05
Emission factors' I4)
HC
29.
580.
58.
CO
105.
2100.
210.
NOX
0.32
6.40
0.64
Fuel consumption
(12)
Units
g/ rated hp hr
gal/ rated hp hr
kg /unit hr
gal/unit hr
kg/unit yr
gal/unit yr
Value
97.
0.035
2.6
0.94
160.
56.
Notes:
average rated hp = 20(14) or 27. 22(12)
annual usage = 100 hr(14) or 60 hr(12)
average load factor = 0. SO^14) or 0. 210(12)
fuel density = 6. 2 lbm/gal = 0. 743 g/ml
are not included in Table 3, because they were different than 2-stroke
levels in several cases. It should be noted that the values are based on
tests of only one engine, a 35 hp unit, and that care must be exercised
in choice of scaling factors when the data are used to represent other
rotaries. Emission factors and fuel consumption for rotary-engined
snowmobiles are given in Table 4 in several sets of units. Factors from
both Tables 3 and 4 can be used for snowmobile populations where the
fraction of each type in the population is known or can be estimated. The
OMC rotaries have been on the market less than three seasons as of now
(10/74); so if the population breakdown is not available, only small
errors would be introduced by assuming that the population is all of the
2-stroke type and using factors from Table 3.
C. MOTORCYCLES
Motorcycle registrations by state are compiled by the U. S.
Department of Transportation, Federal Highway Administration. These
data are published annually in Statistical Abstract of the United States(17),
Highway Statistics'22), and elsewhere. Another source for reasonably
accurate state registration data is Automotive Industries(18) magazine,
-------
Table 4. EMISSION FACTORS AND FUEL, CONSUMPTION
FOR ROTARY-ENGINE SNOWMOBILES(12)
Fuel consumption
Units
g/ rated hp hr
gal/ rated hp hr
kg /unit hr
gal/unit hr
kg /unit yr
gal/unit yr
Value
124.
0.044
4.3
1.54
260.
92.
Emission factors
Units
g/ rated hp hr
g/unit hr
g/gal fuel
kg /unit yr
HC
4. 14
145.
94.
8.70
CO
71.7
2510o
1600.
151.
NOX
0061
21.2
14.
1.27
Part.
0.29
10.2
6.6
006l
SOX
0.052
1.81
1.2
0.11
Notes: rated hp = 35
annual usage = 60 hr
average load factor = 0.217
fuel density =6.2 lbm/gal = 0. 743 g/ml
and this source has a shorter time lag than the official government pub-
M «^
lications. The latest registration data available now are for 1973V °',
indicating total U. S. registered motorcycles to be about 4.36 million.
A recent national survey(19) indicates that 21 percent of all motorcycles
may be unregistered, bringing the estimated national total to 5.52 million
units. The 1973 registrations by state mentioned above are tabulated in
Table A-4 of Appendix A.
A great deal of information is available now regarding motorcycle
usage, but none of it is without flaws. The most comprehensive sources
are two statistical surveys^'* 20) conducted quite recently by a marketing
research firm. The major problems with these data are: (1) that all
survey participants were city residents, and (2) that the researchers used
a "median average" rather than the arithmetic mean for expressing yearly
mileage to compensate for what they felt to be respondent or interviewer
errors resulting in high mileage figures. The influence of sampling
only in cities cannot be estimated quantitatively, but a recent publication
on fuel usage estimation by county(21) indicates annual light-duty vehicle
mileage in rural areas may be significantly greater than that in urban
areas. How well this directional generality would work for motorcycles
is not known.
Annual mileage data in terms of medians and means from the two
surveys'19, 20) are shown in Table 5 as functions of engine size and type.
Most of the trends from the two surveys compare rather well, but the
median average mileages are grossly different. This result is difficult
to explain in view of the very good agreement of the overall mean averages;
-------
Table 50 ANNUAL MILEAGE DATAa FOR MOTORCYCLES
BY ENGINE TYPE AND SIZE
Engine size
90cc or less
91-190cc
191-290cc
over 290cc
All
Annual mileage by type'-'-')
2 -stroke
828
1,644
1,968
2,796
1,896
4-stroke
1,560
1,980
3,000
4,464
3,456
Mean average
All
1, 152
1,764
2,232
3,948
2, 280
3,276
Annual mileage by type (20)
2 -stroke
620
1, 170
1,630
2,420
1,420
4-stroke
480
1,240
1,300
2,740
1,870
Mean average
All
560
1,170
1,570
2,580
1,590
3,460
a"Median average" mileages except where otherwise noted.
and in combination with other mathematical and logical errors in the survey
analyses, the disagreement makes strong confidence in the overall survey
results impossible.
Other mileage estimates for motorcycles^^, 23) nave been largely
a matter of speculation, except one set of figures released in 1973 by the
Motorcycle Industry Council (MIC)(24). These estimates were 1900 miles
per year for machines under lOOcc, 2500 miles per year for 100-199cc
units, 3000 miles per year for bikes in the 200-299cc class, and 4500
miles per year for units 300cc or larger. Even with all the information
available, there is not a clear-cut choice of existing mileage data which is
obviously accurate. Consequently, the estimates in Table 6 are recom-
mended in lieu of more reliable information. The population percentages
Table 6. MILEAGE ESTIMATES RECOMMENDED FOR
MOTORCYCLES AND POPULATION BREAKDOWNS
Engine size
90cc or less
91- 90cc
191-290cc
over 290cc
Annual mileage
750
1400
2100
3000
National mean 1996
% of population(!9)
21
27
11
41
Overall
% of population^ 9)
2 -stroke
11
19
8
13
51
4-stroke
9
8
3
29
49
aComputed using population percentages from Reference 19.
8
-------
by engine size in Table 6 can be used for the nation as a whole, but more
accurate regional size breakdowns^ 9) are given in Appendix A, Table
A-5. The breakdown by engine type in Table 6 can be used for all areas,
It should be noted that where parts of an Air Quality Control Region (AQCR)
fall into two or more motorcycle "regions", it would probably be logical
to use compromise population percentages by engine size for the entire
area rather than use different ones on either side of a boundary,,
Data on emissions from motorcycles are available from several
sources^S-Z?)^ ^^ those given in Reference 25 (and refined in Reference
26, a paper based on Reference 25) are by far the most comprehensive.
Emissions data given in the Olson report(28) are not useful in computing'
emission factors due to the inaccuracy of the old procedures used.
Factors listed by AESi in its report to the California Air Resources
Board(27) are essentially equal to those developed by SwRI in its report
to the Environmental Protection Agency(25) and almost equal to the
refined factors(26).
If a simplified calculation of motorcycle emissions is desired,
data from Table 6 can be used in conjunction with emission factors from
Table 7. A more detailed analysis can be performed (by "region", as
Table 7. GENERALIZED MOTORCYCLE EMISSION FACTORS
AND FUEL CONSUMPTION BY ENGINE TYPE
Application
on- road
off -road
Engine
type
2-s
4-s
2-s
4-s
Data
'ref.
25
26
27
25
26
27
27
27
Emissions in grams per mile
HCa
16.
17.
16.
3o5
3.6
3.5
24.
4.0
CO
27.
30.
27.
33.
34.
33.
32.4
39.6
N0xb
0.12
0.11
0. 12
0.24
0.23
0.24
0.06
0.36
Part.
0.33
Oo36
0033
0.046
0.048
0.04
0.33
0.04
RCHO
Oo 11
0. 12
0.047
0.050
sox
0.038
0.040
0.022
0.023
-
Fuel,
mi /gal
41
44
-
alncludes an allowance for evaporative emissions.
es not reflect correction to new driving schedule for testing
smaller (under 170cc) motorcycles(29)>
defined in Table A-5) by using emission factors from Table 8 and population
breakdowns from Table 6. These factors can be expressed in other units
when the annual mileage estimate from Table 6 is used, and the results
-------
Table 8. MOTORCYCLE EXHAUST EMISSION FACTORS
AND FUEL CONSUMPTION PER UNIT DISTANCE
BY ENGINE TYPE AND SIZE
Engine size
90cc or lessa
91-190cca
191-290cc
over 290cc
Emissions from 2- stroke motorcycles
in grams per mile
HC
6
10
18
25
CO
6
12
30
50
NOX
Ooll
0. 10
0.04
0.04
Part.
0. 14
0.19
0.35
0.55
RCHO
0. 10
0. 11
0. 13
0. 14
sox
0,021
0.025
0.043
0.057
Fuel usage,
mi /gal
80
69
40
30
Engine size
90cc or lessa
91-190cca
191-290cc
over 290cc
Emissions from 4-stroke motorcycles
in grams per mile
HC
202
2.6
3.4
4.8
CO
20
24
32
46
NOX
0.22
0.20
0. 17
0, 11
Part.
0.022
0.030
0.045
0.070
RCHO
0.018
0.026
0.044
0.079
sox
0.014
0.017
0.022
0.031
Fuel usage,
mi/gal
88
74
56
40
aOnly the NOX values have been corrected to reflect the new smaller-
bike (under 170cc) cycle(29)^
are given in Table 9. Note that all the factors developed thus far except
those given in Table 7 include no evaporative emissions. The data and
method required to estimate evaporative emissions will be presented with
the county motorcycle emissions estimation methodology in Section IV.
D. LAWN AND GARDEN EQUIPMENT
In estimating the number of small utility engines used nationwide
in lawn and garden equipment, there are no registration statistics and
very few reliable data on sales or production. The best estimates available
at present are summarized in Table A-6 of Appendix AU^, 30, 31)^ an(j they
are discussed and evaluated in a previous report to the Environmental
Protection Agency'^2) ancj a technical publication based on that repo
In attempting to account for utility engines used for lawn and garden appli-
cations, a. major supposition is that the equipment should be distributed
more or less in proportion to the number of single-unit housing structures.
It has also been assumed that a rough balance should occur between extra
units operated on commercial or public property and homes which have no
engine-powered equipment,, A good check on these assumptions is to note
10
-------
Table 9. MOTORCYCLE ANNUAL, EXHAUST EMISSION FACTORS
AND FUEL CONSUMPTION BY ENGINE TYPE AND SIZE
Engine size
90cc or less
91-190cca
191-290cc
over 290 cc
Emissions from 2- stroke motorcycles
in kg per year
HC
4.5
14.
38.
75«
CO
4.5
17.
63.
150.
NOX
0.082
0. 14
0.08
0. 12
Part.
0. 10
0.27
0.74
1.65
RCHO
0.075
0. 15
0.27
0.42
sox
0.016
0.035
0.090
0.17
Fuel usage,
gal/yr
9.4
20.
52.
100.
Engine size
90cc or lessa
91-190cca
191-290cc
over 290 ccc
Emissions from 4 -stroke motorcycles
in kg per year
HC
1.6
3.6
7.1
14.
CO
15.
34.
67.
140.
NOX
0. 16
0.28
0.36
0. 33
Part.
0.016
0.042
0.094
0.21
RCHO
0.014
0.036
0.092
0.24
SOX
0.010
0.024
0.046
0.093
Fuel usage,
gal/yr
8.5
19.
38.
75.
aOnly the NOX values have been corrected to reflect the new smaller-
bike (under 170cc) cycle*29).
that the 1970 census* '' showed 46.8 million single-unit housing structures
(49.6 million projected to the present), while the population of lawnmowers
(alone) projects to about 45. 6 million at present. This sort of agreement
is quite reasonable and tends to support the overall assumptions.
Based on data from Table A-6 and assuming a growth rate of 6
percent per year for the population of lawn and garden equipment since
1968, the equipment populations basic to this estimation methodology
are presented in Table 10. Usage of lawn and garden equipment undoubtedly
Table 10. ASSUMED POPULATIONS OF LAWN
AND GARDEN EQUIPMENT (10/31/74)
Engine type
4-stroke
2-stroke
Snowthrowers
Other equipment
Total
Typical rated hp
3.5
3.0
3. 5 (approx. )
3. 5 (approx. )
Engines in service
50.9 x 106
3.5 x 106
1.5 x 106
52.9 x 106
54.4 x 106
11
-------
varies with climate, but a well-founded overall estimate of average
usage is 50 hours per year'-^'. A method has been developed to cor-
rect individual county emissions for climate utilizing mean frost-free
.days per year as basis, and it will be discussed in Section IV as the
methodologies are outlined.
Data on emissions from small utility engines are available in
several References^ ' ' ^' ^' ^' representing the results of three
independent studies. The study reported on in References 14 and 16 was
a limited laboratory investigation of 36 engines, with 29 4-stroke engines
and seven 2-stroke engines in the sample. Reference 27 reports on a
study in which eleven machines (eight 4-stroke and three 2-stroke) were
operated through their normal tasks (cutting grass, tilling, etc. ) while
their exhausts were collected via a large bag or a constant-volume sam-
pler. These data may be closer to real-life emissions than any other
information available at this time. The work reported on in References
32 and 33 was an intensive laboratory study of five engines, with one 2-
stroke engine and four 4-strokes in the group investigated. Some degree
of effort was expended by the original researchers or by others on develop-
ment of emission factors and emissions impact using each of the three
studies as basis.
Hourly mass emissions and fuel consumption for the lawn and
garden applications of small utility engines are given in Table 11 as
Table 11. EMISSION FACTORS FOR LAWN AND GARDEN EQUIPMENT
BY TYPE OF ENGINE AND SOURCE OF INFORMATION
Engine type
4-stroke
2-stroke
Data from
reference
16
27
32
16
27
32
Emissions in grams per hour
HC
19.
40.
34.
170.
280.
300.
CO
333.
380.
380.
418.
650.
670.
NOX
5.2
4.0
4.3
1.2
2.0
2. 2
Particulate
0.7a
0.6
io.a
9.4
RCHO
0.7
2.8
SOX
0.5
0.8
Fuel usage,
gal/hr
0.177
0.21
0.400
0.41
Adopted from Reference 32 by author of Reference 27.
estimated according to each of the major studies conducted. Data from
studies described in References 27 and 32 are considered to be most
representative, so a compromise set of emission factors has been drawn
up using these references as basis and is presented as Table 12. These
factors can be used with the methodology as outlined in Section IV to
estimate emissions on a county basis.
12
-------
Table 12. RECOMMENDED EMISSION FACTORS AND FUEL USAGE
FOR LAWN AND GARDEN EQUIPMENT
Units of
factor
g/hr
kg/yra
Engine or
equipment
4-s (all)
2-s snow-
thrower
Other 2-s
4-s snow-
thrower
2-s snow-
thrower
Other 4-s
Other 2-s
Emission factors
. HC
37.
350.
300.
0.19
1.9
1.8
15.
CO
380.
770.
660.
2.0
4.1
19.
33.
NOX
4.2
2.4
2.1
0.02
0.01
0.21
0.01
Part.
0.6
11.
9.4
0.00
0.06
0.03
0.47
RCHO
0.7
3.3
2.8
0.00
0.02
0.04
0.14
SOx
0.5
0.9
0.8
0.00
0.00
0.02
0. 04
Fuel usage
Value
0.20
0.47
0.40
1.1
2.5
10.
20.
Units
gal/hr
gal/hr
gal/hr
gal/hr
gal/hr
gal/hr
gal/hr
aAssuming 40 inches snowfall for snowthrowers and a 213 day season for other
equipment.
E. CONSTRUCTION EQUIPMENT
Although the project considered construction and industrial
equipment as a single category, it now seems more reasonable to con-
sider them separately and thereby reduce the risk of logical errors. In
conjunction with efforts toward developing emission factors, a number
of sources* '» ~ ' provide estimates of construction equipment popu-
lations. In two of these References(27, 38)^ tjie SCOpe was limited to a
single state; so populations estimated therein are not general enough
for present purposes. Another study'0*' made no distinction between
construction and industrial equipment usage, so its population figures
cannot be used here. Two more References'0^' 36) ^o not make use of
explicit equipment populations, but rather a total horsepower-hour figure,
in estimating emissions impact. By elimination, Reference 34 is the only
usable source of population data on construction equipment. Table 13
summarizes these estimates by equipment category, along with data
from several sources on typical machine horsepower and'annual usage.
Since the result required from this section for input to the county
construction equipment methodology is total national construction equip-
I"^A. ^^ ^A\
ment emissions, only three References'0^' o:>' OD' can be used for final
comparison. Emission factors developed in some of the other studies,
however, are useful for indicating the range of estimates available; and
all the factors available are included in Table A-7 of Appendix A. Values
for national construction equipment emissions are given in Table 14, in-
cluding amounts estimated for earthmoving equipment as well as all
13
-------
equipment categories. Agreement between estimates for earthmoving
equipment is reasonably good, although entirely different assumptions
were made for the estimate in Reference 34 as compared to the other
two.
Table 13. ESTIMATES OF CONSTRUCTION MACHINERY
POPULATIONS, USAGE, AND RATED HORSEPOWER
Equipment category
Tracklaying tractors
Tracklaying loaders
Motor graders
Scrapers
Off-highway trucks
Wheel loaders
Wheel tractors
Rollers
Wheel dozers
General purpose
Estimated
(34\
populationv '
197, 000
86,000
95,300
27,000
20,800
134,000
437,000
81,600
2, 700
100,000
Est. usage, hr/yr
Ref.
27a
1350
1700
2000
1000
2400
1400
900
700
1800
600
Ref.
34
1050
1100
830
2000b
2000b
1140
740
740
2000
1000
Ref.
36
1500
2000
1200
2000
2200
2000
Estimated
power, hp
Ref.
27a
140
240
105
475
420
140
82
78
330
115
Ref.
34
120
65
90
475
400
. 130
75
75
300
120
aThese estimates are not considered entirely independent of those in
Reference 34 and are intended for California only.
These estimates are not independent of those in Reference 36.
Table 14. ESTIMATES OF NATIONAL, CONSTRUCTION
EQUIPMENT EMISSIONS AND FUEL CONSUMPTION
Equipment
categories
All const.
Earth-
moving
Fuel
Diesel
Gasol.
Diesel
Diesel
Diesel
Ref.
34
34
35a
36b
34
Emissions in kg/yr x 10"
HC
72.
56.
55.3
39.9
57.4
CO
220.
1100.
164.
202.
160.
NCV
820.
36.
376.
567.
529.
Part.
63.
2.2
- -
18.1
34.8
RCHO
17.
1.
10.
sox
65.
1.6
_ _ _ _
97.0
42.3
Fuel, 106
gal/yr
4615.
602.
3609.°
3874.°
3368.
^Estimate for 1969 made in 1970.
Estimate for 1969 made in 1972.
GAssuming a BSFC of 0. 44 lbm/hp hr = 200 g/hp hr,
fuel density = 0. 86 g/ml.
14
-------
F. INDUSTRIAL EQUIPMENT
This category of engines includes a relatively large number of
small utility engines similar to those used in lawn and garden equipment
and a much smaller number of more durable, more expensive engines
of the heavy-duty type. Treating the heavy-duty class first, it includes
items such as fork lift engines, auxiliary engines used on mobile equip-
ment, engines used in the mineral industries, and pump and generator
engines used by airports and utilities. The major source of information
on this class of equipment is a previous report to the Environmental
/ ^ 21\
Protection Agency* ', in which engine populations and size distributions
were estimated on the basis of engine shipments and their value. These.
estimates are presented in Table 15 along with assumptions on annual
Table 15. ESTIMATES OF HEAVY-DUTY INDUSTRIAL, ENGINE
POPULATION, RATED POWER, AND ANNUAL USAGE*34)
Engine type
Diesel
Gasoline
Typical rated hp
125
55
Annual usage, hr/yr
600
300
Population
417, 000
990,000
engine usage which are about one-half the numbers of hours estimated
earlier for comparably-sized construction equipment.
The light-duty gasoline engines used in industry are assumed to
be the relatively inexpensive air-cooled type. The population of these
engines can be estimated by extending the method used in Reference 34
and by assuming that: (1) useful life of these engines averages 600 hours
and (2) annual usage averages 100 hours. The resulting population esti-
mate for light-duty industrial gasoline engines is 5. 8 million units, and
average rated horsepower is estimated at 3.86^ .
Information on emissions from one or more types of industrial
engines is found in several of the same sources already utilized(27, 33,
34, 37)f Reference 37 is limited in scope to industrial tractors only,
but the specific emissions data are useful for comparison. Reference 27
contains original emissions data only on light duty gasoline engines.
Emissions data from these sources are summarized in Table 16 along
with fuel consumption estimates and a compromise figure is given for
emissions from the light-duty class of industrial engines. The total of
estimated annual emissions can be used with the methodologies developed
in Section IV to estimate county and grid emissions totals.
15
-------
Table 16. EMISSIONS AND FUEL CONSUMPTION
OF INDUSTRIAL ENGINES
Engine type
Heavy-duty
diesel
Heavy-duty
gasoline
Light-duty
gasoline
Ref.
34
37a
34
34
37a
34
27b
33
c
c
Units
g/hp hr
g/hp hr
106 kg/yr
g/hp hr
g/hp hr
10b kg/yr
g/hr
g/hr
g/m
10b kg/yr
Emissions
HC
1. 12
2.7
18. 2
6.68
2.8
86.5
50.0
29.2
32.
19.
CO
3.03
6.5
49.3
199.
163.
1690.
600.
386.
400.
230.
NOX
14.0
8.3
228.
5.16
7.8
43.8
10.0
7.68
7.3
4.2
Part.
1.00
-
16.2
0.327
-
2.8
0.7
0.68
0. 68
0.39
RCHO
0.21
-
3.4
0. 22
-
1.9
_
0.72
0.72
0.42
SOX
0.931
-
15. 1
0. 268
-<
2.3
_
0.60
0.60
0.35
Engine type
Heavy-duty
diesel
Heavy-duty
gasoline
Light-duty
gasoline
Ref.
34
37a
34
34
37a
34
27b
33
c
c
Fuel
Units
g/hp hr
g/hp hr
106 gal/yr
g/hp hr
g/hp hr
106 gal/yr
gal/hr
gal/hr
gal/hr
106 gal/yr
Value
211.
193.
1067.
312.
243.
941.
0.300
0.25
0.23
133.
alndustrial wheel tractors only.
^Category called "home utility" in reference.
cCompromise between estimates given in
References 27 and 33, based on 3.86 average engine hp .
G. FARM EQUIPMENT
The population of farm equipment is quite well defined down to the
county level due to the availability of the Census of Agriculture(39)> This
reference can be considerably out of date, since it is published at five-
year intervals, but the equipment populations change slowly enough so that
16
-------
most of the data remain reasonably accurate. The edition used in pre-
paring this report was for 1969, and a new one (dated 1974) should be out
in 1976. Data given in this reference are much too voluminous to be
included here, but copies of the whole document are available in most
libraries.
Farm equipment usage information is available in the form of
estimates from several sources(33, 34, 37, 38), but accurate survey data
are not available. The estimates are summarized in Table 17, along with
Table 17. SUMMARY OF MOTORIZED FARM
EQUIPMENT ANNUAL USAGE ESTIMATES
Type of equipment
Diesel tractor
Gasoline tractor
Self-propelled combine
Pull combine
Balers
Forage harvesters
Miscellaneous heavy-duty
Miscellaneous light-duty
Ref.
34
37
38b
34
37
38b
34
38b
34
38b
34
38b
34
38b
34
38b
34
Estimated annual
usage, hours
490
432
600
291
282
500
73
100
52
100
24
60
120
100
50
50
50
Typical
power, hp
80. 2a
78. 4a
80.4
40. 9a
39. 7a
50.5
110.
120/105°
25.
120/105°
40.
70/50°
140.
150/110°
30.
60/30°
3.5
Typical
load
factor
0.57
0.43
0.5.7
0.57
0.36
0.57
0.52
0.52
0.52
0.52
0.52
0.52
0.52
0.50
0.52
0.50
0.40
aFlywheel hp
bCalifornia only
cEstimates for dies el/gasoline equipment.
17
-------
values for the typical horsepower and load factor of each category of
equipment. Estimates given in References 34 and 37 for tractors are
in very good agreement, but those in Reference 38 show much higher
usage. It should also be noted that tractor horsepower estimates in
Reference 38 apparently do not include a correction for power train
losses. It is recommended that the values of usage and horsepower
from Reference 34 be used when computing emissions from a given area
due to their consistent availability, but estimates for tractors could be
made using Reference 37 with little change in overall values. Usage of
estimates from Reference 38 should at least be restricted to California,
and the power loss correction noted above should be made for any cal-
culation involving tractors.
In order to be usable in the emissions estimation methodology
without modification, farm equipment emission factors should be ex-
pressed in kg/hr. This step is presented as Table 18 for data from
several references, along with information on fuel consumption. Hourly
emission factors from all the sources are in reasonably good agreement
for diesel tractors, and the disagreements for gasoline tractors stem
primarily from the variation in load factors shown by Table 17 (these
comments also apply to fuel consumption). Since the emission factors
from Reference 38 are derived mainly from those in Reference 34, the
more complete documentation of Reference 34 makes it the logical choice
over Reference 38 for all categories. Making a choice between References
34 and 37 in the gasoline tractor category, however, is a more difficult
problem. For hydrocarbons, the choice would have to be Reference 34
due to the greater accuracy of the analytical method used (FID rather
than NDIR). A compromise between values given in References 34 and
37 for CO, NO , and fuel consumption would probably be most appro-
priate, resulting in factors of 2.86, 0.134, and 5.08 kg/hr, respectively.
18
-------
Table 18. EMISSION FACTORS AND FUEL CONSUMPTION
FOR FARM EQUIPMENT
Equipment
type
Diesel
tractor
Gasoline
tractor
Self-propelled
combine
Pull combine
Baler
Forage
harvester
Miscellaneous
heavy-duty
Miscellaneous
light -duty
Ref.
34
37
38
34
37
38
34
38
34
34
38
34
38
34
38
c
Emission factors, kg/hr
HC
0.078
0.092
0.063
0. 208
0.041b
0. 197
0.300
0.259
0.116
0.183
0.179
0.122
0.171
0.082
.0.079
0.029
CO
0. 154
0.221
0.139
3.34
2.38
4.11
6.37
6. 50
2.83
4.53
5,33
0..297
2.25
1.73
1.70
0.363
NOX
0.429
0. 282
0.426
0. 155
0. 113
0. 190
0.408
0.417
0.068
0.109
0. 148
0.657
0.612
0.112
0. 175
0.007
Part.
0.059
0.050
0.008
0.010
0.054
0.055
0.005
0.008
0.012
0.110
0.098
0.015
0.025
0.001
RCHO
0.016
0.007
0.015
0.003
0.005
0.022
0.004
0.001
S0x
0.040
0.040
0.006
0.007
0.034
0.033
0.004
0. 006
0. 007
0.067
0.060
0.009
0.015
0.001
Fueia,
kg/hr
9.06
8.06
8.23
6.30
3.86
6.94
15.3
12.6
4.25
6.80
6.34
15.2
14.3
4.17
4.71
0.58
aWhere necessary, densities assumed were 0. 731 g/ml for gasoline and
6. 851 g/ml for diesel fuel.
bBased on NDIR data.
°Based on factors for light-duty industrial engines, Table 16.
19
-------
III. SOURCES OF DATA ON COUNTIES
Amid all the information available in the literature on subjects
which bear on this study, very little is available for the county level.
The major sources of county data utilized are given in Table 19, along
Table 19. MAJOR SOURCES OF DATA ON COUNTIES
Source
Type of data contributed
County and City Data Book
1969 Census of Agriculture' '
Area Measurement Reports* '
New York State Statistical
Yearbook - 1973*7)
Statistical Abstract of Ohio -
1969(8)
South Carolina Statistical
Abstract - 1973(9)
Wisconsin Statistical Abstract -
Third Edition - June 1974(1°)
Secretary of State, State of
Illinois*42**
Missouri Department of
Revenue '
Demographic, business, agricultural
Agricultural equipment populations
(1969)
Land and water areas (I960)
Outboard and snowmobile registration
data
Outboard registration data
Outboard registration data
Outboard registration data
Motorcycle registration data
Motorcycle and outboard registration
data
aTwo examples of sources for motorcycle registration data - the other
states were not contacted.
21
-------
with descriptions of the types of data obtained. In some cases, of
course, the data contributed by a given source to this project cons-
titute only a small portion of the data available from that source; and
there may be a great many more sources (e.g., state motor vehicle
departments) which provide equally useful data.
A number of other sources contain a lesser amount of data for
counties, and these sources are listed (along with those given in Table
19) in Appendix B. Another source of county data useful to this effort
has been county maps of the type prepared and distributed by state high-
way departments. These maps normally include not only roads but also
bodies of water, boundaries and populations of incorporated places, rural
dwellings, and many other features. The maps used during this project
were on a scale of one-half inch equals one mile (1:126, 720), but larger-
scale maps are usually available and should be used for any serious ef-
.fort at making county and grid emissions estimates.
22
-------
IV. METHODOLOGY FOR COUNTY EMISSIONS ESTIMATES
Availability of data and the applicability of specific items and
techniques varies among the emissions sources being considered here,
so each source will be considered in a separate subsection. Before ar-
riving at the methodologies presented, experimentation was conducted
with a number of alternatives for some of the source categories. Docu-
mentation of this research is provided in Appendix C, although the
methods presented in the text are recommended as preferable overall.
A. OUTBOARD MOTORS
As a consequence of the'Federal Safety Act of 1971, most states
are currently registering all power boats operating within their borders;
but a few states still exempt very small craft (common exemption limit
is under 10 horsepower). Statistics for 1974 (available in 1975) should
have registrations for all power boats, since the exemptions are no
longer approved by the Coast Guard after calendar year 1973. A number
of states tabulate boat registrations by county as well as total for the
state, but county tabulations are not required for Coast Guard approval.
For the states in which boat registrations are available by county,
the county boat population will be assumed to equal registrations plus
any applicable correction for boats not registered. It would be worth
expending considerable effort to find boat registrations by county or to
extract such values from available data, because registrations by county
are not easily projected from other generally-available county data.
Data from four states (New YorlJ7), Ohio^8), South Carolina^9),
and Wisconsin' ') confirm that boat registrations correlate strongly
with population on a county basis. Simple regression analysis shows
correlation coefficients r^ from 0. 70 to 0. 99 for individual states,and
around 0. 75 for the four states taken together. New York data' '' also
show that, except for inland counties having no surface water usable
for boating ("dry" counties), boats used correlate strongly with boats
registered (r^ over 0. 9). To be recorded by the Bureau of the Census'4*',
ponds must be at least 40 acres (0.16 km^) in area and streams must be
23
-------
at least 1/8 mile (0. 20 km) wide. The best estimate of boats used in a
county, therefore, is to apportion total state boat registrations (corrected
for boats not registered, if any) by population. Adjustments for individual
inland counties can be made if no inland water usable for boating exists
by simply neglecting outboard emissions. Care should be exercised,
however, to make certain that a given county really has no water usable
for boating; because many reservoirs have been constructed since these
area measurements were made (I960 or earlier). A correction for dis-
proportionately low registrations and usage in densely populated counties
can also be made according to the empirical relationship (based on four-
state dataC7'10)
percent of state boat t9tal used in county =
31.6 (population density, inhabitants/mi )~ ' x
(percent of state population in county)
for counties having population densities over 1000 inhabitants/mi .
The general equation to be used for outboard emissions is
county emissions (kg/yr) = (boats used in county) x
(emission factor, kg/unit year).
The emission factor is a function of the mixture of boats in the boat
population (sizes and types) as well as annual operating time. Although
it would be desirable to use a specific mixture of boat sizes and types
for each county, such data are not available; so it will be necessary to
assume a "typical" mixture' ' in order to proceed with calculations.
Annual boat usage has been estimated to average 75 hours nation-
wide' ', but usage undoubtedly varies with climatic conditions. For the
purposes of this methodology, annual usage will be estimated by the
equation
annual boat usage (hr) = 10C2;
where C? = number of months during which "monthly normal" temperature
exceeds:
45°F for counties in the north region (43°N latitude
and northward)
48° F for counties in the central region (37°N latitude
to 43°N latitude)
55°F for counties in the south region (south of 37°N
latitude).
24
-------
The temperatures were computed by assuming that the annual
period of usage averages six months in the north region, seven months
in the central region, and eight months in the south region. "Monthly
normal" temperatures are averages of daytime highs and lows averaged
over each month of the year for a long period of time (typically 30 years
or more). Such data are usually compiled for all weather stations, and
data for the nearest weather station can be used. The expression for
the yearly average emission factor thus becomes
emissions in kg/unit yr = 0.01 G£ (emissions in g/unit hr).
It is understood that the emissions under consideration are air pollutants
only, so the factors should not include pollutants expected to remain in
the water phase.
B. SNOWMOBILES
Snowmobiles are used mainly in the north central and northeast
states, and good state registration data are available'^ '. Registrations
by county, however, were found only for New York' ' '; and they correlate
very well with snowmobiles used in each county (r^ over 0.99). In order
to predict usage of snowmobiles by county where county data are not
available, urban and non-urban counties should be separated. For non-
urban counties in New York (population density under 1000 inhabitants
per square mile), multiple regression analysis yielded the following
relationship (r^ = 0.66):
percent of state snowmobiles used in county = -2.345 +
1.560 (percent of state population in county) +
0.0325 (percent snowfall at state geographic center).
Other variables with which experimentation was conducted, such as num-
ber of developed trails and number of large farms, exhibited very weak
correlation with snowmobile usage. The percentages resulting from the
equation above can be used with state snowmobile registrations to com-
pute the number of snowmobiles operating in each county.
For urban areas where population density is 1000 inhabitants per
square mile or more, snowmobile usage decreases as a function of popu-
lation density. It appears that usage drops to zero when population den-
sity reaches about 3000 per square mile, so it will be assumed that usage
in urban areas follows the relationship
percent of state snowmobiles used in county =
C^ j 1 . 5 - 0. 0005 (county population density, inhabitants / mi )| x
(percent of state population in county),
25
-------
where C^ = 1 for densities from 1000 to 3000 per square mile and C^ =
0 for densities above 3000 per square mile.
The general equation used to estimate snowmobile emissions on
a county basis is
county emissions (kg/yr) =
(snowmobiles operating in county)
(total national emissions, kg/yr) (national snowmobile registrations).
C. MOTORCYCLES
The methodology for motorcycles is one of the least complicated
of those under consideration, because registration data for motorcycles
are available by county. Registrations only tell part of the story, how-
ever, since some motorcycles are always unregistered in each part of
the country. The general relationship to be used for motorcycle emis-
sions is
county emissions (kg/yr) = (county registrations) /
(1 - fraction units unregistered)I x (emission factor, kg/unit yr).
The county registration data are available from individual state motor
vehicle departments, and the fraction of units unregistered is available
on both national and regional bases from a recent statistical survey' ''.
The emission factor for the population of motorcycles under consideration
is computed for each pollutant by
8 f/ mi \
factor, kg/unit yr = (0.001) V F^ [(emissions, g/mi x yr ) +
1=1
l>
C. (riding season, days) (tank volume, JL } (1 - '- - ~ - -. )
1 \ / \ / \/ tank volume day/ 1 .,
where i = individual motorcycle type/ size (e.g., 2- stroke, 91-190
displacement)
F^ = fraction of motorcycle population under consideration which
is classified in category i
CJL =1.0 for hydrocarbons, 0.0 for other pollutants.
The factors F^ and distances travelled annually are available from the
same statistical survey mentioned above' "' on both national and re-
gional bases. Length of the riding season in days is available as a na-
tional average from another survey'^ ', and a method has been devised
to correct the riding season for specific locations by making use of
monthly normal temperatures for U.S. Cities' '. This correction
26
-------
simply involves counting the number of moriths during which monthly
normal temperature was 38°F or higher for the location of interest
and converting those months to days. Fuel tank volumes can be esti-
mated at 2.0 gallons (7.6je) for bikes of 90 cc or less, 2.5 gallons (9.5^)
for those in the 91-190 cc range, 3. 0 gallons (11. 4 t) for those in the
191-290 cc range, and 3.5 gallons (15. 2 j) for those over 290 cc.
D. LAWN AND GARDEN EQUIPMENT
Emissions from individual small utility engines of the types
used in lawn and garden equipment have been studied thoroughly' ',
and estimates of national emissions have been made using these emis-
sions results as basis' ' '. To allocate emissions from lawn and
garden equipment by county, however, consideration will be given to
areas where these machines are used and to seasonal factors. Lawn
and garden equipment is used predominantly around homes, so it seems
reasonable that equipment population should correlate well with number
of one-unit housing structures(39). This data item will be the basic cri-
terion by which emissions from lawn and garden engines are allocated to
counties.
An additional factor for lawn and garden equipment is the highly
seasonal nature of its use. Data on occurrence of freezing conditions' '
can be used to predict the length of season for use of mowers, edgers,
and tillers. Usage of snowthrowers is predominantly limited to relatively
few states, where snowfalls of one inch or more are recorded 10 or 15
times per year (or more frequently). This usage can be evaluated by
assuming that no snowthrowers are in service where annual snowfall is
under 30 inches and that each snowthrower operates eight minutes for
each inch of snowfall. These assumptions are based on a "typical" snow-
fall of 2. 5 inches and a typical usage time of 20 minutes per snowfall.
The criterion of 30 inches annual snowfall leads to an (approximate) di-
viding line of 40°N latitude separating the region of snowthrower operation
from generally warmer climates. The distribution of snowthrowers will
be assumed to follow the distribution of population in those areas where
they are likely to be in service.
The general relationship for lawn and garden equipment emissions is
county emissions (kg/yr) = (nat'l emissions except snowthrowers, kg/yr) x
/average operating yearN /county mean freeze-free days\
V 213 days A year )x
/county one-unit housing structures \ , / county population \
\national one-unit housing structures^ ^\hea.vy snow zone population/
/one hour operation \
(national snowthrower population) ^ g incheg snowfally)
(county snowfall, in/yr) (emission factor, kg/hr);
27
x
-------
where Cj = 0 for counties having less than 30 inches annual snowfall,and
Co = 1 for counties having 30 inches annual snowfall or more.
The "heavy snow zone population" is the sum of populations of 19 states
plus half the populations of three additional states, totalling 83.98 mil-
lion (1970 census). County snowfall can be assumed equal to that re-
corded at the nearest reporting station, either inside or outside the
county.
E. CONSTRUCTION EQUIPMENT
Nationwide emissions from construction equipment have been
estimated by several individual efforts' ' ' ', and there is reason-
ably good agreement on the totals. Allocation of these emissions will
be made first to the states, based on construction dollar volume* '.
Allocation to counties will then be made by population.
The construction volume data to be used are available at intervals
of six months in the open literature and probably at smaller intervals by
consulting directly with the source. The data are broken into three
major categories: heavy construction, highways and bridges, and building
construction (not including home building). The first two categories
make use of more engine-powered equipment per dollar of construction
performed than building construction does, so construction dollars in
the first two categories will be weighted by a factor of 3 as compared to
those spent in building construction. The relationship used to calculate
county emissions from construction equipment (based on the above con-
siderations) is
county emissions (kg/yr) =
, , . . . (state const, volume) (county population)
(national emissions, kg/yr) -. r\ : 1 . 1>'7 :.
& ' (nat'l const, volume) (state population)
Emissions due to homebuilding and other light construction are consi-
dered negligible compared to emissions from larger (contracted) cons-
truction jobs.
F. INDUSTRIAL, EQUIPMENT
Based on rather minimal information, emissions from industrial
engines have been estimated on a national basis' ^'. This category in-
cludes engine applications such as: fork-lifts, generators, pumps, and
other machinery used by manufacturing concerns; refrigeration units,
auxiliary engines, and material-handling machinery used in wholesale
trade; and machinery used in mining and quarrying. The method used
in this case will be to apportion emissions to counties directly from
national estimates by the relationship
28
-------
county emissions (kg/yr) =
i ... -. i / \ county (A + B + C)
(national emissions kg/yr) f \ ' ;
national (A + B + C)
where A = value added by manufacturing establishments,
B = sales of wholesale trade establishments, and
C = value of shipments and receipts of mineral industries.
In some cases it will be necessary to estimate these quantities by ap-
portioning state data according to number of establishments of each type
in the county. Such estimation will be necessary to a greater extent for
item "C" than for the others, but it is (for most counties) a relatively
small contributor to the sum of A, B, and C.
G. FARM EQUIPMENT
Emissions from motorized farm equipment can be estimated quite
accurately due to availability of good data on machine populations' ' and
well-documented estimates of annual machine usage'-^, 37, 38, 32)^ Popu-
lation data are available for farm tractors, garden tractors used on
farms, combines, motorized balers, and motorized forage harvesters.
Population estimates for large miscellaneous engines (mostly used in
irrigation), small utility engines, and small utility engines used speci-
fically on lawn and garden equipment can also be made.
The large (mostly water-cooled) general purpose engines in ser-
vice nationwide number about 27 percent of the tractor population* '.
It will be assumed that these engines number 5 percent of the tractor
population in non-irrigated areas and 30 percent in irrigated areas.
General-purpose small utility engines (used on augers, sprayers, etc.)
will be assumed to number 1.5 per farm (class 1-5 farms only). These
assumptions are based on the ratio of engines in service in agriculture
nationwide to total number of farms and consideration of typical farm
requirements. Annual usage of all these items of equipment and appli-
cable emission factors have been developed sufficiently for use in this
methodology.
The basic relationship for calculating emissions from farm
equipment is .
county emissions (kg/yr) = Y] (equipment population) x
(annual usage) (emission factor, kg/hr),
where the summation is taken over the types of equipment used.
The number of class 1-5 farms in each county is also available from the
Census of Agriculture' ', along with specific data on machinery populations,
29
-------
V. DEMONSTRATION OF COUNTY EMISSIONS
ESTIMATION METHODOLOGIES
The methodologies presented in Section IV are demonstrated
in this section for the 12 counties in the St. Louis Metropolitan Air
Quality Control Region (AQCR 070). While most aspects of the method-
ologies can be demonstrated well for these particular counties, special
situations restricting applicability or accuracy will be identified. It
should be noted, however, that unforeseen circumstances are very
likely to occur if the methodologies are applied widely in the field,
creating a need for sound judgment and good knowledge of the area
under study.
A. OUTBOARD MOTORS
Referring back to Section IV. A. , certain data are needed for
each county to compute the emissions impact of outboard motors. These
data are summarized in Table 20 for the counties in AQCR 070. Before
the Missouri outboard registration data can be used, they must be mul-
tiplied by the appropriate correction factor from Table 1 (1.48) to ac-
.count for unregistered small craft. To convey an idea of the accuracy
of the registration data under discussion, the Missouri total (corrected
for unregistered units) is 227, 450 motors as determined by the Depart-
ment of Revenue, while other sources show 196,000 motors^ '
and 105, 013 outboard boats^3'. It is likely that the last figure is low
due to non-inclusion of small boats (it would correct to 155, 419), but
there is still a considerable amount of disagreement. The figure con-
sidered most reasonable for outboard boats in Illinois is listed in Table
A-l of Appendix A, and for the end of 1973 this figure is 182, 120.
The value of C2 listed in Table 20 is seven (months), so annual
boat usage for AQCR 070 is estimated at 10C2 = 70 hours. The formula
for annual emissions becomes
emissions in kg/unit yr = 0.07 (emissions in g/unit hr).
31
-------
Table 20. COUNTY DATA TO BE USED IN DETERMINING OUTBOARD
MOTOR EMISSIONS IMPACT
State
Illinois
Missouri
County
Bond
(""1 ' tn
Madison
Monroe
Randolph
Qi. y-«1
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Outboard
regist.
a
2, 536b
5,108b
4, 46 8b
23, 488b
12, 121b
% state
popul.
0.126
0-?cc
. t*~j -j
2.26
0.170
0.282
o c 7
0.124
1.18
2.25
1.99
20.3
13.3
Population
density,
inhab. /mi
37
Ac
D .J
342
49
53
474
rx£ri
24
59
158
169
1,907
10, 201
Inland
water,
mi ^
3.0C
oo ^d.
12.5
9.1
12.2
7 o
o!&
8.3
3.4
35.0
17.6
3.8
C2
7
7
(
7
7
7
7
i
7
7
7
7
7
7
aNot available for Illinois.
Includes only boats with motors of 7. 5 hp or more.
°Shown on county road map -0.0 mi^ in I960 tabulation' '.
^Shown on county road map - only 0. 1 mi^ in I960 tabulation' '.
Based on 1973 Illinois total boat registrations apportioned according to
population and corrected Missouri registrations by county, Table 21
shows county emissions and fuel consumption of outboard motors using
factors from Table 2. These values will be combined later with emis-
sions from the other categories of interest to determine totals for counties
and AQCR 070. In terms of season, the outboard emissions are expected
to occur during the months of April through October, inclusive.
B. SNOWMOBILES
As shown by data in Table A-3 of Appendix A, no snowmobiles
are registered in Missouri; and 34, 500 are registered in Illinois. Using
the method developed in Section IV. B. for apportioning the snowmobile
population to counties, the equation for Illinois counties becomes
snowmobiles in county = -809. + 538. (% of state popul. in county)
+ 11.1 (77.7)
= 53 +538 (% of state popul. in county)
assuming that average snowfall at the state geographic center is 22.0
inches per year. All emissions from snowmobiles, of course, would
32
-------
Table 21. EMISSIONS AND FUEL CONSUMPTION OF OUTBOARD MOTORS
FOR COUNTIES IN AQCR 070
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Outboard
boats used
229
464
4,116
310
514
4,680
226
3,753
7, 560
6,613
34,762
17,939
81,166
Emissions, 10^ kg/yr
HC
12.3
25.0
222.
16.7
27.7
252.
12.2
12.2
407.
356.
1,870.
966.
4,370.
CO
36.5
74.1
657.
49.5
82.0
747.
36.1
36.1
1,210.
1,060.
5,550.
2,860.
13, 000.
C02
56.3
114.
1,010.
76.2
126.
1,150.
'55.5
55.5
1,860.
1,620.
8,540.
4,410.
19,900.
NOX
0.072
0.146
1.30
0.098
0.162
1.47
0.071
0.071
2.38
2.08
11.0
5.65
25.6
sox
0.071
0.143
1.27
0.096
0.158
1.44
0.070
0.070
2.33
2.04
10.7
5.53
25.0
Fuel used,
103 gal/yr
24.5
49.7
441.
33.2
55.0
501.
24.2
402.
810.
708.
3,720.
1,920.
8,690..
eo
CO
-------
occur in the winter months (December through March, in this case).
Snowmobile populations, emissions, and fuel consumption are sum-
marized in Table 22 for the counties of AQCR 070 where they are
computed to occur. It is probable that the error of estimate in this
case makes these values higher than actual, due to the rather minimal
snowfall in the area for lengthy snowmobile operation.
C. MOTORCYCLES
Emissions from motorcycles are estimated using the method
in Section IV. C. and data from Section II. C. The breakdown according
to engine size and type is taken from Table 6, and the riding season is
computed to be nine months (March through November) or 275 days.
The computation of emission factors (in kg/unit year) and fuel consump-
tion is outlined in Table 23, with the results for AQCR 070 appearing
as "weighted composites" at the bottom of the table.
Table 23. COMPUTATION OF EMISSION FACTORS AND FUEL,
CONSUMPTION FOR MOTORCYCLES IN AQCR 070
Motorcycle
size
4-s/0-90cc
4-s/91-190cc
4-s/191-290cc
4-s/over 290cc
2-s/0-90cc
2-s/91-190cc
2-s/191-290cc
2-s/over 290cc
i
1
2
3
4
5
6
7
8
Fi
0.109
0.127
0.058
0.231
0.098
0.115
0.053
0.208
Weighted composite
Emissions in kg /unit year
HC
2.7
5.0
8.8
16.
5.6
15.
40.
77.
26.
CO
15.
34.
67.
140.
4.5
17.
63.
150.
79.
NOX
0.16
0.28
0.36
0.33
0.082
0.14
0.08
0.12
0.20
Part.
0.016
0.042
0.094
0.21
0.10
0.27
0.74
1.65
0.50
RCHO
0.014
0.036
0.092
0.24
0.075
0.15
0.27
0.42
0.19
S°x
0.010
0.024
0.046
0.093
0.016
0.035
0. 090
0.17
0.074
Fuel
usage,
gal/yr
8.5
19.
38.
75.
9.4
20.
52.
100.
50.
Calculation of emissions by county requires registration data,
which are available^ ' ',and an assumption of the percentage of un-
registered motorcycles in the total population (15 percent for AQCR
070)' '. Emissions and fuel consumption for counties are shown in
Table 24, as well as a total for AQCR 070. As already mentioned, these
emissions would occur during the March through November period; and
they appear to be concentrated in the more urban counties.
D. LAWN AND GARDEN EQUIPMENT
This category is divided into two classes, namely snowthrowers
and other equipment. According to criteria suggested in Section IV. D.,
34
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Table 22. SNOWMOBILE EMISSIONS AND FUEL CONSUMPTION,
COUNTIES IN AQCR 070
State
Illinois
County
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Total AQCR 070
Snowmobiles
in use
121
190
1269
144
205
1436
120
3485
Emissions, 10 kg/yr
HC
4.57
7.18
48.0
5.44
7.75
54.3
4.54
132.
CO
7.10
11.2
74.5
8.45
12.0
84.3
7.04
205.
NOX
0.073
0.114
0.761
0.086
0.123
0.862
0.072
2.09
Part.
0.202
0.317
2.12
0.240
0.342
2.40
0.200
1.9
RCHO
0.067
0.10
0. 70
0.079
0.11
0.79
. 0.066
5.82
S0x
0.006
0.010
0.065
0.007
0.010
0.073
0.006
0.178
Fuel used,
103 gal/yr
6.78
10.6
71.1
8.06
11.5
80.4
6.72
195.
(Jl
-------
Table 24. EMISSIONS AND FUEL CONSUMPTION OF MOTORCYCLES,
COUNTIES IN AQCR 070
State
Illinois
Missouri.
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Motorcyclesa
in use
555
713
6,080
500
1,012
4,969
295
1,608
3,320
3,492
14,392
10,084
47,020
Emissions, 10 kg/yr
HC
14-
19
160
13
26
130
8
42
86
91
370
260
1,200
CO
44
56
480
40
80
390
23
130
260
280
1, 100
800
3,700
NOX
0. 11
0. 14
1.2
0. 10
0.20
0.99
0.06
0.32
0.66
0.70
2.9
2.0
9.4
Part.
0.28
0.36
3.0
0. 25
0.51
2.5
0. 15
0.80
1.7
1.7
7.2
5.0
24.
RCHO
0. 11
0. 14
1.2
0. 10
0. 19
0.94
0.06
0.31
0.63
0.66
2.7
1.9
8.9
sox
0.04
0.05
0.45
0.04
0.07
0.37
0.02
0. 12
0.25
0.26
1. 1
0.75
3.5
Fuel used,
103 gal/yr
28..
36.
300.
25.
51.
250.
15.
80.
170.
170.
720.
500.
2,400.
u>
Assuming 15 percent of motorcycles unregistered' ''
-------
only an insignificant number of snowthrowers should be operating in the
St. Louis area; so they will be omitted from this analysis. National
.emissions and fuel consumption of equipment other than snowthrowers
are computed using data from Tables 10 and 12, assuming 2. 7 million
2-stroke engines and 50. 2 million 4-stroke engines. The other data
required for this computation are the mean freeze-free days per year
(205)' ' and the numbers of one-unit housing structures in the indivi-
dual counties and the nation* '. The number of one-unit housing struc-
tures in the nation is approximately 46. 8 million, and emissions appor-
tioned to counties using this variable are shown in Table 25 along with
fuel consumption and calculated county engine populations. Emissions
from lawn and garden equipment occurring in AQCR 070 are about 1
percent of the national total emissions from this engine category.
E. CONSTRUCTION EQUIPMENT
Emissions from construction equipment are given in Table 14
(along with fuel consumption) as national totals, and the construction
dollar volume data required for apportioning the national totals to states
can be obtained from Reference 29 (August 1974 issue in this case). Com-
putation of the percentage of national construction equipment emissions
allocated to the two states within which AQCR 070 falls (Illinois and Mis-
souri) is presented in Table 26. These percentages are equal to the
weighted averages of construction dollar percentages given in the last
column of Table 26.
Table 26. COMPUTATION OF ILLINOIS AND MISSOURI
CONSTRUCTION EQUIPMENT EMISSIONS AS PERCENTAGES
OF NATIONAL TOTALS
Area or
state
U.S.C
Illinois
Missouri
Heavy const.
106 dol.
11, 140
395
214
%
100
3.55
1.92
High-way const.
10° dol.
4,385
297
148
%
100
6.77
3.38
Building const. a
106 dol.
13,097
834
348 '
%
100
6.37
2.66
Avg.b
%
100
5.33
2.65
aExcluding ho me building.
Weighted using method in Section IV. E.
°Excluding Alaska and Hawaii.
The state percentages can be divided further to make county esti-
mates by apportioning according to population. Percentages-/, state
populations for each county in AQCR 070 were given in Table 20, and
they are used with percentages from Ta.ble 26 and national totals from
37
-------
Table 25. LAWN AND GARDEN ENGINE EMISSIONS AND
FUEL CONSUMPTION FOR COUNTIES IN AQCR 070
State
Illinois
Missouri
C ounty
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
One-unit
housing
structures
4,490
7,788
65,533
5,383
8,624
68,769
4,848
15,882
27,593
21,631
235,303
81,784
547,628
Computed engine
populations
4- stroke
4, 820
8,350
70,300
5,770
9,250
73, 800
5,200
17,000
29, 600
23, 200
252, 000
87,700
587,000
2-stroke
259
449
3, 780
311
498
3,970
280
916
1,590
1, 250
13, 600
4, 720
31, 600
Fuel used
103 gal/yr
53.4
92.5
779.
63.9
102.
817.
57. 6
188.
328.
257.
2790.
971.
11,750.
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Charles
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Emissions, 103 kg/yr
HC
12.6
21.8
183.
15. 1
24.1
192.
13.6
44.4
77. 1
60.5
658.
229.
1,530.
CO
100.
174.
1,460.
120.
192.
1,530.
108.
354.
615.
482.
5,240.
1,820.
12,200.
NOX
1.01
1.76
14. 8
1. 22
1.95
15.5
1.09
3.59
6. 23
4.88
53. 1
18. 5
124.
Part.
0.27
0.46
3.89
0.32
0. 51
4. 08
0.29
0.94
1. 64
1. 28
14. 0
4.85
32.5
RCHO
0.23
0.40
3.3
0.27
0.44
3.5
0.25
0.81
1.4
1. 1
12.
4.2
28.
SOX
0. 11
0. 19
1.56
0. 13
0.20
1. 63
0. 12
0.38
0. 66
0.51
5.59
1.94
13. 0
38
-------
Table 14 to compute county emissions as shown in Table 27. As ex-
pected, this category has a much larger fuel consumption and loading
of NO emissions than any of the others examined thus far.
F. INDUSTRIAL EQUIPMENT
Fuel consumption and emissions of industrial engines are given
in Table 16 as national totals, and information required to apportion
emissions according to the method outlined in Section IV. F. is presented
in Reference 39. These latter data are summarized in Table 28 for the
counties in AQCR 070, indicating that a range from about 0.002 percent
to 0. 8 percent of national industrial engine emissions occur within indi-
vidual counties. Using national emissions and fuel consumption data
from Table 16 in conjunction with percentage distributions from Table
28, industrial engine emissions by county for AQCR 070 have been
Table 28. COMPUTATION OF INDUSTRIAL EQUIPMENT
POPULATION PERCENTAGES FOR COUNTIES IN AQCR 070
Area or
state
U.S.
Illinois
Missouri
County
All
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Millions of dollars
A = value
added
261,983.8
13.2
17.1
645.2
0.9
30.3
267.3
2.3
56.0
66.4
44.8
1, 285.8
1,793.5
4, 222.8
B = whole-
sale sales
459,475.967
14.583
17.391
229.629
12.829
14.394
519.297
15.643
25.699
17.333
33.644
3,065.356
4,518.156
8, 483.954
C = min-
erals
25,848. 7
a
a
2.8
a
18.4
a
2.8
a
3.0
a
9.8
0..7
37.5
/County A +B+CN
\ U.S. A+B+C /
1.0
3.72x10-5
4. 62xlO'5
1.17xlO"3
1.84X10'5
8.44xlO'5
l.OSxlO'3
2.78xlO"5
3. 44x1 O'5
1.12xlO'4
l.OSxlO'4
5.84xlO'3
8.45xlO"3
1.71X10'2
Negligible
calculated and appear as Table 29. The population of industrial engines
is more heavily weighted toward gasoline-fueled units than the population
of construction equipment, so it produces more HC and CO and less NOX
than does construction equipment on a specific basis.
39
-------
Table 27. CONSTRUCTION EQUIPMENT EMISSIONS AND FUEL CONSUMPTION
FOR COUNTIES IN AQCR 070
State County
Illinois
Missouri
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
% national
emissions
0.00672
0.0136
0.120
0.00906
0.0150
0.137
0.00661
0.0313
0.0596
0.0527
0.538
0.352
1.342
Emissions, 10 kg/yr
HC
8.60
17.4
154.
11.6
19.2
175.
8.46
40.1
76.3
67.5
689.
451.
1,720.
CO
88.7
180.
1,580.
120.
198.
1,810.
87.3
413.
787.
696.
7,100.
4,650.
17, 700.
NOX
57.5
116.
1,030.
77.6
128.
1,170.
56.6
268.
510.
451.
4,610.
3,010.
11,500.
Part.
4.38
8.87
78.2
5.91
9.78
89.3
4.31
20.4
38.9
34.4
351.
230.
875.
RCHO
1.2
2.4
22.
1.6
2.7
25.
1.2
5.6
11.
9.5
97.
63.
240.
sox
4.48
9.06
79.9
6.03
9.99
91.2
4.40
20.8
39.7
35.1
358.
234.
894.
Fuel used,
103gal/yra
351.
710.
6, 260.
473.
783.
7, 150.
345.
1,630.
3,110.
2,750.
28,100.
18,400.
70,000.
188. 5 percent of gallons are diesel fuel, 11.5 percent gasoline.
-------
Table 29. INDUSTRIAL ENGINE EMISSIONS AND FUEL CONSUMPTION
FOR COUNTIES IN AQCR 070
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Emissions, 10 kg/yr
HC
4.60
5.71
145.
2. 28
10.4
130.
3.44
4.26
13.9
13.0
722.
1,050.
2, 120.
CO
73.3
. 91.0
2,300.
36.2
166.
2,070.
54.7
67.7
221.
207.
11,500.
16, 600.
33, 700.
NOX
10.3
12.8
323.
5. 1
23. 3
290.
7.7
9.5
30.9
30.0
1,610.
2,330.
4,720.
Part.
0.72
0.90
22.7
0.36
1.64
20.4
0.54
0.67
2. 17
2.04
113.
164.
332.
RCHO
0.2
0.3
6.7
0.1
0.5
6..0
0.2
0.2
0.6
0.6
33.
48.
96.
sox
0. 66
0.82
20.8
0.33
1.50
18.6
0.49
0.61
1.99
1.86
104.
150.
304.
Fuel used,
103 gal/yra
79.6
98.9
2, 504.
39.4
181.
2,250.
59.5
73.7
240.
225.
12, 500.
18, 100.
36, 600.
149. 8 percent of gallons are diesel fuel, 50. 2 percent gasoline.
-------
G. FARM EQUIPMENT
To compute emissions and fuel consumption of farm equipment
by county, data from Reference 40 on equipment populations are used
with information from Tables 17 and 18 on annual equipment usage and
emission rates. To simplify the calculations, composite emission and
fuel consumption factors can be used for all tractors (both gasoline and
diesel). These factors (in kg/hr) are 0.153 HC, 1.71 CO, 0.259 NOX,
0.030 Particulate, 0.011 RCHO, 0.020 SOX, and 6.77 fuel. In addition,
the composite annual tractor usage is 352 hours and fuel used in tractors
is 53.1 percent diesel fuel by volume. A similar analysis for combines
yields factors (in kg/hr) of 0.281 HC, 6.00 CO, 0.372 NOX, 0.049 Parti-
culate, 0.014 RCHO, 0.031SOX, and 14.1 fuel. Composite annual
combine usage is 70 hours, and fuel used in combines is 34. 2 percent
diesel fuel by volume. Looking at the other farm application made up
of both diesel and gasoline engines, fuel used in miscellaneous heavy-
duty engines is 35.4 percent diesel fuel by volume.
Equipment populations for the counties in AQCR 070 (1969) are
listed in Table 30 along with totals for the region. These data can be
Table 30. FARM EQUIPMENT POPULATIONS FOR COUNTIES
IN AQCR 070<4°)
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Population by type of equipment (1969)
Trac-
tors
1,967
2,946
4,513
2, 244
2,948
3,746
2,980
3, 128
1,312
2,700
1,179
29,663
Com-
bines
379
527
782
454
484
785
674
190
91
472
154
4,992
Bal-
ers
291
586.
751
256
537
478
509
726
382
358
125
4,999
For-
age
harv.
104
303
157
72
151
141
185
149
49
93
16
1, 420
Misc.
heavy
duty
98
147
226
112
147
187
149
156
66
135
59
1,482
Misc.
light
duty
1,140
1,602
2,234
1,068
1,480
1,878
1,580
1,400
514
1, 242
554
12,692
used with emission and fuel consumption factors given above in the text
and in Table 18 to calculate total emissions by county. To avoid
42
-------
Table 31. FARM EQUIPMENT EMISSIONS AND FUEL CONSUMPTION
FOR COUNTIES IN AQCR 070
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Total AQCR 070
Emissions, 103 kg/yr
HC
118.
179.
268.
134.
175.
225.
181.
180.
75.8
160.
68.3
1,760.
CO
1,410.
2, 110.
3, 190.
1, 600.
2,080.
2,690.
2, 180.
2,080.
885.
1,900.
803.
20,900.
NOX
199.
309.
448.
224.
296.
376.
.306.
305.
127.
268.
114.
2,970.
Part.
23.7
37.3
53.0
26.5
35.2
44. 6
36.6
36. 1
15.0
31.7
13.3
353.
RCHO
8.4
13.
19.
9.5
12.
16. .
13.
13.
5.4
11.
4.8
120.
S°x
15. 6
24.5
35.0
17.5
23. 2
29.4
24. 1
23.8
9.9
20.9
8.8
233.
Fuel used3
103 gal/yr
1,770.
2,730.
3,980.
1,990.
2, 620.
3,350.
2,730.
2,690.
1, 130.
2,380.
1,010.
26,400.
L52. 5 percent of gallons are diesel fuel, 47.5 percent gasoline.
-------
unnecessary complication, the results of this calculation are given in
Table 31 as totals for all the farm equipment in each county rather
than as totals for each type of equipment. In the computations for farm
equipment emissions and fuel consumption, it is necessary to assume
that the diesel and gasoline population fractions are the same as for the
national population. Although this assumption may be somewhat in error
for specific counties, no data are available at the county level which
would permit a more refined analysis.
Having calculated emissions and fuel consumption for the seven
categories of internal combustion engines under study, it is now possible
to construct a summary which can be compared to the NEDS (National
Emissions Data System) survey data for AQCR 070. This summary is
presented in Table 32 with the NEDS data for off-highway gasoline- and
diesel-powered vehicles and a total for the AQCR.
Table 32. SUMMARY OF EMISSIONS FROM ENGINE
CATEGORIES UNDER STUDY
Category
Outboard motors
Snowmobiles
Motor cycle sa
Lawn and garden
Construction
Industrial
Farm
. Total
NEDS gasoline
NEDS dips^l
NFTi*-! 1-nfal
HC
4 370
132.
1, 200.
1, 530.
1,720.
2,120.
1,760.
12,800.
4 050
337
4. 390.
CO
1 3 000
205.
3,700.
12,200.
17,700.
33,700.
20,900.
101,000.
22 200
2 050
24. 200
Emissions
NOX
25 6
2.1
9.4
124.
11,500.
4, 720.
2,970.
19,400.
1 290
3 370
4 AAn
, 103 kg/
Part.
5.8
24.
32.5
875.
332.
353.
1,620.
5Q
1 1 ft
1 77
yr
RCHO
1.9
8.9
28.
240.
96.
120.
490.
sox
?c n
0.2
3.5
13.0
849.
304.
233.
1,430.
36
74&
£ "O
?R?
125 percent of this total estimated off-highway.
3Off-highway I. C. engine area sources.
44
-------
VI. METHODOLOGY FOR GRID ELEMENT EMISSIONS ESTIMATES
The intrinsic properties of grid elements which can be helpful
in making emissions estimates for them include:
1. area (1, 4, 25, or 100 krn^) - some portion of area may not
be in county for a given element
2. highway mileage by type of highway in rural areas (street
details not given on maps)
3. presence and amount of surface water suitable for boating
4. area in open land suitable for farming
5. area in towns, cities, and incorporated places
6. number of dwellings (some counties) in rural areas
To indicate typical detail given on a county highway map (scale is 1/2
inch = 1 mile or 1:126, 720), a section of the map for St. Louis County,
Missouri is included as Figure 1. Several of the grid elements have
been laid out on this map section to document their appearance, the
larger ones being 5 km square and the smaller ones 2 km square. The
computer program (a copy of which is in Appendix D) prints geographical
coordinates to the nearest 0.01 second of angle, which represents the
nearest 8 x 10"^ inch for longitude and the nearest 1 x 10 inch for
latitude on the maps. It is obvious that the grids cannot be plotted with
this kind of accuracy, and a reasonable estimate of accuracy is ±0.02
inches on the map or an actual error of ±65 m on the ground.
The distributions of several engine categories under investi-
gation in this methodology development program are probably related
more strongly to population than to any other single variable. Popu-
lation data by grid element, however, are not available from any known
source. It seems desirable to have a system for allocating population
to grid elements; so this problem will be analyzed here before addressing
specific methodologies for engine categories. Neglecting population den-
sity variations within incorporated places (incorporated places are out-
lined on county highway maps), grid element population can be estimated
by
45
-------
PARTIAL. OVERLAY OF
ON
MAP Of ST.
Figure 1. Sample layout of grid elements on a county highway map
46
-------
/privately-owned grid land area\
grid element population =( * ' . a--- J x
5 *" r ^ area of incorporated place /
(population of incorporated place).
Grid element area is given by definition, and both the other variables
can be obtained from Appendix B, Table B-2 of the County and City
Data Book* ' for places having a population of 2500 or more. Places
having fewer than 2500 people are outlined on county highway maps, and
their populations are given; so both variables can still be obtained (area
to be measured by geometric sections or polar planimeter). For a
serious effort directed at grid element estimates, larger-scale maps
than the ones obtained for reference during this project should be avail-
able and are highly recommended for use.
Population estimates for grid elements not in incorporated areas
are not as simple as those for incorporated areas. In this case, the
best which can be done is to allocate (by area) the county's rural popu-
lation to land remaining after all incorporated areas have been sub-
tracted. This process takes the form
. , , , . /privately-owned grid land area\
grid element population = ( : = ; ]x
° r r \ county unincorporated area J
(county farm population + county rural nonfarm population),
where "county unincorporated area" can be determined by subtracting
areas of incorporated places from total county area. An easier but
somewhat less rigorous estimate could be made by assuming that
"county unincorporated area" is equal to county area in farms'39)f
with small probable errors in most parts of the country.
Although not mentioned specifically thus far, grid elements which
contain parts of two incorporated places, and/or two counties, and/or
both incorporated and unincorporated places will have to be treated in
separate parts. After the parts have been analyzed, the grid total popu-
lation estimate can be summed.
A. OUT BOARDS
The major variable by which boating can be allocated to grids
is availability of surface water of a suitable nature. The relationship
proposed for outboard emission estimates is
grid element emissions (kg/yr) = (county emissions, kg/yr) x
/ grid surface water area \
\county surface water area/
47
-------
The equation should be satisfactory where all county surface water is
suitable for boating, but the accepted total county figure' ' should
probably be revised if some water geometrically OK for boating is
heavily polluted, moving very swiftly, or otherwise unfit for use by
small boats. The degree of care exercised on this point depends on the
desired accuracy of the estimate and the amount of detailed data avail-
able for the area under study. Surface water areas for grid elements
can be determined by measurement (e.g. , by polar planimeter) using
maps of the largest practically available scale. On a map such as the
segment shown in Figure 1, minimum-size features used as input to
tabular data' ^ are represented by ponds 0.14 inch in diameter and
rivers 0.062 inch wide. This reporting guideline does not necessarily
reflect a typical minimum water area for outboard operation, but it
would be an involved matter to form a new criterion since re-measure-
ment of all the county's inland water area would be involved. In all
cases, county surface water area from Reference 41 should be checked
(at least roughly) against the county map, because many reservoirs have
been built since I960.
B. SNOWMOBILES
Emissions from snowmobiles will be allocated on an area basis,
since urban and non-urban ownership and usage patterns have already
been accounted for in the county methodology. The relationship which
follows is
(grid element land area\
county land area ) x
(county emissions, kg/yr).
This estimate could be modified by adding lakes which might be frozen
during the snowmobile season to the area terms, but such a modification
could hardly be justified by the accuracy of the overall estimate in most
cases. In the same way, uniform subtractions of areas in which snow-
mobiles do not run can probably not be justified.
C. MOTORCYCLES
Although some other variables may be significant, motorcycles
in service and their usage are probably related strongly to distribution
of population within the county. It is proposed, therefore, that the grid
element estimates for motorcycles be determined by the relationship
grid element emissions (kg/yr) = (county emissions, kg/yr) x
population estimate\
county population /'
48
-------
where the grid population estimate is made as described above.
D. LAWN AND GARDEN EQUIPMENT
Following the same general method used in allocating emissions
from lawn and garden equipment to counties, it will still be attempted
to apportion these emissions to grids according to location of one-unit
housing structures. Using the technique developed earlier for estimating
grid element population, the relationships which result are
/ grid one-unit structures \
grid element emissions (kg/yr) =^county one-unit structures^ x
(county emissions, kg/yr)
and
grid one-unit structures = (grid population) farea one-unit structuresX ^
\ area population /
The last term in the second equation is available for cities of 25, 000 or
more in tabular fornv ''. In all other privately-owned areas, the value
of that term will be assumed as 0. 230, which is the national average' ''.
E. CONSTRUCTION AND INDUSTRIAL EQUIPMENT
It is doubtful that any of the intrinsic properties of grid elements
correlate directly with construction equipment usage. While major con-
struction projects such as highways, sewer systems, and large buildings
are built to serve people's needs, they are often built on the periphery of
the densely-populated areas. Industrial areas are also often located
near, but not in, the most heavily-populated areas. These industrial
areas can be pinpointed, however, by examining zoning maps for the
area of interest if extreme accuracy is desired.
Having noted the shortcomings of the method, it is still necessary
(due to lack of other data) to allocate construction and industrial engine
emissions by population. The method derived earlier for grid population
estimates can be used in the relationship
. , , . ., , . /estimated grid element populationX
grid element emissions (kg/yr) =( ' B , *. )x
& v & ' \ county population /
(county emissions, kg/yr).
A more refined technique, using zoning laws, can be applied to industrial
engine emissions in areas of industrial zoning. This technique results
in the equation
49
-------
'grid element area in industrial/
. , , . . . /i / \ / commercial zones
grid element emissions (kg/yr) = r
1 county area zoned industrial/
commercial
(county emissions, kg/yr),
and this second technique is considered highly preferable to the popu-
lation-based method where the necessary information is available.
F. FARM EQUIPMENT
It will be assumed that emissions from farm equipment correlate
well with area (acreage) in farms, leading to the relation
'privately-owned unincorpo->
grid element emissions (kg/yr) = [ rated grid area | x
county area in farms
(county emissions, kg/yr).
Depending on the desired level of accuracy, the term in the denominator
could be checked against the county sum of privately-owned unincorpo-
rated area, which it is assumed to equal. If the two are not equal, then
"county area in farms" could be replaced by "county privately-owned
unincorporated area" to make the sum of the grid/county ratios equal
1.0.
50
-------
VII. SUMMARY
\
All the phases of this study have been completed, but it should
not be assumed that the results are a completely authoritative and cor-
rect analysis of emissions and fuel consumption on a county basis.
Throughout the narrative, it has been stressed that achieving the
project's objectives has often required usage of data which are really
insufficient for the task. The results must be used only with full
knowledge of their limitations, most of which were known even before
the study began.
Most of the basic emissions data on which county methodologies
(and hence grid methodologies) were derived are probably quite accurate;
but even from the points at which modal data were combined to yield
composite data or individual vehicle/engine data were combined to
produce category data, errors have certainly occurred. In all cases,
however, so many variables are missing that the errors cannot be
estimated statistically. Proper use of the study's results, then,
requires the knowledge that they are limited to estimates of an accuracy
commensurate with the time and effort which went into the project. In
other words, the estimates are reasonably good but in no way rigorous.
A number of good sources of emissions, population, and usage
data were found; and these sources are essentially the composition of
the List of References. A few References (e.g., 7-10, 39, 41, and
perhaps others) are primarily sources of county data relatable to
vehicle or engine population or usage, and a more complete list of
these documents is found in Table 19. Secondary sources of
county/small area data are listed in Appendix B, but they were not
used much in preparation of this report.
The county and grid element methodologies themselves have
been structured as much as possible to permit "plugging in" values
with little or no prior computation involved. In some cases, values
will have to be processed before use, such as those for percentages
of state population in a given county. It is simply not practical here to
convert all data to be used in the methodologies to compatible terms,
especially since the methodologies may not be used for all areas at
any foreseeable time.
51
-------
Due to the length of equations and explanations used in the
methodologies, it is not considered practical to reiterate them all in
this section. It is in order, however, to give an assessment of the
estimated relative accuracy of the methodologies. A rank-ordering
from most accurate to least accurate is as follows:
1.
2.
3.
4.
5.
farm equipment
motorcycles
construction equipment
lawn and garden,and outboards (tie)
industrial equipment and
snowmobiles (tie)
This assessment is based first on availability of county population data,
then on accuracy of emissions data, and finally on availability of usage
information.
As shown in Table 32, total emissions from sources under study
in this project exceed by considerable margins those estimated for
off-highway internal combustion engine sources by the NEDS system.
The basis for the NEDS estimates is not known at this time. To further
compare emissions estimated by the methodologies developed herein to
NEDS figures, Table 33 is presented to illustrate the impact of off-
highway source emissions on totals for AQCR 070. This comparison
shows estimated emissions of HC, CO, and NOX from uncontrolled
engines to be small but significant contributors to air pollution around
St. Louis. It likewise shows that these engines do not contribute
significantly to total particulate or SOX emissions in that area.
Table 33. IMPACT OF OFF-HIGHWAY SOURCES
ON EMISSIONS IN AQCR 070
Source categories
NEDS area sources
NEDS point sources
NEDS all sources
NEDS gasoline
off -highway
NEDS dies el off -highway
NEDS all off- highway
Off-highway (this report)
- as % NEDS total
Emissions, 10^ kg/yr
HC
196,541
71, 194
295, 123
4,050
337
4,390
12, 800
4.34
CO
922, 148
2,573, 063
3,495, 211
22, 200
2, 050
24, 200
101,000
2.89
NO...
-A.
Ill, 181
282, 132
393,314
1,290
3,370
4, 660
19,400
4.93
Part.
34, 043
287, 709
321, 752
59
118
177
1, 620
0. 504
SOX
42,730
1,077, 113
1, 119, 843
36
246
282
1,430
0. 128
52
-------
REFERENCES
1. State Boat Registration. MAREX Marketing Committee .
2. The Boating Business - 1973. The Boating Industry.
3. Hare, C. T. and K. J. Springer. Exhaust Emissions From
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines - Part 2, Outboard Motors. Environ-
mental Protection Agency. Contract EHS 70-108. January
1973.
4. Hare, C. T. and K. J. Springer. Exhaust Emissions from
2-Stroke Outboard Motors and Their Impact. Society of
Automotive Engineers. Paper No. 740737. September 1974.
5. Analysis of Pollution from Marine Engines and Effects on the
Environment. Environmental Protection Agency and Boating
Industries Association. Joint study number 30843.
6. Joint study funded by Environmental Protection Agency and
Departmentof Transportation on emissions from outboard
motors.
7. New York State Statistical Yearbook - 1973. New York State
Division of the Budget/Office of Statistical Coordination.
8. Statistical Abstract of Ohio - 1969. Economic Research
Division Development Department.
9. South Carolina Statistical Abstract - 1973. South Carolina
Budget and Control Board, South Carolina Division of Research
and Statistical Services. July 1973.
10. Wisconsin Statistical Abstract - Third Edition. Departmentof
Administration, State Bureau of Planning and Budget, Infor-
mation Systems Unit. June 1974.
11. Data on snowmobile registrations as of March 1, 1974 submitted
to C. T. Hare of SwRI by letter from John F. Nesbitt. May 22,
1974.
12. Hare, C. T. and K. J. Springer. Exhaust Emissions from
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines - Part 7, Snowmobiles. Environmental
Protection Agency. Contract EHS 70-108. April 1974.
53
-------
13. Hare, C. T. , K. J. Springer, and T. A. Huls. Snowmobile
Engine Emissions and Their Impact. Society of Automotive
Engineers. Paper No. 740735. September 1974.
14. Donahue, J. A. , et al. Small Engine Exhaust Emissions and
Air Quality in the United States. Society of Automotive Engineers.
Paper No. 720198. January 1972.
15. Kollman, R. E. , S. S. Lestz, and W. E. Meyer. Exhaust
Emission Characteristics of a Small 2-Stroke Cycle Spark
Ignition Engine. Society of Automotive Engineers. Paper No.
730159. January 1973.
16. Eccleston, B. H. and R. W. Hum. Exhaust Emissions From
Small, Utility, Internal Combustion Engines. Society of
Automotive Engineers. Paper No. 720197. January 1972.
17. Statistical Abstract of the United States - 1973. U. S. Depart-
ment of Commerce.
18. Automotive Industries 1974 Statistical Issue. April 1, 1974.
19. March 1974 Motorcycle Usage and Owner Profile Study.
Hendrix, Tucker & Walker, Inc.
20. Motorcycle Usage Study. Hendrix, Tucker & Walker, Inc.
Prepared for Motorcycle Industry Council. August 1973.
21. Goldish, J. C. , et al. Development of a Methodology to
Allocate Liquid Fossil Fuel Consumption by Country.
Environmental Protection Agency. Publication No. EPA-
450/3-74-021. March 1974.
22. Highway Statistics 1972. U. S. Department of Transportation,
Bureau of Public Roads. Tables VM-1 and VM-2.
23. Raney, J. L. and G. D. Kittredge. Measurement and Control
of Air Pollution from Aircraft and Other Off-Highway Propulsion
Systems. Prepared for International Clean Air Congress.
24. Motorcycle Mileage Data agreed upon by manufacturer members
of MIC Statistical Committee, July 1973. Submitted to C. T.
Hare of SwRI by Leo Lake of Yamaha Intl. Corp.
54
-------
25. Hare, C. T. and K. J. Springer. Exhaust Emissions From
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines - Part 3, Motorcycles. Environmental
Protection Agency. Contract EHS 70-108. March 1973.
26. Hare, C. T. and K. J. Springer. Motorcycle Emissions, Their
Impact, and Possible Control Techniques. Society of Automotive
Engineers. Paper No. 740627. August 1974.
27. Uncontrolled Vehicle Emission Study. State of California Air
Resources Board. October 30, 1973.
28. Wimette, H. J. and R. T. Van Derveer. Report on the Deter-
mination of Mass Emissions from Two-Cycle Engine Operated
Vehicles. Environmental Protection Agency. Contract CPA-
22-60-91. January 1970.
29. Draft Emission Regulations for New Motorcycles. Environmental
Protection Agency, Emission Control Technology Division
January 17, 1974.
30. Press releases from Outdoor Power Equipment Institutes, Inc.,
11/28/72, 12/1/71, 12/22/70, 1/12/70, 12/6/68. 734 15th
Street Northwest, Washington, D. C. 20005.
31. Implement &.Tractor magazine, issues of 1/7/73, 4/7/72,
1,21/71, 5/21/70, 8/21/69, and others.
32. Hare, C. T. and K. J. Springer. Exhaust Emissions From
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines - Part 4, Small Air-Cooled Spark Ignition
Utility Engines. Environmental Protection Agency. Contract
EHS 70-108. May 1973.
33. Hare, C. T. and K. J. Springer. Small Engine Emissions and
Their Impact. Society of Automotive Engineers. Paper No.
730859. September 1973.
34. Hare, C. T. and K. J. Springer. Exhaust Emissions From
Uncontrolled Vehicles and Related Equipment Using Internal
Combustion Engines - Part 5, Farm, Construction, and
Industrial Engines. Environmental Protection Agency.
Contract EHS 70-108. October 1973.
35. Henderson, R. D. Air Pollution and Construction Equipment.
Society of Automotive Engineers. Paper No. 700551. 1970.
55
-------
36. Henderson, R. D. Digging Into Air Pollution ProblemsAn
Earthmover's Viewpoint. Society of Automotive Engineers.
Paper No. 720609 1972.
37. Hardwick, G. C. and C. R. Hudson. Farm and Industrial
Tractors - Emission Trends and Their Impact. Society of
Automotive Engineers. Paper No. 730829. September 1973.
38. Van Loan, M. and L. Resnick. Impact of Emissions from Farm
Equipment and Off-Road Heavy Duty Equipment on Air Pollution
in California. Society of Automotive Engineers. Paper No.
730830. September 1973.
39. County and City Data Book, A Statistical Abstract Supplement.
U. S. Department of Commerce. 1972.
40. 1969 Census of Agriculture, Volume I - Area Reports. U. S.
Department of Commerce, Bureau of the Census.
41. Area Measurement Reports. U. S. Department of Commerce/
Bureau of the Census. Publication GE-20, No. 1. May 1970.
42. Motor Vehicle Units Registered for the Year 1973. State of
Illinois. Accounting Revenue Division.
43. 1973 County Audit Report. Missouri Department of Revenue.
44. Climatological Data,National Summary. Department of Commerce.
Volume 15, No. 1. 1964.
45. Data on Contracting Bid Volume from several issues of
Construction Machinery and Equipment (periodical).
46. Current Industrial Reports, Internal Combustion Engines 1971
(and Prior Years to 1964), Series MA-35L(71)-1. U. S.
Department of Commerce, Bureau of the Census.
56
-------
APPENDIX A
TABULAR DATA ON POPULATION, USAGE, AND EMISSIONS
OF SELECTED MOBILE SOURCE CATEGORIES
-------
Table A-l. 1973 BOAT REGISTRATIONS AS COMPILED
BY THE U. S. COAST
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dist. of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Outboard boats
131,933
11,642
41,706
67, 20-1
264,085
29,027
54, 159
17,027
2,349
219,433
53,414 .
8, 185
36,541
182, 120
. 91,264
100,009
61, 100
82,586
102,868
29,441
57,579
103,823
512,302
306, 165
37,545
105,013
13,299
State
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Guam
Puerto Rico
Virgin Islands
Total including 48 states and D. C.
Outboard boats
31, 266
14, 170
6,844
. 93,746
22, 141
295, 171
87,716
12,085
127,509
125, 686
85,337
119, 872
10,483
119,206
18,049
161, 136
385, 196
19,264
21,369
96,407
78, 110
11,971
331,980
7,362
437
7, 200
2,802
4,984, 065
A-2
-------
Table A-2. ESTIMATED STATE DISTRIBUTION OF
OUTBOARD MOTORS, DECEMBER 31, 1973<2)
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dist. of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Motor s
150, 000
44,000
121,000
385,000
38,000
92,000
23,000
26,000
527,000
152,000
43,000
312,000
201,000
103,000
71,000
99,000
292,000
86,000
131,000
177,000
482,000
380,000
68,000
196,000
20,000
State
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Perm sylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
Motors
38,000
16,000
43, 000
185, 000
19,000
534, 000
134, 000
24, 000
293, 000
114,000
114, 000
195,000
28, 000
129,000
20,000
167,000
470,000
35,000
23, 000
120,000
185,000
27,000
370, 000
8,000
7, 510, 000
A-3
-------
Table A-3. U. S. SNOWMOBILE REGISTRATIONS
AS OF MARCH 1, 1974(n)
State
Alaska
Arizona
California
Colorado
Connecticut
Idaho
Illinois
Iowa
Maine
Massachusetts
Michigan
Minnesota
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Dakota
Ohio
Oregon
Pennsylvania
Rhode Island
South Dakota
Utah
Vermont
Washington
Wisconsin
Wyoming
Total U. S.
Snowmobiles
20, 100
1,000
15,000
23, 000
15,300
32,000 .
34,500
26,000
75,260
71,900
400,000
290,400
30,000
400
3,000
49,000
12,000
2, 100
172,776
37,751
12,500
10, 600
60,000
1,050
25,077
13,500
13,013
10,500
233,569
12,000
1,714,796
A-4
-------
Table A-4. 1973 MOTORCYCLE REGISTRATIONS BY STATED8)
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Dist. of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Motorcycles
65,560
15, 143
62,768
34,036
631,961
81,871
51,440
6,050
4,045
142,478
90,454
12,000
45,936
177,487
99,000
118,545
99,399
49,112
40,000
20,713
44,000
67,000
269, 185
119,277
130,000
95,263
State
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
Motorcycles
37, 133
45,000
15,434
20, 544
69,208
30,799
91,575
95,435
18,738
179,359
94, 156
100,203
231,475
15, 190
33, 232
19,785
74, 000
257,400
51,375
8,981
69,000
91, 184
48,703
77,080
14,893
4, 362,605
A-5
-------
Table A-5. MOTORCYCLE BREAKDOWNS BY SIZE
FOR REGIONS OF THE UNITED STATES*1?)
"Region" of the U.S.
New England
Middle Atlantic
East North Central
West North Central'
South Atlantic
Motorcycle size distribution
Displacement, cc
90 and less
91-190
191-290
291 and over
Unclassified
90 and less
91-190
191-290
291 and over
Unclassified
90 and less
91-190
191-290
291 and over
Unclassified
90 and less
91-190
191-290
291 and over
Unclassified
90 and les.s
91-190
191-290
291 and over
Unclassified
% of population
9
20
9
59
3
20
22
12
46
0
23
22
11
42
2
18
26
11
45
0
22
25
10
40
3
States included
in "region"
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
New Jersey
New York
Pennsylvania
Illinoi s
Indiana
Michigan
Ohio
Wisconsin
Iowa
Kansas
Minne s ota
Missouri
Nebraska
North Dakota
South Dakota
Delaware
Dist. of
Columbia
Florida
Georgia
Maryland
North Carolina
South Carolina
Virginia
West Virginia
table continued next page
A-6
-------
Table A-5 (continued). MOTORCYCLE BREAKDOWNS BY SIZE
FOR REGIONS OF THE UNITED STATES^?)
"Region1 'of the U.S.
East South Central
West South Central
Mountain
Pacific
Motorcycle size distribution
Displacement, cc
90 and less
91-190
191-290
291 and over
Unclassified
90 and less
91-190
191-290
291 and over
Unclassified
90 and less
91-190
191-290
291 and over
Unclassified
^
90 and less
91-190
191-290
291 and over
Unclassified
% of population
24
27
3
40
6
21
33
7
36
3
28
32
10
22
8
20
27
16
35
2
States included
in "region"
Alabama
Kentucky
Mississippi
Tennessee
Arkansas
Louisiana
Oklahoma
Texas
Arizona
Colorado
Idaho
Montana
Nevada
New Mexico
Utah
Wyoming
Alaska
California
Hawaii
Oregon
Washington
A-7
-------
Table A-6.
SALES, PRODUCTION, AND POPULATION ESTIMATES
FOR LAWN AND GARDEN EQUIPMENT
Previous Population Estimate for Small Utility Engines (1968-A11)(14)
Engine type
Lawn and garden 4- stroke
Lawn and garden 2- stroke
Miscellaneous 4- stroke
Average rated hp
3.43
3.43
3.86
Total
Engines in service
36, 200, 000
2, 500,000
5, 550,000
44, 250,000
Outdoor Equipment Sales and Population Estimates(30)
Sales or population for sales year, millions
Type of equipment
Walking mowers
Lawn tractors and
riding mowers
parden tractors
Total lawn and garden
Estimated total in use
Motor tillers
Snow throwers
ai973
5.45
0.74
0.26
6.45
0.43
0.33
1972
5.2
0.68
0. 25
6. 13
43.
0.43
0.32
1971
4.7
0.88
b
5.58
38.
0.36
0.26
1970
4.7
0.95
b
5. 65
37.
0.36
0.24
1969
4.7
1.0
b
5.7
36.
0.38
0.26
1968
4.56
0.93
b
5.49
0.38
0.26
1967
4.9
0.25
b
5. 15
--
0.35
0. 18
aPrediction
included with lawn tractors and riding mowers.
Breakdown of 1966-1970 Small Engine Production by Application^ 1)
Application
Riding mower
Walking mower
Garden tractor
Motor tiller
Snow thrower
Other lawn and garden
Total lawn and garden
Recreation
Industrial
Agriculture
Miscellaneous
Total
Production (millions)
2.84
23.67
1.19
1.70
1. 18
1.31
31.89
1. 10
2.65
0.97
3.27
39.88
% of total
7. 1
59.4
3.0
4.3
3.0
3.3
80. 0
2.8
6.6
2.4
8.2
100.0
A-8
-------
Table A-7. EMISSION FACTORS AND FUEL CONSUMPTION FOR
CONSTRUCTION EQUIPMENT BY CATEGORY AND REFERENCE
Equipment
category
Tracklaying
tractors
Tracklaying
loaders
Motor
graders
Scrapers
Off -highway
Wheel
loaders
Wheel
tractors
Rollers
Diesel or
gasoline
Diesel
Diesel
Diesel
Gasoline
Both
Diesel
Diesel
Diesel
Gasoline
Both
Diesel
Gasoline
Both
Diesel
Gasoline
Both
Ref.
38a
34
38a
34
38a
34
38*
34
34
38a
34
38*
34
38a
34
38a
34
34
38a
34
38a
34
34
38a
34
38a
34
34
Brake specific emissions, g/hp hr
HC
0.69
0.685
0.36
0.362
1.68
0.532
7. 18b
8.62°
0.936
1.22
1.22
0.85
0.853
1.70
0.948
6.86b
7.35C
1.97
1.70
1.39^
6.86b
7.41C
1.99
1.68
0.777
7. 18b
12. Oc
6.71
CO
2.39
2.39
1.80
1.80
4.08
2. 15
218.
187,
11.4
2. 84
2. 84
2. 62
2.62
3.34
2.63
143.
163.
28.3
3.34
4.40
143.
142.
18. 1
4. 08
3.64
218.
202.
193.
NOX
9.08
9.08
6.56
6.56
9. 03
10.6
5.24
4.92
10.3
12. 1
12. 1
14.9
14.9
9.39
11.2
6.62
5.41
10.3
9.39
9.34
6.62
6.37
9.05
9.03
15.8
5.24
5.47
8.57
Part.
0.69
0.692
0.66
0.655
1.51
0.613
0.37
0.320
0.598
0.79
0.789
0.50
0.502
1. 28
0.810
0.37
0.312
0.730
1.28
1.27
0.35
0.360
1.18
1.51
0.777
0.37
0.394
0.506
RCHO
0. 17
0. 10
0. 12
_
0.30
0. 13
»
0.28
*
0.22
tm
0.20
0.22
0.20
.
0.28
0.26
0. 28
_
0.20
«,
0.25
0. 24
S0x
0.85
0.851
0.85
0.853
0.92
0.874
0.22
0.26
0.844
0.90
0.901
0.89
0.887
0.87
0.857
0. 23
0.244
0.759
0.87
0.851
0.23
0. 230
0.789
0.92
1. 00
0.23
0.279
0.495
BSFC
g/hp hr
.
193.
194.
199.
_ .
295.
-
205.
_
201.
.
195.
276.
-
.
193.
269.
-
.
228.
_
325.
-
table continued next page
A-9
-------
Table A-7 (continued). EMISSION FACTORS AND FUEL CONSUMPTION
FOR CONSTRUCTION EQUIPMENT BY CATEGORY AND REFERENCE
Equipment
category
Wheel
dozers
General
purpose
All equip't.
Earth-
movers
only
Diesel or
gasoline
Diesel
Diesel
Gasoline
Both
Both
Diesel
Ref.
38a
34
38a
34
38a
34
34
34
33d'
36e
34
Brake specific emissions, g/hp hr
HC
0.58
0.576
1.68
l'°^
7.18*
8.3f
1.85
1.45
0.958
0.630
1.04
CO
1.83
1.83
4. 08
2.82
218.
198.
32. 1
14.9
2.84
3. 19
2.90
NOX
12.5
12.5
9.03
14.8
5.24
4.79
13.3
9.61
6.53
8.94
9.62
Part.
0.41
0.411
1.51
0.907
0.37
0.300
0.816
0.731
0.29
0.633
RCHO
0. 16
_
0.20
-
0. 23
0.21
0.20
_
-
sox
0.87
0.867
0.92
0.933
0.23
0.273
0.834
0.752
_
1.53
0.769
BSFC
g/hp hr
197.
212.
-
308.
-
-
_
-
-
aAll Reference 38 values based on Reference 34.
b
25 percent allowance included for evaporative and crankcase emissions,
°Allowance included for evaporative and crankcase emissions (variable).
dEstimate for 1969 made in 1970.
eEstimate for 1969 made in 1972.
A-10
-------
APPENDIX B
LIST OF COUNTY DATA SOURCES
-------
PRIMARY SOURCES
1. County and City Data Book, A Statistical Abstract Supplement.
U. S. Department of Commerce. 1972.
2. 1969 Census of Agriculture, Volume I - Area Reports. U. S.
Department of Commerce, Bureau of the Census.
3. Area Measurement Reports. U. S. Department of Commerce/
Bureau of the Census. Publication GE-20, No. 1. May 1970.
4. New York State Statistical Yearbook - 1973. New York State
Division of the Budget/Office of Statistical Coordination.
5. Statistical Abstract of Ohio - 1969. Economic Research
Division Development Department.
6. South Carolina Statistical Abstract - 1973. South Carolina
Budget and Control Board, South Carolina Division of Research
and Statistical Services. July 1973.
7. Wisconsin Statistical Abstract - Third Edition. Department of
Administration, State Bureau of Planning and Budget, Infor-
mation Systems Unit. June 1974.
8. Motor Vehicle Units Registered for the Year 1973. State of
Illinois. Accounting Revenue Division.
9. 1973 County Audit Report. Missouri Department of Motor
Vehicles.
B-2
-------
SECONDARY SOURCES
10. Economic Abstract of Alabama 1972. Center for Business and
Economic Research, Graduate School of Business, The Univer-
sity of Alabama, University, Alabama, December 1972.
11. Arizona Statistical Review. Phoenix, Arizona, Economic Re-
search Department, September 1973.
12. The Arkansas Almanac 1972. Little Rock, Arkansas, Arkansas
Alamanca, Incorporated.
13. California Statistical Abstract 1973. Sacramento, California, 1973.
14. Delaware Statistical Abstract 1974. Social and Economic Analysis
Section, Delaware, State Planning Office, Dover, Delaware.
15. Florida Statistical Abstract 1973. Gainsville, Florida, University
of Florida Press, August 1973.
16. Norman Nybroten. Idaho/1971 Statistical Abstract, Moscow, Idaho,
University of Idaho, August 1971.
17. 1972 Edition Illinois State and Regional Economic Data Book. State
of Illinois Department of Business and Economic Development.
18. 1972 Statistical Profile of Iowa. Des Moines, Iowa, The Iowa
Development Commission.
19. Kansas Statistical Abstract 1973. Institute for Social and Environ-
mental Studies, The University of Kansas, Lawrence, Kansas.
20. Statistical Abstract of Louisiana. Division of Business and Econo-
mic Research, College of Business Administration, Louisiana State
University in New Orleans. Fourth Edition 1971.
21. 1973 Maryland Statistical Abstract. Department of Economic and
Community Development, State of Maryland, Annapolis, Maryland.
22. Michigan Statistical Abstract, Tenth Edition 1974. East Lansing,
Michigan, Michigan State University.
23. Minnesota Statistical Abstract 1973, Vols. 1 and 2. St. Paul, Min-
nesota, Minnesota State Planning Agency.
B-3
-------
24. Mississippi Statistical Abstract 1973. Mississippi State,
Mississippi, Division of Research, College of Business and
Industry, Mississippi State University, May 1973.
25. Data for Missouri Counties. Columbia, Missouri, University
of Missouri.
26. Montana Data Book. Helena, Montana, Department of Planning
and Economic Development, State of Montana, 1970.
27. Nebraska Statistical Handbook, 1974-1975. Lincoln, Nebraska,
The Nebraska Department of Economic Development.
28. New Mexico Statistical'Abstract 1972. Albuquerque,. New Mexico,
The University of New Mexico.
29. North Carolina State Government Statistical Abstract, Second Edition
1973. Statistical Services Section, Office of State Budget, Depart-
ment of Administration.
30. Statistical Abstract of Oklahoma 1972. Norman, Oklahoma, Bureau
for Business and Economic Research, University of Oklahoma, May
1973.
31. Pennsylvania Abstract 1973. Harrisburg, Pennsylvania, Department
of Commerce.
32. Tennessee Statistical Abstract 1 971. Knoxville, Tennessee, Center
for Business and Economic Reasearch, The University of Tennessee.
33. Texas Almanac and State Industrial Guide 1972-1973. A. H. Belo
Corporation.
34. Statistical Abstract of Utah 1973. Bureau of Economic and Business
Research, Center for Economic and Community Development, Uni-
versity of Utah.
35. Vermont Facts and Figures 1973. Montpelier, Vermont-, Vermont
Department of Budget and Management, March 1973.
36. Statistical Abstract of Virginia 1966, Vol. I and 1970, Vol. II.
Charlottsville, Virginia, University of Virginia.
37. The Research Council's Handbook, Fourth Edition. Olympia, Wash-
inton, Washington State Research Council.
38. The 1973 Statistical Handbook. Charleston, West Virginia, West
Virginia Research League, Inc.
B-4
-------
39. Wyoming Data Book 1972. Laramie, Wyoming, Division of
Business and Economic Research, University of Wyoming.
B-5
-------
APPENDIX C
DOCUMENTATION OF COUNTY METHODOLOGY DEVELOPMENT
-------
DOCUMENTATION OF COUNTY METHODOLOGY DEVELOPMENT
The purpose of this Appendix is to present procedures utilized
in arriving at two of the county methodologies described in Section IV,
including several methods which proved unsuccessful. The categories
for which this presentation will be made are outboard motors and snow-
mobiles. Methodologies for the other categories were developed in a
more straightforward way because either (1) ample information was
available on which to base a logical method or (2) insufficient information
was available to check on the method developed. In the first case, the
methodologies will yield emissions data having good accuracy. In the
second case, the accuracy of calculated values simply cannot be assessed;
so they must be accepted as gross estimates. The categories for which
good data are available are motorcycles and farm equipment. Those
for which few data are available are lawn and garden equipment, cons-
truction equipment, and farm equipment.
1. Outboard Motors
A number of general regression analyses were attempted; and to
show the results concisely, the following terms are defined:
fl = percent of state boat registrations in county
f2 = percent of state boat usage in county
f3 = percent of state population in county
f5 = percent of state inland water area in county.
Data were obtained on f 1, f3, and f5 for New York^7), Ohio^8), South
Carolina''', and Wisconsin'* '. Data on f2 were obtained only for New
York. Regressions were calculated for all the data together, and also
for individual states, urban and rural areas, and coastal and inland
areas. The results of these regression analyses are shown in Table
C-l, and none of the general ones is very promising.
Another approach tried was to characterize the outboard popu-
lation in terms of generalities and then to fit a mathematical model to
these generalities once complete. The observations and calculated data
were the following:
(a) Boat registrations are basically proportional to population
in each state.
(b) Except for inland counties having no surface water usable
for boating ("dry" counties), boats used correlate strongly
with boat registrations (r over 0.9). To be recorded, ponds
must have areas of 40 acres (0. 16 km ) or more and streams
must be at least 1/8 mile (0. 20 km) wide.
C-2
-------
Table C-l. REGRESSION ANALYSES ATTEMPTED
ON OUTBOARD MOTOR DATA
Dependent
variable
£2
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
Independent
variable(s)
fl
f3
f3
f3
f3
f3
f3
f3
f3
f3
£3
f3,f5
£3
£3
Data utilized
All NY
All coastal
All inland
NY coastal
Ohio coastal
SC coastal
Wise, coastal
NY inland
Ohio inland
SC inland
Wise, inland
All NY
All urban
All non-urban
Coefficients )
a
0.0431
1.103
0.255
0.616
0.781
0.503
0.545
0.150
0.172
0.0874
0.340
0.0627
3.97
0.222
b
0.905
0.672
0.885
1.195
0.591
1.075
0.532
1.42
0.880
0.910
0.873
1.257
0.420
0.927
c
0.146
r2
0.892
0.694
0.809
0.686
0.958
0.985
0.991
0.752
0.836
0.8.13
0. 766
0.795
0.372
0.806
(c) As an average, 9 percent more boats are used in coastal
counties than are registered in those counties'''. This gen-
eralization includes counties bordering the great lakes as
well as the oceans.
(d) Congested urban areas generally show fewer outboards re-
gistered than would be projected solely on a population basis.
The following terms were also defined:
^ = county population density (inhabitants/mi ), ( i 1000 is cri-
terion for urban county;
', ", '"as superscripts indicate values after first, second, and
third corrections (coastal-inland correction, dry-wet correction,
and urbannon-urban correction, respectively);
c, i, d, w, u, and n as second subscripts mean "coastal", "in-
land", "dry", "wet", "urban", and "non-urban", respectively.
The estimation procedure was as follows:
(a) Assume f2 = f3 (f3, f5, and { , and an indicator of coastal or
inland status should be tabulated by county for the state)
C-3
-------
(b) Make the "coastal-inland" correction by calculating:
I £c
f2c = 1.09f2c; £2i = f2i j 1 -0. 09 =£=£- J; and tabulating
by county. \ 2_,f2i/
(c) Make the "dry-wet" correction by calculating:
r-» i \
2_,f2d\
I + - 1 j; and tabulating by county.
(d) Make the "urban- rural" correction by calculating:
/ V " V '"
., Lf2 - Lf2u
f2n = f2n\ V " V '" /; and tabu-
lating by county.
in
The values £2 were the final results for all the counties on a per-
centage basis and could be multiplied by the state boat population to yield
the actual number of boats used in each county. While the method guaran-
tees that the sum of the f2 equals 100 percent, the individual f did not
agree very well with the individual f2 for New York (r^ = 0.35).
2. Snowmobiles
The only county snowmobile registration data located were for
New York' ', and a number of approaches were attempted before a
usable relationship was found. The following terms are defined for
convenience:
gl = percent of state snowmobile registrations in county
g2 = percent of state snowmobile usage in county
g3 = percent of state population in county
g4 = snowfall, in/yr
g5 = development index = (number of snowmobile developments
in county)*^- -*.
The regression analyses attempted are described in Table C-2,
verifying that snowmobile usage correlates well with registration.
Table C-2 also shows that separating urban and rural areas enhances
the accuracy of the estimate for rural areas and that the "development
index" is only a marginal contributor to variability in usage. The ex-
pression second from the bottom of Table C-2 was the one modified
for use in the methodology, along with an empirical correction to re-
flect low registrations and usage in congested urban areas. The modi-
fication consisted of normalizing the snowfall term to a percentage of
snowfall at the state's geographical center, making the coefficient c
take on the new value 0.0321.
C-4
-------
Table C-2. REGRESSION ANALYSES ATTEMPTED
ON NEW YORK SNOWMOBILE DATA
Dependent
variable
g2
g2
g2
g2
g2
g2 .
g2
§2
g2
g2
g2
Independent
variable(s)
gl
gl,g5,g3,g4
gl, g5, g3, g4
gl.g3.g5.g4
g3
g4
(g5)2
g3,g4
g3,g5, g4
g3,g4
g3, g4, g5
Data utilized
All
All
Non-urban
Urban
All
All
All
All
All
Non-urban
Non-urban
Coefficients )
a
0.0392
-2. .224
-2.418
0.119
1.702
-1.020
1.306
-2.125
-2.229
-2.345
-2.424
b
0.977
-0.00294
-0.00175
0.981
-0.0092
0.0400
0.111
0.250
0.273
1.560
1.579
c
0.234
0.143
-0.00272
0.0521
0.236
0.0458
0.0432
d
0.272
1.576
0.0945
0.0476
0.145
e
0.0478
0.0433
-0.00660
r2
0.994
0.425
0.665
0.999
0.00016
0,317
0.0633
0.408
0.424
0.657
0.665
o
I
-------
APPENDIX D
UTM TO GEOGRAPHIC COORDINATE CONVERSION PROGRAM
-------
0001)03
000003
000003
OOOOOH
COOUOb
000007
000011
000012
000015
OOOOlb
000017
000020
000021
000033
00003H
OOOOBb
000037
OOOOHO
OOOOH2
OOOOH5
000050
000052
00005H
OOOOSS
OQOOb?
000070
000073
000075
00007S
000077
000100
000100
000102
000104
OOOlOb
000107
Q00110
000111
000117
000121
000123
Q0012H
Q0012b
000127
000130
Q00131
000132
000133
00013b
000145
0001b2
OUOlbb
PPRD
SNLT
CSLT
CSSO
PHRD
PROGRAM UTMGEOUNPUT, OUTPUT)
DIMENSION YNORTHlH ) , XE AST (t ) r ILD( H ) , ILM(H ) , SL A (H )
DIMENSION IGD( 4 ) , IGM( H ) , SNG( * )
(DEGREES) PROGRAM TRANSFORMS UTM TO GEOGRAPHIC
COORDINATES (TBK-lb-J AN-73)
SCALE = .'llSb
ESQ = .OUb?h8bSB
SECRD = i.BtBiabSllE-Ob
SEPO = .OH088870S4
FE = SOOUUO,
EPSO = ESQ/d.-ESQ)
IDIR =0
IZONE=15
IPAGE=1
LCT=S8
32 READ 101 , ID, XEAST, YNORTH
IFCID .EO.O) GO TO SS
XZONE = IZONE
DO 200 I = lrH
YY=YNORTH(I)
XX=X£AST(I)
CM = (b. * XZONE - 183,) * 3bOO.
(YY * . 1570H S1810 * 10.0E-7) / SCALE
SINCPPRD)
C03CPPRD)
CSLT * CSLT
(CCSSQ*, 2^82 + 30. 02335)*CSSQ + 5078.bfS7?)*(SNLT*CSLT)
1*10. E-7 + PPRD
PHIS = PHRD/SECRD
0 = (XX-FE)*10.E-7
IF(O.NE.O) GO TO 720
DLAM = 0.
XLAT = PHIS
GO TO 7fO
720 CONTINUE
SNLAT = SIN(PHRD)
CSLAT = COS(PHRD)
SNSO = SNLAT * SNLAT
CCSO = CSLAT * CSLAT
TNLAT = SNLAT/CSLAT
TNSQ = TNLAT * TNLAT
ENU = b37820b.f / SORTCl,-ESQ*SNSQ)
ENSNS = ENU * SECRD
EPCS = EPSO * CCSO
EPCSQ = 1. « EPCS
OSO = 0*0
GCU = OSGi * 0
OFR = OCU * Q
OFV = OFR * 0
OSX = OFV * Q
SCLAT = 1. /CSLAT
ENSNS = ENU**'f * ENSNS
SVN = (((TNLAT/(2.*ENU*ENSNS))*F.PCSO)/(SCALE*SCALE)5*10.E11
EG=5.+3.*TNSOtSEPO*CCCSQ-SNSQ)-(3.*EPSQ**2*CCSO)*(CCSO+3.
1*SNSO)
EGH = TNLAT/(2H.*ENU*ft3*ENSNS)
EGHT = CEGH*EG/SCALE**4)*lO.E+23
D-2
-------
000172
OOOEOb
000213
QUC220
OOOS23
00023fa
0002bH
000272
000300
000300
000302
Dbl=bl.-K45,*TN3Q)*(2,+TNSQ-EP8Q*SNSQ)+EPSQ*(l07,*CCSQ
l-lb2.*3NSU)
0003Gb
000310
000313
000315
000320
000322
000325
000327
000332
OU033S
OOQ33b
000340
000341
0003H3
00034H
OOOSHb
000350
00035fa
00035b
000357
OOOSbl
000413
000415
OU0415
OOOH1S
000415
000417
Ob s (QSX*Db2*Dbl/SCALE**b)*10Et3S
ANINE s (SCLAT/ENSN3)/SCA|.E*lU.ES
TEN * CSCLAT/(b. *ENU**2*£NSNS) )*(i.t2.*TNSQ+EPC3)
1/SCALE**3*10,E17
ES = UFV*(SCLAT/(120,*ENSN5))*(5,+(4,*tNSQ)*(7f+b,*TNSQ)
l+(2.*EPSQ)*(3.*CCSQt4.*SNSO))/SCALE**5*10,E21
XLAT ~ PHIS-SVN*QSQ + EGHT*QFR - Ob
OLAM a ANIN£*Q - TEN*QCU +E5
740 CONTINUE
XLONG a CM + OLAM
YLONG = -XLONG/3bOO.
YLAT s XLAT/3bOO,
101 s YLAT
REH s (YLAT-IDl) * 3faOO.
IMA = REM/faO,
31 - REM - (IMl*bO,)
102 a YLONG
REM a (YLONG « 102) * 3bOO,
IM2 = REM/bO.
32 = R£M-( IM2*bO,)
ILO(I)sI01
ILM(I)aIMl
5LA(I)csl
IGO(I)=ID2
IGM(I)=IM2
SNGCI)sS2
200 CONTINUE
IFCLCT .LT, 58) GO TO fO
PRINT 104,IPAGE
10f FORMAT(*l*»15Xf*ST LOUIS AQCR GRID SQUARE COORDINATES*/* PAGE*/I3/
1*10 1 2 3 4*
Z /10X, *DEG MIN SEC DEC MIN SEC DEC HIN SEC DEG MIN SE
3C*)
IPAGE sIPAGE+1
40 PRINT 102f 10, ( ILO(I) » ILM(I) ,SLA(I) , 1=1,4),
1 (IGD(I),IGM(I),SNG(I), 1=1,4)
LCT=LCT*3
FORMAT(I4,IX,-3P8F5.1)
FORMAT(*(I*,I4,* LAT*,4(n, I3,Ffa,2)/5X,*
GO TO 32
STOP
END
101
102
13,Fb,2))
D-3
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
2.
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
Methodology for Estimating Emissions from Off-Highway
Mobile Sources for the RAPS
5. REPORT DATE
10-30-74
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Charles T. Hare
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Southwest Research Institute
8500 Culebra Road
San Antonio, Texas 78284
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-1397
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final Report 3/74-9/74
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Emissions, population, and usage data existing in the technical literature have
been collected and organized for the following unregulated sources: outboard motors,
snowmobiles, motorcycles, lawn and garden equipment, construction equipment, in-
dustrial equipment, and farm equipment.
Methodologies for estimating emissions and fuel consumption on a county basis
have been developed for the sources noted above. They have been demonstrated for
counties in the St. Louis Metropolitan Air Quality Control Region (AQCR 070), and
their strengths and weaknesses have been discussed. Methods have also been de-
veloped to apportion county emissions estimates to grid elements, but they have not
been demonstrated. The exhaust constituents assessed include hydrocarbons (HC),
carbon monoxide (CO), oxides of nitrogen (NO ), particulate, aldehydes (RCHO), and
oxides of sulfur (SO ). For outboard motors, neither particulate nor aldehyde data
were available; but Carbon dioxide (C02) emissions were included.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Mobile Source Emissions
Apportion Emissions
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86
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EPA Form 2220-1 (9-73)
-------
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EPA Form 2220-1 (9-73) (Reverse)
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JANUARY 1977 AMC7010.T0108E-CR
OFF-HIGHWAY MOBILE SOURCE
EMISSION INVENTORY
100% COMPLETION REPORT
Prepared for
Environmental Protection Agency
Office of Air and Water Management
Office of Air Quality Planning Standards
Research Triangle Park, North Carolina 27711
by
F.E. Littman
K.M. Isam
Rockwell International
Atomics International Division
Air Monitoring Center
11640 Administration Dr.
Creve Coeur, MO 63141
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AMC7010.T0108E-CR
TABLE OF CONTENTS
PAGE
ABSTRACT 1
1.0 INTRODUCTION 2
2.0 OFF-HIGHWAY MOTORCYCLES 3
2.1 ESTIMATION OF OFF-HIGHWAY MOTORCYCLES IN USE 3
2.2 ASSUMPTIONS PERTAINING TO TYPICAL ENGINE SIZE, TYPE,
AND ANNUAL MILEAGE 4
2.3 OFF-HIGHWAY MOTORCYCLE EMISSION FACTORS 6
2.4 EMISSIONS PER COUNTY DUE TO OFF-HIGHWAY MOTORCYCLES 6
2.5 GRID ELEMENT EMISSIONS 8
3.0 LAWN AND GARDEN EQUIPMENT EMISSIONS 10
4.0 CONSTRUCTION EQUIPMENT EMISSIONS 15
5.0 INDUSTRIAL EQUIPMENT 20
6.0 FARM EQUIPMENT 26
7.0 OUTBOARD MOTORBOATS 30
8.0 TEMPORAL APPORTIONMENT 36
9.0 SUMMARY 37
REFERENCES 39
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AMC7010.T0108E-CR
TABLES
PAGE
TABLE 1 MOTORCYCLE REGISTRATIONS, TOTAL MOTORCYCLES AND OFF-HIGHWAY
MOTORCYCLES PER COUNTY 4
TABLE 2 ANNUAL MILEAGE AND POPULATION DISTRIBUTION FOR MOTORCYCLES
AT THE NATIONAL LEVEL 5
TABLE 3 OFF-HIGHWAY MOTORCYCLE EMISSION FACTORS 7
TABLE 4 OFF-HIGHWAY MOTORCYCLE EMISSIONS PER COUNTY 7
TABLE 5 COUNTY POPULATIONS 9
TABLE 6 SAMPLE CALCULATION DATA, OFF-HIGHWAY MOTORCYCLES 9
TABLE 7 EMISSION FACTORS FOR LAWN AND GARDEN EQUIPMENT 12
TABLE 8 EMISSIONS AND ONE-UNIT HOUSING STRUCTURES PER COUNTY 13
TABLE 9 DATA FOR SAMPLE LAWN AND GARDEN EQUIPMENT EMISSIONS
CALCULATION 14
TABLE 10 ESTIMATED CONSTRUCTION MACHINERY POPULATIONS, USAGE,
RATED HORSEPOWER, AND SERVICE LIFE 16
TABLE 11 ESTIMATED NATIONAL CONSTRUCTION EQUIPMENT EMISSIONS 16
TABLE 12 CONSTRUCTION EQUIPMENT EMISSIONS PER COUNTY 18
TABLE 13 CONSTRUCTION ACREAGE PER COUNTY 18
TABLE 14 DATA FOR SAMPLE CONSTRUCTION EQUIPMENT EMISSIONS
CALCULATION 19
TABLE 15 NATIONAL POPULATION, RATED POWER, AND ANNUAL USAGE OF
HEAVY-DUTY AND LIGHT-DUTY INDUSTRIAL ENGINES 20
TABLE 16 RECOMMENDED EMISSION FACTORS FOR INDUSTRIAL ENGINES 21
TABLE 17 NATIONAL TOTALS OF EMISSIONS FROM INDUSTRIAL ENGINES 21
TABLE 18 INDUSTRIAL EQUIPMENT COUNTY APPORTIONMENT DATA 23
-n-
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AMC7010.T0108E-CR
PAGE
TABLE 19 INDUSTRIAL EQUIPMENT EMISSIONS PER COUNTY 24
TABLE 20 INDUSTRIAL EQUIPMENT SAMPLE CALCULATION DATA 25
TABLE 21 FARM EQUIPMENT ANNUAL USAGE ESTIMATES 26
TABLE 22 RECOMMENDED EMISSION FACTORS FOR FARM EQUIPMENT 28
TABLE 23 FARM EQUIPMENT EMISSIONS PER COUNTY 28
TABLE 24 FARM ACREAGE PER COUNTY 29
TABLE 25 DATA FOR SAMPLE CALCULATION OF FARM EQUIPMENT EMISSIONS
FROM A GRID ELEMENT 29
TABLE 26 OUTBOARD EMISSION FACTORS (KG/UNIT HR.) 33
TABLE 27 OUTBOARD REGISTRATIONS PER COUNTY 33
TABLE 28 STATE OUTBOARD EMISSIONS IN THE AQCR 34
TABLE 29 OUTBOARD EMISSIONS AND NAVIGABLE SURFACE WATER PER COUNTY 34
TABLE 30 DATA FOR SAMPLE CALCULATION OF GRID EMISSIONS 35
-Hi-
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AMC7010.T0108E-CR
FIGURES
PAGE
FIGURE 1 SAMPLE/FORTRAN PROGRAM 38
-TV-
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AMC7010.T0108E-CR
ABSTRACT
Six categories of mobile off-highway sources of pollution have been
analyzed, and emissions of HC, CO, NO,,, SO., and Particulates have been cal-
culated with the aid of a computer for all the 1,989 grid squares comprising
the St. Louis AQCR. Equipment categories included were motorcycles, lawn and
garden equipment, industrial equipment, construction equipment, farm equipment
and outboard motorboats. Emissions contributed by each category were treated
separately.
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AMC7010.T0108E-CR
1.0 INTRODUCTION
The purpose of the off-highway mobile source emission inventory was to
calculate emissions for the Metropolitan St. Louis Air Quality Control Region
(AQCR 070) of a variety of unregulated sources with a spatial resolution corre-
sponding to grid elements1. An EPA methodology for determining the criteria
pollutant emissions of such sources was used as a guide2. Six equipment
categories were dealt with: motorcycles, lawn and garden equipment, construc-
tion equipment, industrial equipment, farm equipment, and outboard motorboats.
Problems were encountered, some significant, in the application of the method-
ology. Departures from it were made where necessary for optimum utilization of
available data. Simplifying assumptions pertaining to area distribution of
equipment populations and usage were used to make calculations possible which
generally inadequate data would have otherwise prohibited.
The procedures involved in arriving at grid element emission values have
been described in detail, all deviations from the recommended methodology noted
and explained. This was not, and could not be (considering the quality of
existing data on the different machine types) a rigorous computation of off-
highway emissions. Instead, this inventory has been an attempt to determine the
order of magnitude of emissions at the grid level within the limitations imposed
by the nature of the subject.
-2-
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AMC7010.T0108E-CR
2.0 OFF-HIGHWAY MOTORCYCLES
2.1 ESTIMATION OF OFF-HIGHWAY MOTORCYCLES IN USE
Among the contributors to off-highway emissions are those motorcycles
specially designed for off-road use. This means the so-called "trail bikes",
"dirt bikes", and "mini-bikes", whose popularity has burgeoned in the last few
years. The primary problem with assessing the emissions impact of these vehicles
was that of accurately determining the number in use in a given area. There is
no registration requirement for off-highway motorcycles in either Illinois or
Missouri. Thus, it was assumed for this emission inventory that the number used
off the highway was equal to the number of unregistered motorcycles.
The estimate for unregistered motorcycles cited in Reference 2 is 15% of
the total motorcycle population of the St. Louis AQCR. An approximation of
total motorcycles per county was obtained by augmenting the number of county
registrations utilizing this percentage. Thus,
(1) Total County Motorcycles = County Registrations = County Registrations
Off-highway motorcycles in a county were calculated by taking 15% of total
county motorcycles, or
(2) Off-Highway Motorcycles Per County = .15 x Total County Motorcycles
,,- County Registrations
lb .85
= .18 x County Registrations
The number of motorcycles registered per county was available from References
3 and 4 for Missouri and Illinois, respectively. This number together with the
calculated number of total and off-highway motorcycles per county appears in
Table 1.
It is recognized that some registered motorcycles were used both on and
off the highway. However the 15% estimate is of limited accuracy, and for this
reason dual-use cases were eliminated from consideration in the inventory.
-3-
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AMC7010.T0108E-CR
TABLE 1
MOTORCYCLE REGISTRATIONS,
TOTAL MOTORCYCLES AND OFF-HIGHWAY MOTORCYCLES PER COUNTY
COUNTY
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
COUNTY I.D.
4300
4280
6900
4680
2280
4160
1680
1440
5180
6460
0520
7920
REGISTRATIONS
15,567
7,263
5,071
6,129
3,019
3,263
1,673
721
552
999
506
300
TOTAL MOTORCYCLES
(including
unregistered)
18,314
8,545
5,966
7,211
3,552
3,839
1,968
848
649
1,175
595
353
OFF-HIGHWAY
MOTORCYCLES
(unregistered)
2,747
1,282
895
1,082
533
576
295
127
97
176
89
53
2.2 ASSUMPTIONS PERTAINING TO TYPICAL ENGINE SIZE, TYPE, AND ANNUAL MILEAGE
To facilitate the computation of emissions, a "typical" off-highway motor-
cycle was defined. The characterization required an average value, based on
representative sampling, for each of three parameters:
1) engine size (engine displacement in cubic centimeters)
2) engine type (2-stroke or 4-stroke and population distribution between
2-stroke and 4-stroke)
3) annual mileage
No quantitative information on these parameters was available which was
strictly applicable to off-highway motorcycles - only general statistics
describing the national motorcycle population as a whole. A combination of the
-4-
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AMC7010.T0108E-CR
general statistics and qualitative information pertaining specifically to "trail
bikes" provided the basis for the assumed parameter values. A departure from
the recommended methodology2 was required at this point as it provided only for
total motorcycle emissions, and no technique for isolating off-highway emissions
from the rest was discussed.
Statistical information utilized in assigning values is contained in
Table 22. The data contained in it refer to the national motorcycle population.
It was felt the most straightforward method to assign a single parametric value
was to determine the size range in which off-highway motorcycles belong, and
then use the values for annual mileage and distribution which correspond to the
particular range. By taking this approach extensive manipulation of data of
somewhat limited applicability was avoided.
TABLE 22
ANNUAL MILEAGE AND POPULATION DISTRIBUTION
FOR MOTORCYCLES AT THE NATIONAL LEVEL
ENGINE SIZE
90cc or less
90-191cc
191-290cc
over 290cc
ANNUAL MILEAGE
750
1400
2100
3000
RATIO OF 2-STROKE TO 4-STROKE
2-STROKE
11
19
8
13
4-STROKE
9
8
3
29
Motorcycles were grouped according to engine displacement5'6 as follows:
1) under lOOcc - almost exclusively mini-bikes
2) lOOcc - strictly dirt-bikes and trail bikes
3) 125cc - by far "the biggest class of all... considered somewhat small
for safe street riding... strictly for dirt and competition riding."
4) 175cc - "this class is primarily for the dual-purpose and dirt-riding
enthusiast". Second only to the 125cc category for off-highway use.
-5-
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AMC7010.T0108E-CR
5) 250cc -athe weight factor rules out "the big... cycles displacing
over 250cc's, as well as the overweight 250s" for off-road use. An
D
extremely small number of motorcycles displacing 250cc and above are
used by an elite group of serious racing enthusiasts.
As this analysis of different motorcycle sizes revealed, the 90-190cc
range was the most appropriate range within which the "typical" off-highway
motorcycle would fall. From Table 2, then, the corresponding annual mileage
was assumed to be 1400; 2-stroke and 4-stroke motorcycles were assumed to be
distributed in a 19 : 8 ratio respectively.
2.3 OFF-HIGHWAY MOTORCYCLE EMISSION FACTORS
Recommended emission factors2 are shown in Table 3. Separate emission
factors for 2-stroke and 4-stroke engines were available. Since it was assumed
that the two different types of engines occurred in a 19 : 8 ratio, a composite
emission factor was computed by combining the two factors in a weighted average
(Table 3) as, for example:
(3) SO,, .Off-Highway Emission Factor, kg/mile =
(.040 x 10"3 kg/mile x 19) + (.023 x 10"3 kg/mile x 8)
19 + 8
= .035 x 10~3 kg/mile SOX
2.4 EMISSIONS PER COUNTY DUE TO OFF-HIGHWAY MOTORCYCLES
To calculate county emissions a modified version of the equation used in
the recommended methodology2 was used to compute off-highway emissions instead
of total motorcycle emissions. Thus,
(4) County Emissions, kg/yr = Off-Highway Motorcycles in County
x Emission Factor, kg/mile x 1400 miles/yr
where 1400 miles/year is the assumed average value for annual mileage. Off-
highway motorcycle emissions per county appear in Table 4. For example the
emissions of S0>, in Franklin County have been calculated as:
-6-
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AMC7010.T0108E-CR
(5) County Emissions, kg/yr = 295 x (.035 x 10 3 kg/mile) x (1400 miles/yr)
= 14.5 kg/yr
where 295 is from Table 1 and .035 x 10~3 kg/mile is the factor for SOX from
Table 3.
TABLE 3
OFF-HIGHWAY MOTORCYCLE EMISSION FACTORS
ENGINE TYPE
2-Stroke
4-Stroke
Weighted Composite
(2-Stroke & 4-Stroke
combined in a 19:8
ratio)
KG/MILE OF EMISSIONS x 10"3
HC
24.0
4.0
18.0
CO
32.4
39.6
34.5
NOY
A
0.06
0.36
0.148
PART
0.33
0.04
0.244
sox
0.040
0.023
0.035
NOTE: These factors allow for evaporative hydrocarbon
emissions.
TABLE 4
OFF-HIGHWAY MOTORCYCLE EMISSIONS PER COUNTY
COUNTY
St. Louis County (4300)
St. Louis City (4280)
St. Clair (6900)
Madison (4680)
Jefferson (2280)
St. Charles (4160)
Franklin (1680)
Clinton (1440)
Monroe (5180)
Randolph (6460)
Bond (0520)
Washington (7920)
EMISSIONS, KG/YR x 103
HC
69.2
32.3
22.6
27.3
13.4
14.5
7.43
3.20
2.44
4.44
2.24
1.34
CO
133
61.9
43.2
52.3
25.7
27.8
14.2
6.13
4.69
8.50
4.30
2.56
NOX
.569
.266
.185
.224
.110
.119
.061
.026
.020
.037
.018
.011
PART
.938
.438
.306
.370
.182
.197
.101
.043
.033
.060
.030
.018
sox
.135
.063
.044
.053
.026
.028
.0145
.0062
.0057
.0086
.0044
.0026
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AMC7010.T0108E-CR
2.5 GRID ELEMENT EMISSIONS
Knowing county emissions, grid element emissions were calculated according
to the relation:
(6) Grid Element Emissions, kg/yr = County Emissions, kg/yr x county°Pop^ation
This relation expresses the direct proportionality assumed between motorcycle
emissions and population as recommended in Reference 2.
Two more assumptions are implicit in this approach; first, that unregistered
motorcycles are distributed uniformly over the counties, and second that their
usage is also uniformly distributed over the counties, in proportion to county
population. While assumption (1) may be realistic, assumption (2) is not, but
no better way is readily available.
For an illustration of the calculation of grid element emission, SO,,
emissions from grid #1 have been calculated from data in Table 6.
(7) Grid Element Emissions of SOY, kg/yr = 14.5 x
1059
60,459
(Grid #1 - off-highway motorcycles) = 0.254 kg/yr
A computer tabulation is available7, which lists all 1989 grid elements
in increasing numberical order, and across from each grid number is printed the
identification number of the county in which the grid falls, the grid element
population, housing units in the grid, and other useful statistics. Grid number,
county I.D. number, and grid populations were the items used from this printout
for the motorcycle emission inventory. Table 1 includes the SAROAD county
identification numbers, for the purpose of computer identification.
Due to the large number of grid elements and the five separate calculations
of emissions of the five primary pollutants required for each grid element or
square, it was found advantageous to write a computer program in FORTRAN that
would process the available data and yield grid element emissions from off-
highway motorcycles.
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AMC7010.T0108E-CR
TABLE 5
COUNTY POPULATIONS
COUNTY I.D. NO.
4300
4280
6900
4680
2280
4160
1680
1440
5180
6460
0520
7920
COUNTY NAME
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Cl inton
Monroe
Randolph
Bond
Washington
POPULATION
996,515
578,493
309,777
230,290
102,223
101,713
60,459
29,538
21,193
32,289
14,014
13,852
TABLE 6
SAMPLE CALCULATION DATA
OFF-HIGHWAY MOTORCYCLES
VARIABLE
Grid Element Number
Pollutant
County
County Emissions
(Off-Highway)
County Population
Grid Element Population
VALUE
1
sox
Franklin (1680)
14.5 kg/yr
60,459
1059
SOURCE OF VALUE
Specified
Specified
Reference 7
Table 4
Table 5
Reference 7
-9-
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AMC7010.T0108E-CR
3.0 LAWN AND GARDEN EQUIPMENT EMISSIONS
The lawn and garden category includes several types of equipment, in par-
ticular riding mowers, walking mowers, garden tractors, and motor tillers.
Snowthrowers have not been included in the inventory for two reasons. First,
they represent only a very small percentage of lawn and garden equipment, and
second, they are rarely used more than two or three times per year in the
St. Louis AQCR.
As for the four types of equipment which were considered, the walking mower
is by far the most common, comprising approximately 75% of total equipment units,
with riding mowers the next highest at only 9%.8 Garden tractors and motor till-
ers account for even less, approximately 5% of total units in each case.8 Two
types of engines occur as a rule, either 2-stroke or 4-stroke, and they make up
6% and 94% of small utility engines respectively.8 So-called "typical" horse-
power ratings for them are based on population estimates of walking mowers,
garden tractors, etc., coupled with a knowledge of the engine types found most
frequently to occur in a particular application. Thus, the 2-stroke is rated on
the average at 3.0 horsepower, and the 4-stroke at 3.5 horsepower.2
To be sure, there are still certain difficulties involved in trying to de-
termine the number of small utility engines in use and precisely how and where
those engines are being used. No registration data exists and there is no truly
adequate sales or production information available. Furthermore, no reliable
distribution statistics as to type and size of engines in use have been compiled.
In spite of these obstacles, estimates have been made which provide sufficient
groundwork for an emissions inventory with grid element resolution. But it must
be added that with present limited information, emission figures at the grid
level are only approximations, meant solely to give an idea of the order of
magnitude of emissions per grid resulting from the off-highway mobile sources
under consideration.
More encouraging are the emission factors which have been derived for small
utility engines. A variety of engines of the type used in lawn and garden equip-
ment have been tested in the laboratory and their emissions measured accurately
-10-
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AMC7010.T0108E-CR
under different loads.2 Some such engines have even been tested, albeit on a
limited basis, while oeprating under normal work-loads in the field, exhausts
being collected in bags or constant-volume samplers during the grass-cutting
or other characteristic operation. So, as might be expected, the emission fac-
tors for such mobile sources are quite reliable as long as operating conditions
are taken into account. As is natural, simulated operations and actual field
operations can be at variance with one another, and the human factor will always
yield different operating patterns. Hence, while emission factors may be good,
it is in the application of them that caution must be exercised. Recommended
emission factors for lawn and garden equipment are in Table 72.
A few assumptions were made in deriving and applying the factors in Table
7 which bear mentioning here. They pertain to the seasonal nature and variation
with climate of equipment usage. In Reference 2, it was assumed that national
mean operating days per year amounted to 213, and the average usage time for the
nation as a whole was 50 hours per year. The average number of freeze-free days
(or equivalently mean operating days) per year in the St. Louis area is 190 +_ 40;
so 190 was used as a county mean.9 (The 190 day figure is more recent than the
205 day figure used in the recommended methodology.2) It was assumed that there
were 2.7 million 2-stroke engines and 50.2 million 4-stroke engines,2 and using
the emission factors in Table 7 in conjunction with the 50 hour usage figure
national emissions (kg/yr) were calculated for each of the primary pollutants.
Emissions were apportioned to the twelve AQCR 070 counties on the basis of
housing units per county. The total number of one-unit housing structures in
the nation was assumed to be 46.8 million.
This brings up an important point about the significance of housing struc-
tures in the inventory. A direct relationship was assumed between one-unit
housing structures in a given area and the number of small utility engines in
use in that area, on the strength of the excellent agreement between the two
found in the U.S. Census publications. Since housing units and engines can be
assumed to be directly proportional, a knowledge of housing structures per grid
makes possible grid-element apportionment of emissions on this basis.
-11-
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AMC7010.T0108E-CR
TABLE 7
EMISSION FACTORS FOR LAWN AND GARDEN EQUIPMENT
UNITS
G/HR
KG/YR
ENGINE TYPE
2-STROKE
4-STROKE
2-STROKE
4-STROKE
EMISSION FACTORS
HC
300
37
15
1.8
CO
660
380
33
19
NOX
2.1
4.2
0.01
0.21
PART
9.4
0.6
0.47
0.03
sox
0.8
0.5
0.04
0.02
NOTE: These factors allow for evaporative hydrocarbon emissions.
Of course, this is oversimplifying the matter somewhat, since a certain
number of lawnmowers, tillers, etc., are used in commercial application. There
are additional small utility engines arising from households with two or more
pieces of lawn and garden equipment. Whether these "extra" engines are offset
by the households which have only electric equipment is uncertain. To obtain
a more accurate inventory, it would have been necessary to locate each commercial
organization and obtain information on the utilization of ground maintenance
equipment. A survey of households with more than one piece of lawn and garden
machinery would have been necessary, too. Finally, an inventory of households
with electric lawn mowers, edgers, and the like would have had to be made.
Since this was felt to be very impractical, it was decided the best course to
follow was assumption of a one-to-one correspondence between one-unit housing
structures and small utility engines.
The value of the one-to-one relation becomes apparent in the equation used
to calculate lawn and garden equipment emissions at the county level:
(8) County Emissions, kg/yr = National Emissions, kg/yr
County One-Unit Housing Structures
National One-Unit Housing Structures
County Mean Operating Days
x 213
where county mean operating days = 190 for the St. Louis AQCR.9
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AMC7010.T0108E-CR
The national emissions total was calculated utilizing the emission factors
in Table 7 which are in kg/yr. As previously mentioned it was assumed that 2.7
million 2-stroke engines and 50.2 million 4-stroke engines were used nationally.
Thus for CO emissions, for example,
(9) National Emissions of CO = 2-stroke emissions + 4-stroke emissions
= (33 kg/yr x 2.7 x 106) + (19 kg/yr x 50.2 x 106)
= 1.043 x 109 kg/yr
Then to calculate county emissions of CO, from Madison County for instance, we
have after substituting the proper values into equation 8:
(10) County Emissions, kg/yr = 1.043 x 109 kg/yr x 65'533 c x 19°
46.8 x
213
= 1.303 x 10° kg/yr of CO
In Table 8 emissions for all AQCR 070 counties are shown (in units of 10 kg/yr)
TABLE 8
EMISSIONS AND ONE-UNIT HOUSING STRUCTURES PER COUNTY
COUNTY
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
I.D.
NO.
4300
4280
6900
4680
2280
4160
1680
1440
5180
6460
0520
7920
ONE UNIT
HOUSING
STRUCTURES
235,202
81,784
68,769
65,533
27,593
21,631
15,882
7,788
5,383
8,624
4,490
4,848
EMISSIONS 103 kg/yr
HC
586
204
171
163
68.8
53.9
39.6
19.4
13.4
21.5
11.2
12.1
CO
4675
1625
1367
1303
549
430
316
155
107
171
89.3
96.4
NOX
47.4
16.5
13.9
13.2
5.6
4.36
3.20
1.57
1.08
1.74
.90
.98
PART
12.4
4.33
3.64
3.47
1.46
1.14
.84
.41
.28
.46
.24
.26
SOV
A
4.99
1.73
1.46
1.37
.58
.46
.34
.17
.11
.18
.95
.102
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AMC7010.T0108E-CR
Once emissions per county are known, emissions per grid square follow
from the relation
(11) Grid Element Emissions, kg/yr -
* (County Eraissions
Grid one-unit structures were available from Reference 7. County one-unit
structures, found in Reference 2, have been included in Table 8.
TABLE 9
DATA FOR SAMPLE LAWN AND GARDEN EQUIPMENT
EMISSIONS CALCULATION
VARIABLE
Pollutant
Grid Element
Grid Element One-Unit
Structures
County
County One-Unit Structures
County Emissions
VALUE
CO
281
68
Madison (4680)
65,533
1303 x 106 kg/yr
SOURCE
Specified
Specified
Reference 7
Table 8 (or Ref. 7)
Table 8
Table 8
To better illustrate the procedure, emissions of CO from grid element #281
will be calculated here. The necessary data has been assembled in Table 9 for
convenience.
(12) Grid Element Emissions, kg/yr of CO =
Grid #281
(1.303 x 10 kg/yr)
= 1.35 x 10 kg/yr
Lawn and garden equipment emissions from all grids have been calculated with the
aid of a Fortran program.
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AMC7010.T0108E-CR
4.0 CONSTRUCTION EQUIPMENT EMISSIONS
Construction equipment types considered in the inventory are listed
in Table 10, along with estimated populations, usage, and rated horsepower.
Since few data are available on either sales or population of the various
machines estimates were heavily relied upon. Some machines, like tracklaying
tractors, wheel loaders, and scrapers are better represented in the literature
than others. The major sources of data on construction equipment are general-
ized national figures on units shipped per year, annual usage, total horse-
power in use, load factors, and duty cycles10. Specific population data by
machine type and manufacturer, or engine type are not available2.
Composite emission factors for the ten construction categories were
developed, assuming a distribution for each category composed of test engines
in the same combination10. These factors were meant to reflect not only the
composition of population by size and type of engine, but the typical duty or
operating cycles as well. Taken together with the estimates in Table 10 of
machinery population, etc., the factors were used to calculate national emis-
sions of construction equipment10. The results are shown in Table 11.
In arriving at the numbers in Table 11, three assumptions supplemented
the estimates in Table 10. First, construction equipment life (in years),
found by dividing service life (in hours) by usage (in hours/year), could be
used along with typical annual shipments to estimate the number of units in
service, or population. Second, emissions from construction engines could be
estimated by combining the results of a number of laboratory tests. Third,
engine operating cycles could be deduced from manufacturers' operating data
to a reasonable approximation. The tests took evaporative hydrocarbon emis-
sions into account.
National emissions were apportioned to the states of Illinois and Missouri
by construction volume (in dollars) according to the relation:
(13) State Emissions, kg/yr = (National Emissions, kg/yr)
(
(
State Construction Volume)
National Construction Volume)
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AMC7010.T0108E-CR
TABLE 10
ESTIMATED CONSTRUCTION MACHINERY POPULATIONS, USAGE,
RATED HORSEPOWER, AND SERVICE LIFE10
Equipment Type
Track! aying Tractors
Tracklaying Loaders
Motor Graders
Scrapers
Off-highway Trucks
Wheel Loaders
Wheel Tractors
Rollers
Wheel Dozers
General Purpose
Population
197,000
86,000
95,300
27,000
20,800
134,000
437,000
81,600
2,700
100,000
Usage, hr/yr
1050
1100
830
2000
2000
1140
740
740
2000
1000
Horsepower
120
65
90
475
400
130
75
75
300
120
Service Life, hr
10,000
10,000
12,000
12,000
12,000
12,000
12,000
12,000
12,000
TABLE 11
ESTIMATED NATIONAL CONSTRUCTION EQUIPMENT EMISSIONS10
EMISSIONS IN KG/YR x 10C
Fuel
Diesel
Gasoline
Total
HC
72
56
128
CO
220
1100
1320
NOX
820
36
856
PART
63
2.2
65.2
sox
65
1.6
66.6
Dollar volume of construction was available only at the national and state
levels so could not be used for a more refined distribution of emissions. Con-
struction acreage was known for the St. Louis AQCR counties. State construction
was not known, making it impossible to determine the county percentages of
state construction. Consequently emissions were allocated to the counties by
population. This represented the least desirable method but is the only viable
-16-
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AMC7010.T0108E-CR
alternative since state and county populations were both known quantities.
Population can be considered to be a sufficiently reliable indicator of ongoing
construction, there being an approximately proportional relationship between the
two. State emissions were then apportioned to the counties by the relation.
(14) County Emissions, kg/yr = State Emissions, kg/yr x Cstate Population
Emissions contributed by construction equipment to each of the twelve
counties under consideration are shown in Table 12. Homebuilding and other
light construction emissions were taken to be negligible compared to contracted
construction jobs in the county apportionment computations. Also, construction
expenditures in heavy construction, and highway and bridge construction were
weighted by a factor of 3 relative to building construction.
Using the values for county emissions set forth in Table 12, grid element
emissions were calculated. Although the methodology by Hare2 suggests appor-
tionment of county emissions to the grid elements by population, a different
approach was taken for the present inventory. Recently, a computer tabulation
has become available,11 which assigns to each of the grid elements a value for
construction acreage. This makes it possible to use it rather than population
to allocate emissions to the individual grid elements as follows:
(15) Grid E1«ent Mssions, kg/yr -
x (County Emissions, kg/yr)
It was assumed that the areas experiencing construction had remained more
or less the same since the time when construction acreage allotments were made.
Construction acreage per county may be found in Table 13.
As an example of the calculation, emissions of NOw from Grid Number 61
have been calculated. Pertinent data for the calculation are in Table 14.
(16) Grid Element Emissions, kg/yr = i]??gacresj\ x (4.51 x 105 kg/yr)
\ I T- I D aCi GS )
=4.94 x 10 kg/yr
As with the other off-highway categories calculation for the 1989 AQCR grid
squares was accomplished through the aid of a Fortran program.
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AMC7010.T0108E-CR
TABLE 12
CONSTRUCTION EQUIPMENT EMISSIONS PER COUNTY2
COUNTY
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
I.D. NO.
4300
4280
6900
4680
2280
4160
1680
1440
5180
6460
0520
7920
EMISSIONS, 103 kg/yr
HC
689
451
175
154
76.3
67.5
40.1
17.4
11.6
19.2
8.60
8.46
CO
7,100
4,650
1,810
1,580
787
696
413
180
120
198
88.7
87.3
NOX
4,610
3,010
1,170
1,030
510
451
268
116
77.6
128
57.5
56.6
PART
351
230
89.3
78.2
38.9
34.4
20.4
8.87
5.91
9.78
4.38
4.31
sox
358
234
91.2
79.9
39.7
35.1
20.8
9.06
6.03
9.99
4.48
4.40
TABLE 13
CONSTRUCTION ACREAGE PER COUNTY11
COUNTY
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
I.D. NO.
4300
4280
6900
4680
2280
4160
1680
1440
5180
6460
0520
7920
TOTAL CONSTRUCTION ACREAGE
4,789
292
1,718
1,535
1,178
1,416
431
302
196
339
175
93
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AMC7010.T0108E-CR
5.0 INDUSTRIAL EQUIPMENT
Fork lifts, motorized utility carts, small tractors and wheel loaders,
quarrying machinery, portable generators, and any other fuel consuming mobile
equipment used at industrial plants or in the performance of industrial oper-
ations, all fall within the scope of the industrial equipment category. In
general their engines may be divided into two broad categories - small utility
engines similar to those used in lawn and garden, or heavy-duty engines.
Determination of engine population and size distributions has been accom-
plished by studying shipment and production statistics for small utility and
heavy-duty industrial engines10. Obtaining accurate estimates involved separa-
tion of locomotive engines and so-called "miscellaneous four-stroke small utility
engines" from the available statistics. Pertinent estimates for heavy-duty
engines may be found in Table 15. Service life of light-duty industrial gasoline
engines was assumed to be 600 hours and annual usage 100 hours on the average10.
TABLE 1510
NATIONAL POPULATION, RATED POWER, AND ANNUAL USAGE OF
HEAVY-DUTY AND LIGHT-DUTY INDUSTRIAL ENGINES
HORSEPOWER
Diesel 125
Gasoline (Heavy-duty) 55
Gasoline (Light-duty) 3.86
USAGE, HR/YR
600
300
100
POPULATION
417,000
990,000
5,800,000
There are no really typical duty cycles (fractions of operating time spent
in various rpm or speed ranges) for industrial engines since applications are
so diverse. For heavy-duty gasoline and diesel engines a "general purpose
industrial" cycle has been proposed10 using special weighting factors corre-
sponding to more than twenty different operating modes. Composite emission
factors were devised to represent the variety of models on the market. They
were based on the weighted emissions of twelve test engines. No attempt at a
rigorous correlation with population was made due to the general lack of
specificity characteristic of available statistics10.
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AMC7010.T0108E-CR
Light-duty engine emission factors were developed along similar lines.
Recommended emission factors for the industrial category are presented in Table
16. National emissions from industrial engines have been computed using the
information in Tables 15 and 16. Resulting annual totals are in Table 17.
TABLE 16
RECOMMENDED EMISSION FACTORS FOR INDUSTRIAL ENGINES2
ENGINE TYPE
Heavy-Duty Diesel
Heavy-Duty Gasoline
Light-Duty Gasoline
UNITS
g/hp. hr.
g/hp. hr.
g/hr.
EMISSION FACTORS
HC
1.12
6.68
29.2
CO
3.03
199
386
NOX
14.0
5.16
7.68
PART
1.00
0.327
0.68
sox
0.931
0.268
0.60
NOTE: Allowance for evaporative hydrocarbon emissions was incorporated
into these factors.
TABLE 1710
NATIONAL TOTALS OF EMISSIONS FROM INDUSTRIAL ENGINES
EMISSIONS, 10° kg/yr
ENGINE TYPE
Heavy-Duty Diesel
Heavy-Duty Gasoline
Light-Duty Gasoline
TOTALS
HC
35.0
109.1
16.9
161.0
CO
94.8
3,251
133
3,478.8
NOX
437.9
84.3
4.5
526.7
PART
31.3
5.34
.39
37.03
sox
29.1
4.37
.35
33.82
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AMC7010.T0108E-C.R
A method has been developed2 to apportion national emission estimates
directly to counties using the relation
(17) County Emissions, kg/yr = (National Emissions, kg/yr)
(
(
County Total of A + B + C)
National Total of A + B +C)
where A = value added by manufacturing establishments
B = sales of wholesale trade establishments, and
C = value of shipments and receipts of mineral industries
Quantities A, B, and C are considered to be reliable indicators of industrial
activity. Their sum is proportional (directly, to a good approximation) to
industrial equipment usage. Values for A, B, and C obtained from Reference 12
are in Table 18, and emissions per county computed with these values may be
found in Table 19.
The final step was the apportionment of county emissions to all the
grid elements. Because industrial equipment would, by definition, only be
found at industrial plants, a listing of those grid squares containing such
plants along with the number of plants contained in each provided the basis
for apportioning emissions.
Using References 13, 14, and 15 a listing of all the industrial plants
in AQCR 070 was compiled including the grid elements or squares in which these
194 plants were located. Total grid squares with industrial plants in them
numbered 150. The number of industrial plants (190) represents the most
complete tabulation available in the most recent Regional Air Pollution Study
(RAPS) emission inventory. Admittedly, some industrial plants have not been
accounted for. Nonetheless, apportionment of emissions to grid elements on
the strength of this data was felt to produce the most accurate results.
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AMC7010.T0108E-CR
TABLE 18
INDUSTRIAL EQUIPMENT COUNTY APPORTIONMENT DATA
County
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
MILLIONS OF DOLLARS (1972)
A (= value added)
$
1,285.8
1,793.5
267.3
645.2
66.4
44.8
56.0
17.1
0.9
30.3
13.2
2.3
B (= wholesale sales)
$
3,065.356
4,518.156
519.297
229.629
17.333
33.644
25.699
17.391
12.829
14.394
14.583
15.643
C (= minerals)
$
9.8
0.7
0.0
2.8
3.0
0.0
0.0
0.0
0.0
18.4
0.0
2.8
U.S. TOTALS, $ 261,983.8 459,475.967 25,848.7
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AMC7010.T0108E-CR
TABLE 19
INDUSTRIAL EQUIPMENT EMISSIONS PER COUNTY
County
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
EMISSIONS, 103 kg/yr
HC
940
1,360
169
188
16.9
18.0
4.53
7.44
2.96
13.6
5.99
4.48
CO
20,316
29,396
3,653
4,070
365
390
119
161
64
294
129
96.7
NOX
1,950
4,451
553
616
55.3
59.0
18.1
24.3
9.7
44.5
19.6
14.6
PART
216
313
38.9
43
3.89
4.14
1.27
1.71
.68
31.3
1.37
1.03
sox
198
286
35.5
39.6
3.55
3.79
1.16
1.56
.62
2.85
1.26
.94
-24-
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AMC7010.T0108E-CR
County emissions were apportioned by the equation
(18) Grid Element Emissions, kg/yr = (County Emissions, kg/yr)
(Grid Industrial Plants)
(County Industrial Plants)
As an illustration, the emissions of SO,, from grid #1008 have been calculated.
Essential data are presented in Table 20.
(19) Grid Element Emissions, kg/yr of SOY = (286 x 103 kg/yr) x 2
31
grid #1008
= 1.85 x
kg/yr
Emissions from all grid elements were calculated with the aid of a computer.
There are certain limitations on the accuracy of this and other industrial
emissions calculations. Most severe is the necessity of starting with national
totals and making successive apportionments from them. National totals are good
estimates only and must be considered in that light. This point source listing
has been updated with the latest RAPS emission inventory data.
TABLE 20
INDUSTRIAL EQUIPMENT SAMPLE CALCULATION DATA
VARIABLE
VALUE
SOURCE
Pollutant
Grid Element
Grid Industrial Plants
County
County Industrial Plants
County Emissions
sox
1008
St. Louis City (4280)
31
286 x 103
Specified
Specified
Plant Listing
Reference 7
RAPS emission inventories
Table 19
-25-
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AMC7010.T0108E-CR
6.0 FARM EQUIPMENT
Among the equipment types used on farms which were taken into consider-
ation in this inventory were farm tractors, garden tractors used on farms,
and self-propelled combines, forage harvesters, and balers. In addition,
irrigation pump engines ("miscellaneous heavy-duty"), and the auxiliary
engines ("miscellaneous light-duty") used on some of the larger machinery
were considered. Extensive information on both the production and population
of such equipment was available, a great deal on tractors in particular.
However, a breakdown in terms of size and types of engines used in the current
population did not exist, requiring that estimates be made.
Much effort has been expended in the development of emission factors for
farm machinery by C. T. Hare10 and others. A detailed population and usage
analysis of farm tractors and other related equipment preceded emission factor
computation. Annual usage rates were estimated from either survey data
(available for tractors) or consideration of the fact that the usage of special-
purpose farm machinery was dictated by the crop acreage for which it was needed.
Annual usage estimates of the various equipment types are presented in Table 21,
along with typical horsepower ratings and load factors.
TABLE 21
FARM EQUIPMENT ANNUAL USAGE ESTIMATES10
TYPE OF EQUIPMENT
Diesel Tractor
Gasoline Tractor
Self-propelled Combine
Pull Combine
Balers
Forage Harvesters
Miscellaneous Heavy-duty
Miscellaneous Light-duty
ESTIMATE ANNUAL
USAGE, (HRS)
490
291
73
52
24
120
50
50
HORSEPOWER
80.2
40.9
110.0
25.0
40.0
140.0
30.0
3.5
LOAD
FACTOR
0.57
0.57
0.52
0.52
0.52
0.52
0.52
0.40
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AMC7010.T0108E-CR
Test engines on which much data had been gathered were assumed to represent
each field application. For each engine a typical duty or operating cycle
(estimated from manufacturers operating data and field operation data) was
assumed, composite load factors were derived, and finally emission factors
were computed. Resulting emission factors in kg/hr are in Table 22.
To calculate emissions from farm equipment, the following relationship
was used for this inventory:
(20) County Emissions, kg/yr = I (Equipment Population)
x (Annual Usage) x (Emission Factor kg/yr)
where the summation was taken over the equipment type used. Specific data
on equipment populations per county were available from Reference 16. This
data in conjunction with annual usage, emission factors (kg/yr) from Tables
21 and 22, made it possible to arrive at emissions per county (presented in
Table 23.
In apportioning county emissions to grid elements, the following rela-
tion was used:
(21) Grid Element, kg/yr = (County Emissions, kg/yr)
(
(
Farm Acreage in Grid)
County Farm Acreage)
County farm acreage is presented in Table 24. Acreage per grid element
is available from Reference 11. As explained therein, farm acreage was allo
cated to grid squares by means of land use maps and aerial photographs.
To exemplify the grid-apportionment procedure, the emissions of CO from
Grid #1 have been calculated. All necessary data are gathered in Table 25.
(22) Grid Element Emissions, kg/yr of CO = 2.08 x 106 kg/yr x 79*490
= 8.3 x 104 kg/yr
As with all other categories under consideration, emissions of the five cri-
teria pollutants have been calculated with the aid of a computer.
-27-
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AMC7010.T0108E-CR
TABLE 22
RECOMMENDED EMISSION FACTORS FOR FARM EQUIPMENT2
TYPE OF EQUIPMENT
Diesel Tractor
Gasoline Tractor
Self-propelled Combine
Pull Combine
Balers
Forage Harvesters
Miscellaneous Heavy-duty
Miscellaneous Light-duty
EMISSION FACTORS, KG/HR
HC
0.078
0.208
0.300
0.116
0.183
0.122
0.082
0.029
CO
0.154
3.34
6.37
2.83
4.53
0.297
1.73
0.363
NOX
0.429
0.155
0.408
0.068
0.108
0.657
0.112
0.007
PART
0.059
0.009
0.054
0.005
0.008
0.110
0.015
0.001
sox
0.040
0.006
0.034
0.004
0.006
0.067
0.009
0.001
Allowance made for evaporative hydrocarbon emission.
TABLE 23
FARM EQUIPMENT EMISSIONS PER COUNTY2
COUNTY
St. Louis County
C+- Inline Ci f-\/
oL. LUUIb Ul Uy
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
HC
68.3
225
268
75.8
160
180
179
134
175
118
181
EMISSION,
CO
803
2,690
3,190
885
1,900
2,080
2,110
1,600
2,080
1,410
2,180
103 KG/YR
NOX
114
376
448
127
268
305
309
224
296
199
306
PART
13.3
44.6
53.0
15.0
31.7
36.1
37.3
26.5
35.2
23.7
36.6
S0x
8.8
29.4
35.0
9.9
20.9
23.8
24.5
35.0
23.2
15.6
24.1
-28-
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AMC7010.T0108E-CR
TABLE 24
FARM ACREAGE PER COUNTY11
COUNTY
FARM ACREAGE
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
37,542
-0-
213,772
188,815
29,712
12,147
79,490
192,865
111,714
165,034
130,252
212,114
TABLE 25
DATA FOR SAMPLE CALCULATION OF
FARM EQUIPMENT EMISSIONS FROM A GRID ELEMENT
VARIABLE
VALUE
SOURCE
Pollutant
Grid Element
Farm Acreage in Grid
County
County Farm Acreage
County Emissions
CO
1
3172
Franklin (1680)
79,490
2.08 x 106 kg/yr
Specified
Specified
Reference 11
Ref. 11 or Ref. 5
Table 24
Table 23
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AMC7010.T0108E-CR
7.0 OUTBOARD MOTORBOATS
This part of the off-highway inventory included boats powered by outboard
engines and used on the St. Louis AQCR waterways. For the sake of brevity the
boats were termed "outboards". Emission factors for the engines used in the
boating applications were developed from the study of a limited number of test
engines in the laboratory1!
Simulation of outboard engine performance was hindered somewhat by the
complexity of the real-life operating conditions. Engine exhaust outlets are
normally below water, but if the boat is bobbing on the water surface, expecially
if the water is rough, it is possible for some exhaust to be released in sporadic
bursts directly into the atmosphere. While bubbling through water a certain por-
tion of the exhaust pollutants are removed and therefore do not reach the atmos-
phere. The extent of the scrubbing process is highly dependent on water turbu-
lence, and in a more subtle way on the chemical composition of the water itself.
Crude simulation of this bubbling process has been attempted by researchers and
measurements made to determine the extent of pollutant removal. Their test re-
sults played an important role in emission factor development. Direct emission
to the atmosphere of pollutants has not been allowed for in the emission factors
recommended in the Reference 2 methodology and used in this inventory. The emis-
sion factors are presented in Table 26. They represent the best-researched fac-
tors available. Note that the factor for particulates is zero; all particulates
are removed in the water.
To determine emissions from a given area it was necessary to use emission
factors in conjunction with usage and population data. Population data was in
the form of boat registrations. For Missouri, Reference 3 provided separate
figures for motorboats and boat motors per county. These two figures were added
with the assumption that the total would be a reasonable representation of total
outboards per county. This was done for two reasons: First, when motorboats are
sold they invariably come with an engine, thus boat and motor would be registered
as one unit. Since outboards are the most abundant of motorboats, this is a good
partial count of them. Second, although a certain number of outboard engines
registered individually may be sitting idle in storage sheds, perhaps only
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AMC7010.T0108E-CR
infrequently used, there are very likely an equal number of unregistered out-
boards in use during the boating season. Therefore boat motor registration
could very well represent additional outboards, and were added to motorboat
registrations with this in mind.
For Illinois the only registration statistics kept are in terms of "cer-
tified watercraft per county". It was assumed that this number equalled out-
boards per county. Any watercraft which were not outboards (e.g. inboard motor-
boats) would be offset by those outboards which were unregistered. The end
result would be an approximation of the actual number in use. Boat totals for
the twelve AQCR 070 counties are in Table 27.
The remaining factor considered before area emissions could be analyzed
was outboard usage. Those boats registered in a county are not necessarily
used in that county. In fact, many boats registered in the St. Louis AQCR are
not only used outside the counties they were registered in, but outside the
AQCR as well. As a consequence, the calculated emissions are likely to be on
the higher side. Because the majority of Missouri residents use their boats
primarily in Missouri, and Illinois residents in the state of Illinois, it was
decided to first calculate emission totals of the criteria pollutants contributed
by all motorboats registered in all counties within AQCR 070 in each state.
State emissions were calculated by the following relation:
(23) Motorboat Emissions in AQCR by state
= (State Motorboat Registrations in AQCR)
x (Emission Factors, kg/unit yr.)
Using the data in Tables 26 and 27 in (23) yielded the values for state
emissions which comprise Table 28.
The next step was to allocate state emissions to the 12 counties in the
St. Louis Region. Emissions were apportioned according to the amount of nav-
igable water area in each county. This method was chosen because navigable sur-
face waters determined boat usage in a county. Recreational suitability of the
water also plays a role; however no statistics were available on the popularity
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AMC7010.T0108E-CR
of the different waterways. Apportionment to counties was accomplished via the
relation.
(24) County Emissions, kg/yr = AQCR/State Emissions
County Surface Water
x AQCR/State Surface Water
where "AQCR/State Emissions" and "AQCR/State Surface Water" totals were for the
St. Louis AQCR in each state, and "Surface Water" means navigable surface water
area. Outboard emissions per county appear in Table 29 along with the surface
water data used to calculate them.
As the final step, emissions at the grid level were calculated (with the
aid of a computer) using the relation
(25) Grid Element Emissions, kg/yr = County Emissions, kg/yr
Grid Surface Water
County Surface Water
Again it was assumed that boat usage was directly proportional to navigable
water area. To illustrate the calculation, the emission of HC from grid #1019
were calculated. Necessary data are collected in Table 30.
(26) Grid Element Emissions of HC, kg/yr = (2.964 x 106 kg/yr)
x (1 km2)
(90.7 km2)
= 3.26 x 104 kg/yr
Surface water area per grid square was determined by drawing the waterways
onto the grid system and estimating as accurately as possible the percentage
of a grid covered by water. Specific waterways considered to have sufficient
boating activity for inventory purposes were:
a. Mississippi River d. Alton Lake
b. Missouri River e. Carlysle Lake
c. Meramec River f. Lake St. Louis
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AMC7010.T0108E-CR
HC
0.769
TABLE 26
OUTBOARD EMISSION FACTORS (KG/UNIT HR.)
CO
2.28
NOX
.0045
sox
.0044
PART
0
NOTE: Evaporative hydrocarbon emissions have not been measured and are
not reflected by these factors.
HC
53.83
KG PER UNIT-YEAR (ASSUMING 70 HRS/YR OVER OPERATION)
CO
159.6
NOX
.315
sox
.308
TABLE 27
OUTBOARD REGISTRATIONS PER COUNTY
MISSOURI3
COUNTY
St. Louis County
St. Louis City
Jefferson
St. Charles
Franklin
TOTAL
REGISTERED
OUTBOARDS
62,768
16,013
11,607
10,779
5,837
107,004
ILLINOIS18
COUNTY
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
TOTAL
REGISTERED
OUTBOARDS
506
1,166
8,489
685
1,523
7,923
483
20,775
-33-
-------
TABLE 28
STATE OUTBOARD EMISSIONS IN THE AQCR
(KG/YR x 106)
AMC7010.T0108E-CR
Missouri
Illinois
HC
5.759
1.112
CO
17.07
3.315
NOX
.0337
.00654
sox
.0329
.00639
TABLE 29
OUTBOARD EMISSIONS AND NAVIGABLE SURFACE WATER PER COUNTY
COUNTY
St. Louis County
St. Louis City
St. Clair
Madison
Jefferson
St. Charles
Franklin
Clinton
Monroe
Randolph
Bond
Washington
SURFACE WATER2
KM2
45.6
9.8
5.7
32.8
8.8
90.7
21.5
99.5
23.6
31.6
7.8
1.6
EMISSIONS, 103 KG/YR
HC
1,490.0
321.8
31.54
179.2
288.0
2,964.0
703.1
550.5
130.5
174.9
43.0
8.602
CO
4,418
953.8
310.1
594.2
853
8,786
2,083
1,632
386.8
518.6
127.5
25.5
NOX
8.723
1.883
.1846
1.049
1.685
17.34
4.114
3.222
.7634
1.024
.2517
.0503
S°x
8.528
1.841
.1805
1.025
1.648
16.96
4.022 '
3.149
.7464
1.001
.2461
.0492
-34-
-------
AMC7010.T0108E-CR
TABLE 30
DATA FOR SAMPLE CALCULATION OF GRID EMISSIONS
VARIABLE
VALUE
SOURCE
Pollutant
Grid Element
County
County Emissions
County Surface Water
Grid Surface Water
HC
1019
St. Charles (4160)
2.964 x 10C
90.7 km2
1 km2
kg/yr
Specified
Specified
Reference 5
Table 29
Table 29
Map with grid overlay
-35-
-------
AMC7010.T0108E-CR
8.0 TEMPORAL APPORTIONMENT
Annual emission totals of the several off-highway mobile source types
had to be temporally distributed over the year to reflect diurnal and seasonal
variation of usage. To accomplish this end each equipment category was assigned
an annual operating pattern which was felt to most closely approximate real-
life use during a calendar year. The operating patterns assumed were as
follows:
1. Off-highway motorcycles
2. Lawn and garden equipment
3. Construction equipment
4. Industrial equipment
5. Farm equipment
6. Outborad motors
March through October
April through September
March through October
Year round
March through October
April through September
9 AM
9 AM
6 AM
8 AM
5 AM
9 AM
- 7 PM
- 7 PM
- 6 PM
- 6 PM
- 7 PM
- 7 PM
All the days in the month were included, no distinction being made for
weekends. Total yearly operating hours were found by multiplying together
operating hours per day, operating days per month, and operating months per
year. Then the annual emissions total was divided by yearly operating hours
to give emissions per hour.
-36-
-------
AMC7010.T0108E-CR
9.0 SUMMARY
Emissions of criteria pollutants for each of the six types of off-highway
sources have been calculated for each grid square in the St. Louis AQCR. The
methodology has been described, with any departure from the methodology reported
in EPA-450/3-75-002 justified. Most of the data which formed the basis of the
inventory was two years old, and many assumptions on equipment populations and
usage were made where data were not available.
A Fortran program has been prepared in order to compute emissions from the
nearly 2,000 grid squares for each of the six equipment types. Sample calculations
for each category showed that the magnitude of emissions from off-highway mobile
sources is by no means insignificant at the grid element level (see sample in
Figure 1).
-37-
-------
AMC7010.T0108E-CR
M-CYCL OFF HIWAY MOTORCYCLES
LWN&GDN LAWN (& GARDEN EQUIPMENT
FRM EQ FARM [EQUIPMENT
CONSTR CONSTRUCTION EQUIPMENT
IND EQ INDUSTRIAL EQUIPMENT
OUTBD OUTBOARD MOTORS
UNITS KG/YR
3RID POLT
i HC
CO
NOX
PART
SOX
2 HC
CO
NOX
PART
SOX
3 KC
CO
NOX
PART
SOX
4 HC
CO
NOX
PART
2QX
5 :HC
CO
NOX
PART
SOX
6 KC
CO
NOX
FART
sex
7 HC
CO
NOX
PART
sex
3 HC
CO
NOX
PART
SOX
M-CYCL
8.6717+01
1 ..660. 1+02
7.0311-01
1.1718+00
2.5290-01
8.4603+01
1.6J.97+02
6.8597-01
1.143:3+00
2.477.1-01
c
1.3729+02
2.6232+02
1. 1131+00
1.335.2+00
4.0197-01
2.5740+01
4.92.77+01
2.0370-01
3.4733-01
7.5364-02
6.9774+01
1.3353+02
5.6573-01
9.4237-O1
2.0427-01
3.4323+01
6.5718+01
2.7S33-01
4.638
-------
AMC7010.T0108E-CR
REFERENCES
1. Haws, Richard C., & Paddock, Richard E., The Regional Air Pollution
Study (RAPS) Grid System, Research Triangle Institute EPA-450/3-76-021,
Dec. 1975.
2. Hare, Charles T., Methodology for Estimating Emissions From Off-Highway
Mobile Sources for the RAPS Program EPA 450/3-75-002, October 1974.
3. Missouri Department of Revenue Vehicles Per County as of 12/31/75.
4. Illinois Department of Revenue 1974 Motorcycle Population Per County.
5. Clampett, Robert, The Motorcycle Handbook. Fawcett Publications, Inc.
Greenwich, Connecticut - 1975.
6. Richmond, Douglas, Your Trail Bike. H. P. Books, Tuscon, Arizona - 1972.
7. Environmental Science and Engineering, Inc. Residential and Commercial
Area Source Emission Inventory Methodology for the Regional Air Pollution
Study EPA-450/3-75-078 September 1975.
8. Hare, C. T. and K. J. Springer, Exhaust Emissions From Uncontrolled
Vehicles and Related Equipment Using Internal Combustion Engines - Part 4
Small Air-Cooled Spark Ignition Utility Engines, Environmental Protection
Agency Contract EHS 70-108, May 1973.
9. Local Climatological Data, Annual Summary With Comparative Data -
St. Louis, Missouri. U. S. Department of Commerce. National Oceanic
and Atmospheric Administration Environmental Data Service. 1973.
10. Hare, C. T. and K. J. Springer. Exhaust Emissions From Uncontrolled
Vehicles and Related Equipment Using Internal Combustion Engines -
Part 5 Heavy-Duty Farm, Construction, and Industrial Engines.
Environmental Protection Agency Contract EHS 70-108 October 1973.
11. Cowherd, Chatten, and Guenther, Christine. Development of a Methodology
and Emission Inventory For Fugitive Dust For The Regional Air Pollution
Study EPA-450/3-76-006 January 1976.
-39-
-------
AMC7010.T0108E-CR
12. City and County Data Book, 1972.
13. National Emission Data System Point Source Listing. Missouri and
Illinois Printout. EPA 1973.
14. Missouri Emission Inventory Printout 1973.
15. Illinois EPA Emission Inventory 1974.
16. 1969 Census of Agriculture, Volume I - Area Reports. U. S. Department
of Commerce 1972.
17. Hare, C. T. and K. J. Springer. Exhaust Emissions From Uncontrolled
Vehicles and Related Equipment Using Internal Combustion Engines -
Part 2 Outborad Motors. EPA Contract EHS 70-108 January 1973.
18. Illinois Department of Conservation. Total Valid Watercraft Per County
1975.
-40-
-------
EPA-450/3-76-003
AND EMISSION INVENTORY
FOR FUGITIVE DUST
AIR POLLUTION STUDY
hv
Dr. Chullcn Cowherd and Ms. Christine Guenlher
Midwest Research Institute
1-25 Volker Boulevard
Kansas City. Missouri1 (ill 1O
Contract No. 68-02-2040
EPA Project Officer: Charles C. Masser
Prepared for
EiNVIROMEiNTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 277 1 I
January 1976
-------
This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees, current contractors and
grantees, and nonprofit organizations - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711; or, for a fee,
from the National Technical Information Service, 5285 Port Ro:yal Road,
Springfield, Virginia 22161.
This report was furnished to the Environmental Protection Agency by
Midwest Research Institute, Kansas City, Missouri 64110, in fulfillment
of Contract No. 68-02-2040. The contents of this report are reproduced
herein as received from Midwest Research Institute. The opinions,
findings , and conclusions expressed are those of the author and not
necessarily those of the Environmental Protection Agency. Mention of
company or product names is not to be considered as an endorsement
by the Environmental Protection Agency.
Publication No. EPA-450/3-76-003
-------
ACKNOWLEDGEMENTS
This report was prepared for the Environmental Protection Agency's
Office of Air Quality Planning and Standards under EPA Contract No. 68-
02-2040. Mr. Charles Masser served as EPA Project Officer.
The program was conducted in MRIfs Physical Sciences Division under
the supervision of Dr. Larry J. Shannon, Assistant Director. Dr. Chatten
Cowherd, Jr., Project Leader for MRI, was assisted by Ms. Christine
Guenther, Mr. Daniel Nelson, and Mr. Kenneth Walker.
Approved for:
MIDWEST RESEARCH INSTITUTE
. ^&*-6-*-^
-------
CONTENTS
Page
List of Figures vii
List of Tables ix
Summary 1
Introduction 3
Technical Approach 5
Unpaved Roads 9
Grid Source Extent 9
Emission Factor 12
Temporal Apportioning Factors 12
Agricultural Tilling 15
Grid Source Extent 15
Emission Factor 18
Temporal Apportioning Factors 19
Wind Erosion From Tilled Land 23
Grid Source Extent 23
Emission Factor 23
Temporal Apportioning Factors 26
Construction 29
Grid Source Extent 29
Emission Factor 32
Temporal Apportioning Factors 32
-------
CONTENTS (concluded)
Paee
Aggregate Storage 35
Grid Source Extent , 35
Emission Factor 37
Temporal Apportioning Factors 37
Unpaved Airstrips. 41
Grid Source Extent 41
Emission Factor 44
Temporal Apportioning Factors 44
Data Tabulations and Calculated Results 47
Analysis of Results and Estimated Accuracies 53
References 57
Appendix A - Example Calculations (RAPS Grid No. 1) 61
Appendix B - Factors Affecting Atmospheric Transport of Fugitive
Dust . . 65
vi
-------
FIGURES
[0..
1
2
3
4
5
6
7
Title
Procedure for Determination of Annual Vehicle-Miles on Un-
Percentage of Total Daily, Weekly, and Annual Vehicle-Miles
Procedure for Determination of Annual Acres of Land Tilled .
Percentage of Total Daily, Weekly, and Annual Agricultural
Tilline
Page
6
7
10
13
16
20
21
8 Procedure for Determination of Acreage of Exposed Agri-
cultural Land 24
9 Percentage of Total Daily, Weekly, and Annual Wind Erosion
from Agricultural Tilled Land 27
10 Procedure for Determination of Annual Acres of Construc-
tion 30
11 Percentage of Total Daily, Weekly, and Annual Construction
Activity 33
12 Procedure for Determination of Annual Tons of Aggregate
Storage 36
vii
-------
FIGURES (concluded)
No. Title Paee
13 Percentage of Total Daily, Weekly, and Annual Aggregate
Storage Operations . 39
14 Procedure for Determination of Annual LTD Cycles on Un-
paved Airstrips 42
15 Percentage of Total Daily, Weekly, and Annual LTO Cycles. . . 45
16 Simplified Flow Diagram of Calculation Procedure for Annual
Emissions by Grid 50
17. Simplified Flow Diagram of Calculation Procedure for Hourly
Emissions by Grid 51
18 Example Computer Output of Annual Emissions by Grid ..... 52
B-l Roughness Heights for Various Surfaces. ... 68
g-2 Relationship Between Particle Size and Drift Distance .... 72
viii
-------
TABLES
No. Title Page
1 County Statistics for Unpaved Roads 11
2 County Breakdown of Harvested Acres by Crop and Equivalent
Tillings 17
3 Agricultural Operations by Crop 18
4 Seasonal Exposed Acreage by County ...... 25
5 Construction Acreage by County 31
6 Annual Acres of Aggregate Stored by County .... 38
7 Data on Unpaved (Turf) Airstrips by County 43
8 Example Coded Source Extent and Correction Factor Data ... 48
9 Hourly Adjustment Example Coded Factors. .... 49
10 Summary of Annual Emissions by County 54
11 Estimated Errors for Tabulated Data 55
B-l Particle Drift Distances Calculated from Eq. (9b) 71
B-2 Distances to Point of Maximum Settling, xmax , Calculated
from Eq. (12) 74
ix
-------
SUMMARY
This report outlines the methodology that was used in developing
an hourly fugitive dust emissions inventory for the Metropolitan
St. Louis Air Quality Control Region as part of the Regional Air Pol-
lution Study (RAPS). The inventory encompassed the following source
categories: (a) unpaved roads, (b) agricultural land tilling, (c) wind
erosion of agricultural land, (d) construction sites, (e) aggregate
storage piles, and (f) unpaved airstrips.
For each of approximately 2,000 RAPS grid areas, data were compiled
on annual emissions of fugitive dust. This required, in addition to basic
emission factors adjusted for local climatic and surface conditions, an-
nual measures of source extent (vehicle-miles traveled on unpaved roads,
acres of land tilled, etc.) for each grid area. Finally, hourly apportioning
factors were derived to account for emissions variations by hour of the
day, day of the week, and season of the year.
Results presented in this report include temporal apportioning fac-
tors, county totals of annual source extent and annual emissions for
each source category. Fine particle emissions from fugitive dust sources
in the St. Louis area are found to comprise 39% of the total emissions
of suspended particulates.
-------
INTRODUCTION
Analysis of the physical relationships between air pollutant source
emissions and ambient air quality is essential to the rational develop-
ment and implementation of pollution abatement and control strategies.
These relationships are predictable through the use of mathematical mod-
els which simulate the processes of atmospheric transport, dispersion,
transformation, and removal of pollutant emissions.
The Environmental Protection Agency (EPA) is currently sponsoring
a comprehensive regional air pollution study (RAPS) in the St. Louis
Air Quality Control Region (AQCR 70). The primary purpose of the RAPS
program is the development and validation of improved air quality mod-
els. To accomplish this purpose, a major portion of the program effort
is being directed to the preparation of a comprehensive regional data
base.
Inputs required for model verification include an emissions in-
ventory, meteorological data (wind velocity and temperature) and air
quality data. The spatial and temporal resolution of the RAPS data base
will be far more precise than any previously compiled in an undertaking
of this type. This will permit verification of sophisticated models
which predict air quality distributions on a short term (hourly) basis.
Recently it has become evident that fugitive dust sources contri-
bute substantially to atmospheric concentrations of total suspended
particulates (TSP) in both urban and rural areas. Failure to incorporate
fugitive source emissions into model-based control strategies has re-
sulted in widespread overestimation of TSP reductions resulting from
the control of conventional point and area sources. Therefore, the need
to include fugitive dust sources in the RAPS emissions inventory is
evident.
This report presents the results of an investigative program di-
rected to (a) development of a methodology for reporting fugitive dust
-------
emissions in the RAPS region and (b) compilation of an hourly emissions
inventory of fugitive dust sources for the nearly 2,000 RAPS grid areas.
The following six categories of fugitive dust sources were addressed
in this study:
1. Unpaved roads;
2. Agricultural land tilling;
3. Wind erosion of agricultural land;
4. Construction sites;
5. Aggregate storage piles; and
6. Unpaved airstrips.
Appendix B presents an assessment of factors affecting atmospheric
transport of fugitive dust.
-------
TECHNICAL APPROACH
Figure 1 traces the methodology that was developed to compile hourly
emissions of fugitive dust by grid. The key data elements in this scheme
are:
1. Appropriate annual measures of the extent of each source type
within each grid area.
2. Emission factors adjusted to climatic conditions and surface
properties characteristic of the St. Louis area.
3. Temporal apportioning factors to account for emissions varia-
tions by hour of the day, day of the week, and season of the
year.
The basic emission factors and associated correction terms used in this
study, as shown in Figure 1, were developed by Midwest Research Institute
(MRI) under EPA Contract No. 68-02-0619.!/ These factors refer to dust
particles smaller than 30 |Jm in diameter, the approximate effective cut-
off diameter of a standard high-volume particulate sampler (based on a
particle density of 2 to 2.5 g/cnP).
The initial work objective was to prepare a base map of the RAPS
grid system which incorporated county outlines and river outlines.
United States Geological Survey (USGS) maps with a scale of 1:250,000
were used to locate the RAPS grid system based on Universal Transverse
Mercator (UTM) coordinates designated for Zone 15.
A reduction of the resulting overlay map is shown in Figure 2. The
overlay was photographically scaled to fit appropriate land use and
street maps of the St. Louis area. A computer-generated plot of the grid
system, supplied by the EPA project officer, was also reduced to the size
of the MRI overlay for comparative purposes.
The following sections of this report document, for each source
category, the methodology used to obtain annual grid source extent, cor-
rected emission factors, and temporal apportioning factors. Also pre-
sented are key computational results summarized by county, including
extent of fugitive dust sources, temporal apportioning factors, and
annual totals of fugitive dust emissions.
5
-------
For Each County
Annual for Each County
DATA
Miles of Unpaved Roads
Acres of Harvested Cropland
Construction Projects
NEDS Aggregate Listing
Number of Based Aircraft
COUNTY SOURCE EXTENT
Unpaved Roads (vehicle miles).
Land Tilling (acres)
Wind Erosion (acres)
Construction (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO cycles)
For Each Grid
Annual for Each Grid
SPATIAL APPORTIONING
FACTORS
Land Use
Grid Area
GRID SOURCE EXTENT
Unpaved Roads (vehicle miles)
Land Tilling (acres)
Wind Erosion (acres)
Construction (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO cycles)
Annual for Each Grid
CORRECTION FACTORS
Number of Dry Days per Year (d)
Precipitation-Evaporation Index (PE)
Duration of Construction Activity (D^
Silt Content - Roads, Gravel (sg)
- Roads, Dirt (sj)
- Tilling (sf)
- Airstrips (sa)
Vehicle Speed - Roads (Sr)
- Airstrips (Sa)
Calculate : EMISSIONS
(tons/yr, Mtons/yr)
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
Hourly for Each Grid \
TEMPORAL APPORTIONING
FACTORS
Critical Wind Speed
Activity
-Work Cycle
-Traffic Cycle
Compute : EMISSIONS
(Ib/hr, kg/hr)
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
Unpaved Roads
- Gravel
Compute: EMISSION FACTORS
EF°=°-4"°&)(^)
\~30~
Ib
- Dirt
Land Tilling
EFd=0.49sdf^_\/_d_
'"A365,
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
EF»= 1.1st
(PE/50)Z
EFW = 0.9
EFC = D
EF, = 0.33
0.49sa/Sa_
\30
365
vehicle mile
Ib
vehicle mile
Ib/acre
tons/acre
tons/acre
Ib/tons stored
t
Ib
LTO cycle
Figure 1. Project data flow diagram
-------
Figure 2. Example of RAPS grid system overlay
-------
UNPAVED ROADS
GRID SOURCE EXTENT
The measure of source extent for fugitive dust emissions from un-
paved roads is vehicle miles traveled (VMT). The basic equation for cal-
culation of annual VMT on unpaved roads in a specified grid area is given
by:
4
VMT = 365 £ (ADT )m.
i = 1 i x
where ADT^ is average daily traffic on unpaved roads with surface type
i , and m. is the mileage of unpaved roads with surface type i within
the grid area. Road surface types considered in this study were: (a)
gravel/stone surfaced, (b) soil surfaced, (c) graded and drained, and
(d) unimproved. The procedure used to determine ADT. and m^ for each
grid is depicted in Figure 3.
Traffic volume on unpaved roads within each grid was derived from
appropriate county maps. Traffic flow and road surface-type maps were
obtained from the Illinois Department of Transportation^' for each of
the seven Illinois counties in the St. Louis rAQCR. Highway maps, desig-
nating road surface type, were obtained from the Missouri State Highway
Commission^/ for the counties of Franklin, Jefferson, St. Charles, and
St. Louis in Missouri. Communications with officials of St. Louis City
and County-t' indicated that there are no unpaved roads in the city and
only a few municipal or private unpaved roads in the St. Louis County.
The RAPS grid system was scaled to each county map, and mileage
and average ADT for each of the four road surface types were manually
obtained for each grid. Table 1 presents a county summary of the mile-
age and ADT for each road type. As indicated, values for ADT on unpaved
roads in the Missouri counties were estimated based on reported ADT val-
ues for Illinois roads differentiated by road surface type.
-------
For Illinois Counties:
By County
Map of Roads
by Surface Type
By County
Map of ADT
on Each Road
By Grid
Miles of Road
Gravel or Stone
Soil Surfaced
Graded & Drained
Unimproved
By Grid
Daily Vehicle
Miles on Unpaved
Roads
By Grid
Annual Vehicle
Miles on
Unpaved Roads
For Missouri Counties:
By County
Map of Roads
by Surface Type
By Road Type
Average ADT
(Based on
7-County
Illinois Data)
By Grid
Miles of Road
Gravel or Stone
Soil Surfaced
Graded & Drained
Unimproved
By Grid
Annual Vehicle
Miles on
Unpaved Roads
Figure 3. Procedure for determination of annual vehicle-miles on unpaved roads
-------
Table I. COUNT* STATISTICS FOR UNPAVED ROADS
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
V/ashlngton
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
Mao
Road
surface
1972
1972
1973
1973
1973
1973
1971
1973
1975
1971
1969
date
Traffic
1969
1968
1971
1973
1973
1972
1973
..
Gravel/
stone
351.1
504.7
20.0
234.2
284.5
209.0
339.5
606.1
260.7
282.7
0.0
0.0
Soil
surfaced
39.5
11.0
285.0
0.0
13.7
42.2
24.0
0.0
0.0
0.0
0.0
0.0
Mileaee
Graded and
drained
41.7
36.0
11.0
32.5
42.2
15.0
107.5
1.2
0.0
0.0
0.0
0.0
Unimproved
1.5
1.5
0.2
1.7
9.0
0.2
3.5
3.0
0.0
0.0
0.0
0.0
Total
433.8
553.2
316.2
268.4
449.5
266.5
474.5
610.3
260.7
282.7
0.0
0.0
Gravel/
stone
81
83
92
64
65
--
73
71*'
"-
71*'
..
Soil
surfaced
57
58
63
280
~
85
73*'
"*i
7Ja/
"
ADT
Graded and
drained
51
72
50
64
50
--
51
&'
53*
53*'
-.
"
Unimproved
25
25
25
25
25
25
25
25
25
25
..
"
Annual VMT
(thousands)
Gravel
10,321
15,226
675
5,548
9,429
13,500
9,079
16,563
6,882
6,967
0
0
Dirt
1,607
1,361
6,736
591
2,304
2,284
2,821
33
0
190
0
0
ace type.
-------
EMISSION FACTOR
The emission factor for dust emissions from unpaved roads (pounds
per VMT) is given by:
EF_ =
where sr = silt content of road surface material, gravel (sg) and dirt
(sjj) (percent), i.e., particles smaller than 75 Urn in diameter, S ==
average vehicle speed (miles per hour), and d = number of dry days per
year, i.e., days with less than 0.01 in. of precipitation. Based on driver
interviews, the average vehicle speed on unpaved roads in the St. Louis
area was taken to be 30 mph. On the average, there are 250 dry days per
year in the RAPS study region.^.'
£/
The silt content of gravel roads was estimated to be 16%, and the
silt content of dirt roads (i.e., soil-surfaced, graded and drained, and
unimproved) was assumed to be the same as the soil silt content deter-
mined for agricultural sources (see Section Agricultural Tilling, Emis-
sion Factors). Composite road silt content by grid was found to vary from
10 to 70% with corresponding emission factors ranging from 3.36 to 23.5
lb/vehicle mile.
TEMPORAL APPORTIONING FACTORS
Little data are available describing temporal variations in traffic
on unpaved roads. Figure 4 illustrates hourly, daily, and seasonal varia-
tions of VMT on unpaved roads for a farming area in California.^.' These
data were assumed to approximate temporal variations in the St. Louis
area.
12
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Unpaved Roads
8
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Mon Tue Wed Thu Fri
Day of Week
Sat
Sun
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Fall
Figure 4. Percentage of total daily, weekly, and annual
vehicle-miles on unpaved roads
13
-------
AGRICULTURAL TILLING
GRID SOURCE EXTENT
Dust emissions from agricultural tilling can be quantified in terms
of annual acres of cropland tilled. Data used for this determination (see
Figure 5) were:
1. Acreage of harvested cropland by grid, for five major crops (corn,
soybeans, wheat, milo and hay).
2. Number of yearly agricultural operations by crop, including til-
ling, planting, and harvesting.
The acres of harvested cropland for all farms on a county basis,
as presented in Table 2, were obtained from the 1969 Census of Agricul-
ture.' The number of yearly agricultural operations for the five major
crops (see Table 3) were estimated by knowledgeable MRI personnel. This
information was used to determine the equivalent acres of land tilled
per year by county, based on the following equation:
Equivalent acres 5
of land tilled _V
annually by £>
county
Number of equiv-
alent tilling
operations by
crop, i
Acres of har-~
vested crop-
land, by crop
i,
by county
Planting and harvesting operations were estimated to have half of the fugi-
tive dust potential of tilling, based on visual observations made by MRI
personnel.
Annual acreage of land tilled by grid was determined by spatial ap-
portioning of county totals on the basis of grid area and land use, ac-
cording to the following equation:
Annual acres of
land tilled by =
grid
Annual acres of
land tilled by
county
Agricultural acreage
within grid
Fraction of
grid in county
Agricultural acreage within county
15
-------
By County
Area of Each
Grid (Acres)
% of Grid
in County
By County
Acres in
Each Grid
By Grid
1970 Land
Use Maps
% Agricultural
Land in Grid
By Crop
Number of
Agricultural
Operations
per Year
Tilling
Planting
Harvesting
By County
1969 Acres
of Each Crop
Harvested
By County
By Grid
Figure 5. Procedure for determination of annual acres of land tilled
-------
Table 2. COUNTY BREAKDOWN OF HARVESTED ACRES BY CROP AND EQUIVALENT TILLINGS
State
Illinois
Missouri
a/
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
Harvested
cropland
(acres)
122,755
170,718
224,634
114,343
148,136
214,750
204,371
74,974
27,506
108,909
35,460
b/
Percentage of acres by croi>
Corn, (5)
32.9
39.7
32.0
36.9
32.9
31.5
28.3
32.7
18.0
37.9
29.1
Wheat (4)
16.3
16.7
18.1
25.3
19.4
20.9
20.5
17.4
13.8
22.8
29.1
Soybeans (4'
39.6
29.1
38.7
27.1
29.6
38.6
38.1
3.9
7.0
25.5
22.2
) Mi,lo (5)
0.4
0.4
0.9
0.1
0.1
0.2
0.5
0.9
1.8
0.2
0.5
Hav (2.5)
8.6
12.0
8.0
6.9
13.4
5.0
9.4
39.8
52.6
10.7
10.5
Other (0)
2.2
2.1
2.3
3.7
4.6
3.8
3.2
5.3
6.8
2.9
8.6
Equivalent
tillings
oer vear
4.1
4.1
4.1
4.1
4.0
4.1
4.0
3.5
3.1
4.1
3.8
.a,/ St. Louis City not included; harvested cropland (acres) = 0.
J}/ Numbers in parentheses are equivalent tillings per year for each crop.
-------
Table 3. AGRICULTURAL OPERATIONS BY CROP
Number of equivalent tillings per year'
Crop
Corn
Wheat
Soybeans
Milo
Hay
Primary
tilling
1 (F)
1 (Su)
1 (W, Sp)
1 (F, W,
Sp)
1/2 (Su)
Secondary
tilling
3
2
2
3
1
(Sp)
(Su, F)
(Sp)
(Sp)
(Su, F)
Planting
1/2 (Sp)
1/2
1/2
1/2
1/2
(F)
(Sp)
(Sp)
(F)
Harvesting
1/2 (F)
1/2
1/2
1/2
1/2
(Su)
(F)
(F)
(Su)
Total
5
4
4
5
2.5
a./ Season of operation is abbreviated by W = winter, Sp = spring,
Su = summer, and F = fall.
Agricultural acreage within each grid and within each county was
determined by analysis of land use maps supplied by the East-West Gateway
Coordinating Council."»*u/ The area of a grid lying within a particular
county was determined from the base map of the RAPS grid system (Figure
2). Results for grids which cross county lines were summed.
EMISSION FACTOR
The emission factor for dust emissions from agricultural tilling
operations (pounds per acre tilled) is given by:
EFt = 1.1
(PE/50)'
where st = silt content of soil (percent), i.e., particles between 2
and 50 |Hn in diameter, and PE = Thornthwaite's Precipitation-Evaporation
IndexJJ/
Soil silt content for each grid was determined from an analysis of
soils maps, obtained from Soil Conservation Service offices for the coun-
ties of Bond, Clinton, Madison, St. Glair, and Washington in Illinois!2/
18
-------
and St. Charles County in Missouri.' A map of the soils of the North
Central United Statesl^/ was used for the remaining counties and to pro-
vide data comparisons.
The soil classification system for each map was converted to soil
families (the second most specific classification of soils, indicating
the soil texture), and a soil texture triangleJLl/ was used to estimate
silt content for each family designation. Areas of uniform soil family
were superimposed on a grid map (see Figure 6) and appropriate silt con-
tent values were assigned to each grid.
A map of the PE-index by state climatic division, generated in an
earlier MRI study,!/ indicates a PE-index of 93 for both state climatic
divisions which comprise the Metropolitan St. Louis AQCR.
TEMPORAL APPORTIONING FACTORS
Agricultural land tilling, planting, and harvesting follow a regu-
lar yearly cycle dependent on the type of crop. Within these yearly cy-
cles, agricultural operations are performed mainly during the hours from
dawn to dusk and uniformly through the week, with only a slight reduction
on Sundays. The temporal apportioning factors derived for agricultural
operations are shown in Figure 7.
Based on seasonal performance of primary and secondary tilling, plant-
ing, cultivation, and harvesting for the main crops in the St. Louis AQCR,
as determined by MRI personnel (see Table 3), seasonal apportioning factors
were determined for each county, taking into account the respective crop
mixes. Separate average seasonal factors were calculated for Missouri and
Illinois to reflect wide differences in types of crops in the two states.
19
-------
i WASHINGTON
: 70
Litl±L
RANDOLPH "^
Figure 6. Soil silt content (%) for RAPS Grid system
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Agricultural Tilling
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
55
50
45
40
35
30
25
20
15
10
5
^^^
Mon Tue Wed Thu Fri Sat Sun
Day of Week
A
/
//
//
//
y
f
/\
/ \
/ s^*- \
/ / \ v
/ / "^
/
\
^
\\..-*""
\ -^^**
^^
^^
^^^
Winter Spring Summer Fall
Illinois
~ Missouri
Season of Year
Figure 7. Percentage of total daily, weekly, and annual
agricultural tilling
21
-------
WIND EROSION FROM TILLED LAND
GRID SOURCE EXTENT
The measure of source extent for wind erosion from tilled agricul-
tural land is average exposed (unvegetated) acreage. Agricultural land
is assumed to remain vulnerable to wind erosion from the time of primary
tilling to about 1 month after planting. The procedure used to determine
average area of erodible agricultural land within each grid is depicted
in Figure 8.
Annual average exposed acreage for each county was determined from
seasonal values (see Table 4) which were calculated from the acreage
planted in each crop and the corresponding months of exposure. Erodible
acreage for each grid was determined by apportioning county totals on
the basis of the proportion of county agricultural acreage which lies
within the grid.
EMISSION FACTOR
An emission factor for wind erosion from agriculturally tilled land
was derived from data on atmospheric loadings of suspended dust measured
by Gillette^' during dust storms in West Texas. The threshold rate of
wind erosion was adjusted to apply to values of soil silt content and
climatic factor which are representative of the St. Louis area.
The threshold value for the St. Louis area was calculated to be:
3.5 tons/acre/year. Based on meteorological data for 3-hr time incre-
ments, winds in the St. Louis region exceed 12 mph approximately 26% of
the time.-=-£' Thus, the annual average emission factor for wind erosion
becomes:
3.5 tons/acre x 0.26 = 0.9 tons/acre
23
-------
By County
Area of Each
Grid (Acres)
% of Grid
in County
By County
Acres in
Each Grid
By Grid
1970 Land
Use Maps
% Agricultural
Land in Grid
By Crop
Time Period
of Crop
Tillings
By County
1969 Acres
of Each Crop
Harvested
By County
By County
Acres of
Agricultural
Land in
Each Grid
By Grid
Annual
Exposed Acres
of Agricultural
Tilled Land
t
'
Annual
Exposed Acres
of Agricultural
Tilled Land
Figure 8. Procedure for determination of acreage of
exposed agricultural land
24
-------
Table 4. SEASONAL EXPOSED ACREAGE BY COUNTY
Ul
State
Illinois
Missouri
a/
County
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson
St. Charles
St. Louis
Average acres exposed
Winter
58,292
86,609
104,702
53,762
65,426
97,786
86,719
26,462
6,166
51,505
13,384
Spring
77,427
97,376
138,675
60,500
78,736
131,115
119,832
19,943
5,626
56,288
15,168
Summer
18,474
27,109
37,021
25,744
27,379
39,798
38,340
15,061
5,154
22,584
9,269
Fall
42,190
67,420
79,008
49,070
54,667
77,749
69,983
27,822
7,216
45,657
14,402
Average
49,096
69,628
89,851
47,269
56,552
86,612
78,718
22,322
6,040
44,008
13,055
.a/ St. Louis City not included; harvested cropland (acres) = 0.
-------
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors for wind erosion are shown in Figure
9. Seasonal apportioning factors were scaled to the product of (a) sea-
sonal values of exposed acreage by state and (b) the seasonal climatic
factoci^' for the St. Louis AQCR. Hourly factors were proportioned to
the probabilities that the wind speed will exceed 12 mph, the threshold
value for the onset of wind erosion.
26
-------
TEMPORAL APPORTIONINQ FACTORS
Source Type: Wind Erosion from Tilled Land
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Mon Tue Wed Thu Fri
Day of Week
Sat
Sun
Winter
Spring Summer
Season of Year
Fall
Figure 9. Percentage of total daily, weekly, and annual wind erosion
from agricultural tilled land
27
-------
CONSTRUCTION
GRID SOURCE EXTENT
Fugitive dust emissions from construction activities are directly
related to the land area being worked, over a specific time period. Fig-
ure 10 presents the methodology used to determine annual acres of con-
struction within each grid area. Construction activity considered in this
study was confined to the Source Industrial Classification (SIC) Major
Group 15 (Building ConstructionGeneral Contractors and Operative
Builders) and Group 16 (Construction Other than Building Construction-
General Contractors).
Detailed 1974 data for major building construction sites in the
Missouri counties except Franklin were obtained from the East-West
Gateway Coordinating Council.-^' These data included: county, location,
census tract, description of activity, project name, size in acres (or
square feet), and stage of development. All sites were located by grid
and construction acreage was totaled by county. It was evident that the
building construction centered around St. Louis County.
A detailed listing of road construction projects in the St. Louis
area was also obtained from the East-West Gateway Coordinating Council.
For the Missouri counties except Franklin, road construction projects
differentiated by type and mileage were assigned to the proper grid areas.
Estimates of contruction acreage per mile of road construction, for each
type of project, were used to convert mileage to acreage within each grid.
Road construction acreage totals for St. Charles and St. Louis Counties,
which amounted to less than 10% of building construction acreage, were
disregarded.
Table 5 gives construction acreage by county. Construction acreage
totals for Jefferson, St. Charles and St. Louis counties are slightly
larger than the estimates reported earlier by MRI, which were based
on state construction receipts^*.' and county construction employment;'
this is apparently due to increased area development. However, the
St. Louis City construction acreage was smaller than the previously reported
29
-------
CONSTRUCTION
GRID SOURCE EXTENT
Fugitive dust emissions from construction activities are directly
related to the land area being worked, over a specific time period. Fig-
ure 10 presents the methodology used to determine annual acres of con-
struction within each grid area. Construction activity considered in this
study was confined to the Source Industrial Classification (SIC) Major
Group 15 (Building ConstructionGeneral Contractors and Operative
Builders) and Group 16 (Construction Other than Building Construction
General Contractors).
Detailed 1974 data for major building construction sites in the
Missouri counties except Franklin were obtained from the East-West
Gateway Coordinating Council.' These data included: county, location,
census tract, description of activity, project name, size in acres (or
square feet), and stage of development. All sites were located by grid
and construction acreage was totaled by county. It was evident that the
building construction centered around St. Louis County.
A detailed listing of road construction projects in the St. Louis .
area was also obtained from the East-West Gateway Coordinating Council.
For the Missouri counties except Franklin, road construction projects
differentiated by type and mileage were assigned to the proper grid areas.
Estimates of contruction acreage per mile of road construction, for each
type of project, were used to convert mileage to acreage within each grid.
Road construction acreage totals for St. Charles and St. Louis Counties,
which amounted to less than 10% of building construction acreage, were
disregarded.
Table 5 gives construction acreage by county. Construction acreage
totals for Jefferson, St. Charles and St. Louis counties are slightly
larger than the estimates reported earlier by MRI, which were based
on state construction receipts,^' and county construction employment;'
this is apparently due to increased area development. However, the
St. Louis City construction acreage was smaller than the previously reported
29
-------
For Missouri Counties (except Franklin)
1974 Construction
Projects
Residential
Commercial
Highways
By Project
Acres of
Construction
By Project
Grid Location
of Construction
Projects
By Grid
Annual Acres
of Construction
For Illinois Counties and Franklin County, Missouri
By County
1972 Acres
of Construction
By County
Land Area
By Grid
Land Area
By Grid
Annual Acres
of Construction
Figure 10. Procedure for determination of annual acres of construction
30
-------
Table 5. CONSTRUCTION ACREAGE BY COUNTY
Construction acreage
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis City
St. Louis
Building
989
1,088
234
4,999
Road
204
a/
62
a/
Total
143
254
1,640
151
333
1,760
87
435
1,193
1,088
296
4,999
a/ Road construction acres less than 10% of total.
31
-------
value, which was based on the assumption that construction employees
residing in the city worked only within the city.
For the remaining counties, i.e., Franklin County in Missouri and
all of the Illinois counties, MRI estimates of total construction acre-
age-2/ (buildings plus roads) were apportioned to grids within a county
on the basis of grid area.
EMISSION FACTOR
County-wide emission factors for dust emissions from construction
activities were determined by multiplying a previously determined emis-
sion rate factor (1 ton/acre/month)i' by an average duration of construc-
tion within the county, weighted by the relative proportion of acreage
differentiated by project type and the average duration for each project
type. MRI estimates of the average duration of constructions' are:
6 months for residential buildings,
11 months for nonresidential buildings, and
18 months for nonbuilding construction.
The emission factor for construction can thus be written as follows:
EFC = D tons/acre
where D = weighted average duration of construction within a given
county.
The value of D for St. Louis City and the Missouri counties of
Jefferson, St. Charles, and St. Louis was determined to be 9.1 months,
and the value for the remaining counties was estimated to be equal to
12 months.&J
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors for determining construction emissions
by hour of the day, day of the week, and season of the year were derived
from analysis of the work cycle of construction activity (see Figure 11).
Construction activity reaches its peak level during June and July and
is lowest during December through February. Weekday activity is relatively
uniform with some reduction on weekends. The hourly factor distribution
has mid-morning and mid-afternoon peaks.
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Construction
9
8
7
6
5
4
3
2
1
(
J
I
/
r
J
f
/
/
/
/
/
\
\
/
/
\
W
V
V
\
V
\
\
s
s
s
\
^x
>s
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Man Tue Wed Thu Fri
Day of Week
Sat
Sun
35
30
25
20
15
10
5
I
"^
^-
^^^~^~*^
~* ^.^
Winter Spring Summer Fall
Season of Year
"igure 11. Percentage of total daily, weekly, and
annual construction activity
33
-------
AGGREGATE STORAGE
GRID SOURCE EXTENT
The amount of fugitive dust emissions from aggregate storage piles
is proportional to the quantity of aggregate stored^i' i.e., the tonnage
put through the storage cycle. Figure 12 illustrates the methodology for
determining the quantity of aggregate stored annually within each grid.
The following Source Classification Codes of the National Emissions
Data System (NEDS) were identified as industrial producers and users of
mineral aggregate:
SCC ID
I II IH 1Y
3 05 All All
23/
A NEDS point source listing (August 25, 1975)' for the above codes was
obtained for the St. Louis AQCR.
Aggregate storage data from the NEDS listing were analyzed and the
grid numbers for aggregate user and producer industries were determined
from the respective UTM coordinates. Only industries with open aggregate
storage were considered in this study. Producers are stone quarries and
sand/gravel processors, and users are cement manufacturing (wet and dry),
and concrete batching. Asphalt batching plants in the St. Louis area nor-
mally store aggregate in enclosed areas.
The methodology employed to determine the amount of aggregate mate-
rial stored on-site by a producer or user industry and the average period
of storage is presented below.
Stone quarries - The amount of aggregate material stored annually
is specified in the NEDS output. An estimated 3-month storage period is
assumed from previous experience with the stone quarry industry.
35
-------
CJ
1975 NEDS
Listing of SCC
Codes 03-05-
( Industrial
Process -
Mineral Products)
Contacts with
Industries
By County By Grid
Aggregate Storage
Users
Producers
Methodology
for Determining
Annual Tons of
Material Stored
Aggregate Storage
^_ I l-nnr
Producers
E
iy Grid i
Annual Tons
of Aggregate
Storage
Figure 12. Procedure for determination of annual tons of aggregate storage
-------
Sand and gravel - The amount of aggregate material stored in an an-
nual period is taken to be 50% of the tonnage processed. An estimated
3-raonth inventory period is assumed from previous experience with the
sand and gravel industry.
Cement manufacturing - The following equation for calculating the
amount of aggregate stored by this user industry was determined from tele-
phone contacts with area plants and a literature survey:
, tons aggregate
Aggregate stored = Cement produced x 1.2 "
5& B ^ tons cement
(tons) (tons)
The NEDS output designates tons of cement produced from wet and dry pro-
cess facilities. On the average, aggregate material used in cement manu-
facturing is stored for 1 week.
Concrete batching - Cubic yards of concrete produced by each batch-
ing plant is specified in the NEDS listing. Based on contacts with this
user industry, the following conversion factors were obtained: (a) 1
cu yd of concrete is equivalent to 2 tons, and (b) approximately 75% of
each ton of concrete produced is comprised of aggregate material taken
from open storage. The average aggregate storage period for this user
industry is 1 week.
Table 6 summarizes by county the quantity of aggregate stored an-
nually for each of the above user and producer industries.
EMISSION FACTOR
The emission factor for dust emissions from aggregate placed in open
storage for a period of 3 months is:
EFS = 0.33 Ib/ton placed in storage
which includes emission contributions from wind erosion (33%), movement
of traffic among the storage piles (40%), and loading and unloading op-
erations (27%).' The corresponding emission factor for a 1-week storage
cycle is 0.22 Ib/ton placed in storage.
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors (see Figure 13) were determined sep-
arately for the emission contributions from storage pile activity and
from wind erosion. The factors for storage pile activity were derived
on the basis of the information from industrial personnel and NEDS data.
37
-------
Table 6. ANNUAL ACRES OF AGGREGATE STORED BY COUNTY
oo
Aggregate storage (tons/year)
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson .
St. Charles
St. Louis City
St. Louis
Sand/
gravel
0
0
0
0
0
0
0
0
12,950
t o
' 0
189,500
Stone
quarry
0
20,000
74,000
46,800
275,000
1,900,000
100,000
0
8,000
220,000
0
64,000
Cement
manufacturing
0
0
0
0
0
0
0
0
1,232,700
0
0
1,709,600
Concrete
batching
0
11,300
0
0
0
0
0
37,950
54,000
125,550
0
0
Total
0
31,300
74,000
46,800
275,000
1,900,000
100,000
37,950
1,307,650
345,550
0
1,163,100
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Aggregate Storage
5
A
i
i
it
r\
f
'
i
a
»^
^-i
> '
»
*
-«.
-.
.,\'
k
V
V
" ^
^
\
m
_^
01 2345678 91011121314151617181920212223
Hour of Day
Wind
Erosion
^ Activity
Weighted
Average
on
zU
15
in
IU
c
"^_-
* «.
Wind
Erosion
Activity
Weighted
Average
Mon Tue Wed Thu Fri Sat Sun
Day of Week
35
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Fall
Climatic
Factor
Activity
Weighted
Average
Figure 13. Percentage of total daily, weekly, and annual
aggregate storage operations
39
-------
For aggregate producers (stone quarries and sand/gravel processors),
approximately 75% of the industry operates year-round and the remaining
25% operate 9 months during the year. Production rates are at peak level
during June and July, and are lowest during December through February.
For most of the year, the operating schedule is 6 days/week and 15 hr/day.
For aggregate users (cement manufacturing and concrete batching),
approximately 60% of the industry in the St. Louis AQCR operate year-
round, and the remaining 40% operate 9 to 10 months during the year.
Production rates change seasonally with demand for concrete for local
construction projects. The operating schedule is normally 6 days/week
and 16 hr/day. Spring and summer are peak seasons, and activities de-
cline during the winter months (December through February).
Seasonal and hourly apportioning factors for wind erosion from stock-
piles were based on observed variations in governing climatic conditions.
Seasonal factors were scaled to values of the climatic factor for wind
erosion,.Ul/ and hourly factors were proportioned to the probability that
the wind speed will exceed 12 mph, the threshold value for the onset of
wind erosion.
40
-------
UNPAVED AIRSTRIPS
GRID SOURCE EXTENT
The landing/takeoff (LTO) cycle is the designated measure of source
extent for fugitive emissions from unpaved airstrips. Figure 14 illus-
trates the procedure used to determine LTO cycles on unpaved airstrips
by grid.
9/ /
Airport data were extracted from an "Airport Services1* computer
tape obtained by MRI from the Federal Aviation Administration (FAA) under
EPA Contract No. 68-02-1437. Data on this tape include the following in-
formation for each airport: site number, city, state, airport name,
county code, latitude, longitude, airport type, number of total based
aircraft, number of multi-engine based aircraft, runway pavement type,
runway length, population served, ownership type, and usage type. A com-
puter program was written to list all Missouri and Illinois airports and
to output required data onto standard computer cards.
Nine airports within the St. Louis AQCR were designated as Pavement
Type 5 (dirt or gravel runways). However, seven of these airports did
not have any based aircraft and the remaining two were helicopter bases.
Airstrips with Pavement Type 4 (turf runways) numbered 43, of which,
25 turf airstrips (excluding heliports) listed based aircraft. Grid num-
bers for each of these 25 airstrips (see Table 7) were determined from
latitude and longitude indicated on the FAA tape.
Regional FAA officials estimated the number of operations per based
aircraft at small airport facilities to be in the range of 400 to 800
operations per year with a typical value being 500, i.e., 250 LTO cycles
per year.-H' The total number of LTO cycles on unpaved airstrips in each
grid was calculated by multiplying 250 LTO cycles per year times the total
number of aircraft based at unpaved airstrips within each grid.
41
-------
By Airport
1974 Based
Aircraft
By Airport
1974 Pavement
Type
Annual LTO
Cycles per
Based Aircraft
(Estimated =250)
By County
By Grid
Number of
Based Aircraft
on Unpaved
Airstrips
Number of
Based Aircraft
on Unpaved
Airstrips
By Grid
Annual LTO
Cycles on
Unpaved
Airstrips
Figure 14. Procedure for determination of annual LTO cycles on
unpaved airstrips
42
-------
Table 7. DATA ON UNPAVED (TURF) AIRSTRIPS BY COUNTY
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
No. of turf
airstrips with
based aircraft
2
3
3
4
3
7
1
0
1
1
0
0
Grid location
1,764
1,739, 1,761, 1,784
1,595, 1,641, 1,710
951, 990, 1,057, 2,273
1,579, 1,582, 1,633
1,456, 1,484, 1,586,
1,617 (2), 1,639,
2,341
1,842
_
185
166
-
-
LTO cycles/
year
500
2,500
5,250
12,000
750
8,750
250
0
3,000
1,250
0
0
-------
EMISSION FACTOR
The emission factor for unpaved airstrips, in units of pounds of
dust per landing/takeoff cycle, was derived by analogy to the equation
for unpaved roads,.2/ doubled to include propeller-generated wind ero-
sion. The expression for dirt airstrips is given by:
=2[0.49sa (
where sfl is the silt content (percent) of dirt airstrips (equivalent
to the agricultural soil silt content), Sa is the average aircraft
ground speed (mph), d is the number of dry days per year, and (1)
mile is the approximate length of runway used for an LTO cycles.' in-
cluding taxiing. Regional FAA officials^' estimated Sa to be 40 mph;
and, on the average, there are 250 dry days per year in the St. Louis
area.,1/
During the months of July through October, turf airstrips will ap-
proximate dirt airstrips due to dry weather conditions and higher volume
of traffic. It was estimated that the emission factor for turf airstrips
should be one-half the factor for dirt airstrips to account for the ef-
fect of grass cover in reducing wind erosion. The emission factor for
turf airstrips ranged from 4.5 to 31 Ib/LTO cycle for agricultural silt
contents ranging from 10 to 70%.
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors were derived from the following infor-
mation (see Figure 15):
1. Air traffic, i.e., landings and takeoffs, occurs primarily be-
tween the hours of dawn to dusk.
2. Approximately 50% of the air traffic occurs on weekends and holi-
days.
3. Approximately 70% of the air traffic occurs between the months
of April through October.
44
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Unpaved Airstrips
01 2345678 9 10 1112 13 14 15 16 17 18 19 20 21 22 23
Hour of Day
Saturday,
Sunday
Monday
through
Friday
30
25
20
15
10
5
35
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Fall
Figure 15. Percentage of total daily, weekly, and annual LTO cycles
45
-------
DATA TABULATIONS AND CALCULATED RESULTS
Tables 8 and 9 illustrate example data tabulations prepared for this
project. Table 8 gives data on (a) annual extent of fugitive dust sources
and (b) agricultural soil silt content, for the first 35 grids in the
RAPS study region. Table 9 presents the hourly adjustment factors for
a Sunday in the winter season. A complete set of example calculations is
detailed in Appendix A.
The preceding data were used as input for two computer programs:
1. Program 1, which calculates the annual emissions of fugitive
dust for each source category, by grid, and
2. Program 2, which calculates hourly emissions of fugitive dust
within a specified grid, for any hour of the year, through
multiplication of the annual emissions total by the particu-
lar hourly adjustment factor.
Simplified logic diagrams of these programs are presented in Figures 16
and 17. Both programs were written in Fortran IV to provide compatibility
with most computer systems. Example output for the annual emissions com-
puter program is illustrated in Figure 18.
47
-------
Table 8. EXAMPLE CODED SOURCE EXTENT AND CORRECTION FACTOR DATA
£
Source extent
Grid
No.
I
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
19
20
21
22
25
26
27
28
29
32
33
34
35
37
38
39
40
43
44
Coordinates
(in;M Zone 15)
E
640
640
640
640
645
645
645
645
645
650
650
650
650
650
650
655
660
660
660
660
665
670
670
670
670
670
670
670
670
671
671
672
672
673
673
_£L
4,235
4,245
4,265
4,280
4,230
4,255
4,260
4,275
4,280
4,230
4,235
4,245
4,255
4,265
4,275
4,230
4,235
4,245
4,255
4,265
4,230
4,230
4,235
4,245
4,250
4,260
4,265
4,268
4,269
4,268
4,269
4,268
4,269
4,265
4,266
Size
(km)
10
10
10
5
5
5
5
5
5
5
10
10
10
10
5
5
10
10
10
10
5
5
10
5
5
5
3
1
1
1
1
1
1
1
1
Unpaved
(10* veh.
Gravel
8,827
10,690
8,260
1,296
907
1,490
2,203
1,101
2,389
583
10,496
7,710
8,876
6,673
405
713
7,580
5,377
7,256
5,759
1,684
1,500
6,738
.1,555
1,745
130
0
0
0
0
0
0
0
0
0
roads
mi. )
Dirt
0
0
0
0
242
0
0
0
0
0
0
0
0
0
0
0
0
0
0
101
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Land tilling
Cacres)
11,103
11,102
11,102
2,774
2,776
2,776
2,776
2,776
2,774
2,776
11,105
11,105
11,105
11,448
2,774
2,776
11,105
11,105
11,105
11,102
2,776
2,776
11,105
2,776
2,776
2,776
999
110
110
110
110
110
110
110
110
Wind
erosion
(acres)
944
945
945
236
236
236
236
236
236
236
944
944
944
944
236
236
944
944
944
945
236
236
944
236
236
236
85
9
9
9
9
9
9
9
9
Construction
(10"1 acres)
147
153
156
23
46
46
46
46
34
46
184
184
184
178
32
46
184
184
184
156
46
46
184
46
46
46
17
2
2
2
2
2
2
2
2
Aggregate storage
(tons)
0
0
0
0
0
0
0
0
43,050
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Dirt airstrips
(LTO cvcles)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
o ,
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Correction
factor
Silt
content (7.)
40
40
59
59
40
40
40
59
59
40
40
40
40
59
59
40
40
40
40
59
30
40
40
40
40
67
67
50
50
50
50
50
50
67
67
-------
Table 9. HOURLY ADJUSTMENT EXAMPLE CODED FACTORS
Hourly adlustment factors (10 )
Number
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Time of
dav
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Unpaved
roads
283
252
189
63
63
377
1,227'
2,139
1,667
1,919
1,919
1,887
1,699
1,887
2,328
2,265
2,517
2,863
1,919
1,510
1,070
692
440
283
Land tilline
Illinois
3
3
3
3
3
78
130
156
182
182
208
208
182
182
208
182
182
156
130
104
78
26
10
3
Missouri
1
1
1
1
1
39
65
78
91
91
104
104
91
91
104
91
91
78
65
52
39
13
5
1
Wind erosion
Illinois
1,667
1,667
1,667
1,620
1,667
1,667
1,667
1,805
1,944
2,083
2,129
2,222
2,268
2,268
2,315
2,315
2,222
2,083
1,991
1,944
1,805
1,805
1,759
1,713
Missouri
1,713
1,713
1,713
1,665
1,713
1,713
1,713
1,855
1,998
2,141
2,188
2,283
2,331
2,331 ,
2,378
2,378
2,283
2,141
2,046
1,998
1,855
1,855
1,808
1,760
Construction
8
8
8
8
38
188
375
525
600
675
675
525
525
675
675
600
525
375
225
150
75
30
8
8
Aggregate
storage
311
311
311
311
311
851
851
867
884
900
900
916
916
916
933
933
916
900
884
884
425
311
311
311
Unpaved
airstrips
36
36
36
36
72
360
720
1,440
2,160
3,240
2,880
2,880
2,880
2,880
5,400
4,320
2,880
1,440
1,080
720
360
72
36
36
-------
For Each Grid:
INPUT: GRID DATA
Number
Coordinates
Width (km)
County
Agricultural Silt
Content, % (st,
, sa)
INPUT: SOURCE EXTENT DATA
Unpaved Roads (vehicle miles)
- Gravel & Dirt
Land Tilling (acres)
Wind Erosion (acres)
Construction (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO cycles)
INPUT: CORRECTION FACTOR CONSTANTS
Number of Dry Days Per Year (d)
Precipitation-Evaporation Index (PE)
Duration of Construction Activity (D)
Missouri, Illinois
Silt Content - Roads, Gravel (s_)
- Roads, Dirt (s
-------
For Each Season, Day, and Hour:
INPUT: TEMPORAL APPORTIONING FACTORS
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
COMPUTE: HOURLY ADJUSTMENT FACTORS
Season Factor x Day Factor x Hour Factor
o Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
For Each Grid:
INPUT: ANNUAL EMISSION RATE (tons/year)
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Roads
For Specified Grids:
COMPUTE & OUTPUT:
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
HOURLY EMISSION RATE (Ib/hr, kg/hr)
Figure 17. Simplified flow diagram of calculation procedure
for hourly emissions by grid
51
-------
CALCULATED ANNUAL EMISSION HATES BY G»1D
COORDINATE
GRID E N
*!D
S/rH
b/
-
C- UNPV. ROADS
EMISSION RATE
AG. TILLING WIND EROSION CONSTRUCTION
AG. STORAGE UNP.AIRSTRIP
TOTAL**
Ui
Ni
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
19
20
21
22
25
26
27
28
29
32
33
34
35
37
36
39
40
43
44
45
46
47
46
49
640
640
640
640
645
645
645
645
645
650
650
650
650
650
650
655
660
660
660
660
665
670
670
67o
670
670
670
670
670
671
671
672
672
673
673
673
673
673
674
674
4235
4245
4265
4280
4230
4255
42t>0
4275
4260
4230
4235
4245
4255
426S
4275
4230
4235
4245
4255
4265
4230
4230
4235
4245
4250
4260
4265
4266
4269
4268
4269
4266
4269
4265
4266
4267
4266
4269
4265
4266
10
10
10
5
5
5
5
5
5
5
10
10
10
10
5
5
10
10
10
10
5
5
10
5
5
5
3
1
1
1
1
1
1
1
1
1
1
1
1
1
8
e
a
a
a
8
a
8
a
a
a
a
a
8
a
8
a
a
a
e
3
a
8
8
8
6
a
e
H
8
a
a
a
8
8
8
6
8
6
6
2369.99
2870.19
2217.75
347.97
405.96
400.05
591.49
295.61
641.43
156.53
2818.10
2070.08
2383.15
1791.65
108.74
191.44
2035.18
1443.69
1946.19
1646.25
452.14
402.74
1609.11
417.51
468.52
34.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
70.61
70.61
104.15
36.0*
17.65
17.65
17.65
26. .1*
26.0*
17.65
70.62
70.62
70.62
104.15
26.04
17.65
70.62
70.62
70.62
10*.15
13.24
17.65
70.62
17.65
17.65
29.57
10.64
.7
.87
.87
.87
.87
.87
1.17
1.17
1.17
.87
.87
1.17
1.17
849.6
850.5
650.5
212.4
212.4
212.4
212.4
212.4
212.4
212.4
649.6
849.6
849.6
349.6
212.4
849.6
849.6
8*9.6
850.5
212.4
212.4
849.6
212.4
212.4
212.4
76.5
8.1
8.1
8.1
8.
8.
8.
8.
8.
8.
8.
8.
8.
8.
176.4
143.6
187.2
27.6
55.2
55. 2
55.2
55.2
40.8
55.2
220.8
220.8
220.8
213.6
38.4
55.2
220.8
220.8
220.8
187.2
55.2
55.2
220.8
55.2
55.2
55.2
?0.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
r.103
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
o.eoo
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
3467
3975
3360
614
691
685
877
589
928
442
3959
3211
3524
29S9
386
477
3176
2585
3089
2788
733
688
2950
703
754
332
108
11
11
11
11
11
11
12
12
12
11
11
12
12
£/ Grid size (width) in kilometers.
t>/ County which represents major portion of grid.
Figure 18. Example computer output of annual emissions by grid
-------
ANALYSIS OF RESULTS AND ESTIMATED ACCURACIES
Table 10 presents a county breakdown of annual fugitive dust emis-
sions in the Metropolitan St. Louis AQCR. This data represents all grids
which lie entirely or partially within a specific county. As indicated,
unpaved roads and wind erosion from agricultural tilled land account for
more than 80% of the total fugitive dust emissions for the St. Louis area.
The total quantity of particulate emissions smaller than 30 um in
diameter emitted by fugitive dust sources considered in this project is
1,145,000 tons/year. Assuming that 20% of the emissions (i.e., the por-
tion smaller than 5 um in size) will be transported to ambient air qual-
ity monitoring stations (see Appendix B), then 229,000 tons/year of fugi-
tive dust will have an impact on regional air quality and must be taken
into account in modeling the St. Louis AQCR. In comparison, total nonfugitive
emissions for the St. Louis AQCR are 355,000 tons/year;JfL=L/ thus, fugitive
emissions may be said to represent 39% of the total particulate pollutant
problem.
Table 11 presents estimates for possible error in the calculated
values corresponding to a 90% confidence level and were determined by a
progressive analysis of errors associated with each calculation step.
Composite ranges of error are presented for calculated source extent, cor-
rected emission factors, and hourly adjustment factors.
53
-------
Table 10. SUMMARY OF ANNUAL EMISSIONS BY COUNTY
Ui
Emission rate (tons/vear)
State County
Illinois Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Subtotal
Missouri Franklin
Jefferson
St. Charles
St. Louis City
St. Louis
Subtotal
Total
Unpaved
roads
46,594
56,874
69,509
21,338
50,431
62,286
57,524
364,556
44,721
18,478
19,818
0
0
83,017
447,573
Agricultural
tilling
5,612
7,804
8,796
4,852
6,132
9,509
9,115
51,820
3,750
678
2,478
0
1,395
8,303
60,123
Wind
erosion
44,186
62,665
80,865
42,542
50,896
77,950
70,846
429,950
20,089
5,436
39,607
0
11,749
76,881
506,831
Construction
1,716
3,048
19,680
1,812
3,996
21,120
1,044
52,416
5,220
12,765
9,901
3,256
45,491
76,633
129,049
Aggregate
storage
0
5.2
12.2
7.7
45.4
313.5
16.5
400.5
6.3
215.8
57.0
0
323.9
603.0
1,003.5
Unpaved
airstrips
7.8
39.1
70.4
174.6
10.9
133.0
3.9
439.7
0
33.6
9.8
0
0
43.4
483.1
Total
98,115
130,435
178,932
70,726
111,511
171,311
138,549
899,582
73,788
37,606
71,870
3,256
58,958
245,478
1,145,058
-------
Table 11. ESTIMATED ERRORS FOR TABULATED DATA
Estimated relative error
Source Source Corrected Hourly adjust-
category extent emission factor merit factor
Unpaved roads + 5% + 20% + 15%
Agricultural tilling + 15% + 30% + 20%
Wind erosion + 30% + 20% + 15%
Construction + 35% + 30% + 20%
Aggregate storage + 25% + 30% + 20%
Unpaved airstrips + 15% + 25% + 20%
55
-------
REFERENCES
1. Cowherd, C., Jr., K. Axetell, Jr., C. M. Guenther, and G. A. Jutze,
Development of Emission Factors for Fugitive Dust Sources, prepared
for the U.S. Environmental Protection Agency, Office of Air and
Waste Management, Office of Air Quality Planning and Standards,
Contract No. 68-02-0619, Publication No. EPA-450/3-74-037, June
1974.
2. Personal communication from Mr. John Godar, Head, Planning Depart-
ment, Illinois Department of Transportation, District 8, East
St. Louis, Illinois, September 1975.
3. Personal communication from Mr. Robert Barren, Mapping Department,
Missouri State Highway Commission, Jefferson City, Missouri,
November 4, 1975.
4. Personal communication from Mr. George Daykin, County Engineer,
St. Louis County, Clayton, Missouri, November 3, 1975.
5. Climatic Atlas of the United States, U.S, Department of Commerce,
Environmental Science Services Administration, Environmental Data
Service, U.S. Government Printing Office, Washington, D.C., June
1968.
6. Cowherd, C., Jr., C. Guenther, and D. Wallace, Emissions Inventory
of Agricultural Tilling, Unpaved Roads and Airstrips, and Construc-
tion Sites. EPA Publication No. EPA-450/3-74-085, November 1974.
7. Kennedy, N., J. H. Kell, and W. S. Homburger, Fundamentals of Traffic
Engineering, 8th edition, Institute of Transportation and Traffic
Engineering, University of California, Berkeley, California, 1973.
8. 1969 Census of Agriculture, County Summary, Table 2, U.S. Department
of Commerce, U.S. Government Printing Office, Washington, D.C,
57
-------
9. 1971-72 Existing Land Use Update and Analysis, Land Use Component
Technical Report, East-West Gateway Coordinating Council, June 1973,
10. "Generalized Existing Land Use 1970St. Louis Metropolitan Area,"
East-West Gateway Coordinating Council, 1973.
11. Thornthwaite, C. W., "Climates of North America According to a New
Classification," Geograph. Rev., 2^:633-655 (1931).
12. Personal communication from J. Wiley Scott, Assistant State Soil
Scientist, Soil Conservation Service, Champaign, Illinois, July
28, 1975.
13. Personal communication from J. Vernon Martin, State Conservationist,
Soil Conservation Service, Columbia, Missouri, July 22, 1975.
14. "Major Soils of the North Central Region, U.S.A.," a map from Soils
of the North Central Region of the United States, North Central
Regional Publication No. 76, Bulletin 544, published by the Agri-
cultural Experimental Station, University of Wisconsin, in coop-
eration with the U.S. Department of Agriculture, June 1960.
15. "Guide for Textural Classification in Soil Families," supplement
to Soil Classification; A Comprehensive System, Seventh Approxi-
mation, Soil Survey Staff, Soil Conservation Service, U.S. Depart-
ment of Agriculture, p. 40, March 1967.
16. Gillette, D. A., "Production of Fine Dust by Wind Erosion of Soil:
Effect of Wind and Soil Texture," paper presented at the Atmos-
phere-Surface Exchange of Particulate and Gaseous Pollutants, 1974
Symposium, September 1974.
17. STAR program, six stability classes (day/night), seasonal and annual
listing, National Climatic Center, Asheville, North Carolina,
January 1970 - December 1974.
18. Personal communication from Neil Woodruff, U.S. Department of Agri-
culture, Agricultural Research Service, Kansas State University,
Manhattan, Kansas, January 10, 1974,
19. Personal communication from John Kinsey, East-West Gateway Coordi-
nating Council, September-November 1975.
20. 1974 Short-Range Improvement Program, East-West Gateway Coordinating
Council, St. Louis, Missouri, June 1974.
58
-------
21, 1972 Census of Construction Industries, Preliminary Report, U.S.
Department of Commerce, Bureau of the Census.
22. County and City Data Book 1972, a Statistical Abstract Supplement,
U.S. Department of Commerce, Bureau of the Census, U.S. Govern-
ment Printing Office, Washington, D.C. (1973).
23. Personal communication from Mr. Charles C. Masser, Project Officer,
U.So Environmental Protection Agency, Office of Air Quality Plan-
ning and Standards, September 15, 1975.
24. "Airport Services Tape," Federal Aviation Administration, Public
Information Center, AIS 230, Washington, D.C. 20591.
25. 1972 National Emissions Report, National Emissions Data System (NEDS)
of the Aerometric and Emissions Reporting System (AEROS), U.S.
Environmental Protection Agency, Publication No. EPA-450/2-74-012,
June 1974.
59
-------
APPENDIX A
EXAMPLE CALCULATIONS
(RAPS GRID NO. 1)
61
-------
GRID DATA
Number: 1
UTM Coordinates: E 640, N 4235
Size (length): 10 km
C oun ty: F rank1in
State: Missouri
ANNUAL SOURCE EXTENT
f\
Unpaved Roads: gravel = 8,827 x 10^ vehicle miles
soil = 0 vehicle miles
Agricultural Tilling: 11,104 acres
Wind Erosion: 944 acres
Construction: 147 x 10""1 acres
Aggregate Storage: 0 tons
Unpaved Airstrips: 0 LTO cycles
CORRECTION FACTORS
Number of Dry Days Per Year (d): 250 days
Precipitation-Evaporation Index (PE): 93
Duration of Construction Activity (D): 12 months
Silt Content: Unpaved roads, gravel (sr): 16%
Dirt (sr): 407.
Agricultural tilling (st): 40%
Unpaved airstrips (sa): 40%
Vehicle Speed: Unpaved roads (Sr): 30 mph
Unpaved airstrips (S ): 40 mph
ANNUAL EMISSION FACTORS
Unpaved Roads: EFr = 0.49 sr [ " d ' lb
30/1365/ vehicle mile
y AN
Gravel: EF = 0.49 (U»)
r
250
= 5.37
lb
/ w \
Dirt: EFr = (0.49) (40)( \ 115P.J = 13.4
vehicle mile
lb
vehicle mile
Land Tilling: EFt = ' .l*^ ~^-
c (PE/50)Z acre
EF . 1.1 (40) = 12.72-ib_
fc (93/50)2 acre
Wind Erosion: EFW =0.9 tons/acre
62
-------
Construction: EFC = D
acre
EF = 12 months x 1 ton/acre = u ton/acre
c . month
Aggregate Storage: EFS =0.33 lb
ton stored
f \ /
Unpaved Airstrips: EFa = 0.49 (sfl) (fSjj-^-.l lb
30\365/ LTO cycle
0.49 (40) - 17.9
a ^ ^ V^ x I A 11 * I » ^
\30/\365 j LTO cycle
ANNUAL EMISSIONS
Annual Emissions (tons) = Annual Source Extent x Annual Emission Factor
Unpaved Roads: gravel = (8,827 x 1Q2 veh. mile)(5.37 Ib/veh. mile)
2,000 Ib/ton
= 2,370 tons
dirt'= 0 tons
Land Tilling: (11,104 acres)(12.72 lb/acre) = 70<6 tons
2,000 Ib/ton
Wind Erosion: (944 acres)(0.9 tons/acre) = 850 tons
Construction: (147 x 10 acres)(12 tons/acre) = 176.4 tons
Aggregate Storage: (0 tons)(0.33 lb/ton)(l ton/2,000 lb) = 0 tons
Unpaved Airstrips: (0 LTO cycles)(l7.9 Ib/LTO cycles)(l ton/2,000 lb) = 0 tons
TEMPORAL APPORTIONING FACTORS
Temporal Apportioning Factor = (Seasonal Factor)(Day of the Week
Factor)(Hour of the Day Factor)
Example: (Winter Factor)(Sunday Factor)(Hour 0 Factor)
Unpaved Roads: (0.214)(0.147)(O.Q09) = 283 x 10~6
Agricultural Tilling: 1 x 10"6
Wind Erosion: 1,713 x 10"6
Construction: 8 x 10'6
63
-------
Aggregate Storage: 311 x 10"°
Unpaved Airstrips: 36 x 10"^
HOURLY EMISSIONS
Hourly Emissions (tons) = Annual Emissions (tons) x Temporal
Apportioning Factor
Example: Winter, Sunday, Hour 0, Grid 1
Unpaved Roads: (2,370 tons)(283 x 10~6) = 0.671 tons
Agricultural Tilling: (70.6 tons)(l x 10~6) = 70.6 x 10"6 tons
Wind Erosion: (850 tons)(l,7l3 x 10"6) = 1.46 tons
Construction: (176.4 tons)(8 x 10"6) = 1.41 x 10~3 tons
Aggregate Storage: (0 tons)(311 x 10"6) = 0 tons
Unpaved Airstrips: (0 tons)(36 x 10"6) = 0 tons
METRIC UNITS CONVERSION
Annual Emissions (Mtons) = Annual Emissions (tons) x 0.907185
(Mtons/ton)
Hourly Emissions (Mtons) = Hourly Emissions (tons) x 0.907185
(Mtons/ton)
64
-------
APPENDIX B
FACTORS AFFECTING ATMOSPHERIC TRANSPORT OF FUGITIVE DUST
65
-------
This appendix presents an assessment of factors which determine
the drift distances of fugitive dust particles in the atmosphere. Drift
distance is defined as the horizontal displacement from the point of
particulate injection to the point of particulate removal by ground-
level deposition.
Factors to be considered in this assessment may be grouped into
two categories: .
1. Meteorological factors - properties of the atmosphere which
affect contaminant advection and turbulent diffusion over surfaces of
varying roughness scales.
2. Source factors - height of injection and particulate properties
which affect gravitational settling and vertical mixing.
This assessment does not treat atmospheric washout of particulate matter.
METEOROLOGICAL FACTORS
Fugitive dust particles are typically injected into the lower por-
tion of the "surface layer" region of the atmosphere which extends from
ground level to a height of about 100 m. In this region the profile of
the wind and its turbulence characteristics are strongly dependent on
surface roughness properties.
For neutral atmospheric stability, the vertical profile of mean
wind speed, u(z) , in the surface layer is described by a logarithmic
relationship:
Uju
,,(v\ __ Oi
u(z) - k h
where u* = friction velocity
k = von Karman1s constant (0.4 for clear fluids)
, ZQ = surface roughness height
Neutral stability occurs with wind speed exceeding 12 mph or with over-
cast conditions regardless of wind speed.
The friction velocity, u* , is related to the rate of momentum ex-
change at the surface:
66
-------
1/2
u* = (T0/pa) (2)
where TQ = surface shear stress
pa = density of air
Within the surface layer, the vertical flux of momentum (and hence
is known to be roughly constant and the eddy diffusivity is given by
e (z) = ku* z (3)
Aerodynamic roughness height, zo , is related to the size, shape
and spatial density of the roughness elements. Based on similarity con-
cepts Lettaui' has derived the following expression for evenly spaced
elements:
z = -
2o 2A
where H = effective height of roughness elements ' ~: -
a = silhouette area normal to the wind
A = total ground area per element
1/2 = average drag coefficient.
Figure B-l gives roughness heights for various natural and man-
made roughness features.
SOURCE FACTORS : ..
The primary source factors which affect the drift distance of a
fugitive dust particle are injection height, h , and particle settling
velocity, V , which may be approximated by the Stoke1s relationship:
V = 0.00301 p D2 (5)
s p
67
-------
High Rise Buildings
(30+Floors )J/
Suburban
Medium Buildings-
(I institutional)-!/
E
o
o
N
O
<2 Suburban
Z Residential Dwellings-!/!.
g Wheat Field J/'
O ,
Plowed FieldJ/-
Zo(cm)
1000
Natural Snow_rA
-800
600
400
-200-
100
-80.0-
-60.0-
40.0
-20.0-
10.0
-8.0
-6.0
4.0
2.0-
1.0
0.8-
0.6-
-0.4-
0.2|
0.1
Urban Area JL/
Woodland Forest _£/
Grassland _£/
Figure B-l. Roughness heights for various surfaces
68
-------
where V_ = terminal settling velocity (cm/sec)
5
p = density of particle (g/cnr)
D = particle diameter (um)
Fugitive dust particles typically have a mineral composition with a
density of about 2.5 g/cm .
CALCULATION OF DRIFT DISTANCE
In the past, most analyses of the atmospheric disperison of par-
ticles with appreciable settling tendencies have focused on the dis-
tribution of settling rate, S(x) , expressed as:
S(x) = Vg CQ(x) (6)
where Co = the ground-level concentration of particulate with
settling velocity Vs
x = downwind distance from the source , '';
Accordingly, an Eulerian approach to the problem has been taken.
However, analysis of particle drift with no net effect of atmospheric
turbulence, is most conveniently treated by a Lagrangian approach. This
is illustrated in the following section.
Case 1: Monodisperse particles, single injection height, negligible
turbulence effect.
Consider the case of a steady stream of monodisperse particles re-
leased from a continuous crosswind line source at height h . It is
assumed that each particle during its lifetime in the atmosphere is sub-
jected to a balanced set of vertical turbulent velocity fluctuations with
the result that the particle does not deviate appreciably from the tra-
jectory it would have in the absence of turbulence.
The vertical position, z , of the particle as a function of time is
given by
zp(t) = h-Vst (7)
69
-------
Substitution of Eq. (7) into Eq. (1) gives the following expression for
the horizontal speed of the particle:
The particle drjft distance, xp , is given by:
3 undt (9a)
where the upper limit of integration is the lifetime of the particle
in the atmosphere. Integration of Eq. (9a) yields
To determine the effect of injection height and roughness height
on the drift distance of particles of given aerodynamic sizes, the wind
speed at z = 100 m was fixed at 6.9 m/s (15.4 mph) and friction velocities
were determined from Eq. (1). The results are shown in Table B-l for
injection heights of 1, 3 and 10 m and for roughness heights spanning
the range given in Table B-l. Figure B-2 shows the variations of x-
for h = 3 m, measured above ZQ .
As expected, for particles of a given size, drift distance increases
with injection height and decreases with roughness height. The latter .
effect is a direct result of the decrease in wind velocity near the sur-
face caused by obstacles to the flow.
Case 2: Monodisperse particles, single injection height, turbulent
atmosphere.
The analysis presented under Case 1 assumed that all particles gen-
erated from a particular fugitive dust source were deposited at the same
point downwind (x-). Clearly, however, particles subjected to a pre-
ponderance of downward turbulent velocity fluctuations will settle from
the atmosphere at distances less than xp and particles propelled above
the trajectory defined above may drift far beyond Xp . In other words,
because of the random nature of turbulent velocities, xp approximates
the distance at which half of the particles have deposited on the surface.
70
-------
Table B-l. PARTICLE DRIFT DISTANCES CALCULATED FROM EQ. (9b)
Injection Roughness
height, I/ height,
h zo
(m) (m)
1 0.01
0.05
0.10
0.50
3 0.01
0.05
0.10
0.50
1.00
10 :'. . .- o.oi
0.05
0.10
-. ;- /, -i 0.50
- i.oo
Friction
velocity,
u*
(cm/sec)
30.0
36.4
40.0
52.2
30.0
36.4
40.0
52.2
60.0
30.0
36.4
40.0
5.2.2
60.0
Drift distance, xp , by particle size
30 urn
40.6 m
29.5
24.2
12.5
157.1 m
128.2
112.9
73.5
56.4
655 m
582
541 ;
423
363 ,,
20 urn
91.2 m
66.4
54.4
28.1
353 m
288
254
165
127
1,474 m
1,309
.-.'. 1»216
; 952
816
10 urn
366 m
266
218
113
1,418 m
1,157
1,019
663
509
5.92 km
-5.25"
4.88
3.82
3.28
5 um
1,460 m
1,060
871
450
5.66 km
4.62
4.07
2.65
2.03
23.6 km
21.0
19.5
15.3
13 .1
1 urn
36.6 km
26.7
21.8
11.3
141.8 km
115.7
101.9
66.3
50.9
592 km
525
488
382
328
aj Injection height measured above roughness height.
-------
Injection Height (h) = 3m above z(
Natural Snow (zo= 0.1 cm)
Plowed Field (zo= 1.0cm)
- Grassland (zo = 3.0 cm)
Suburban Residential
Dwelling.(z0-5.0 cm)
Suburban Medium
Building (zo= 70.0 cm)
DRIFT DISTANCE (meters)
Figure B-2. Relationship between particle size and drift distance
-------
The specific question addressed here has to do with the form of
the settling rate distribution. Recalling Eq. (6), this problem re-
duces to finding the distribution of ground-level concentration by
solving the appropriate transport equations and accompanying boundary
conditions* _ .
The phenomena of quasi-steady advectipn and turbulent diffusion
from a continuous line source under the condition"of uniform wind speed
is described by the following equation: ? - »
u <£. = pu £- [ z ££}+ vs *£
dx dzV dz 7 s dz
where C = particulate concentration
U = uniform speed of crosswind
p = turbulence parameter.
The uniform wind speed, U , is assumed to have the value given by the
Case 1 velocity profile at z = h. The quantity pUz becomes the coef-
ficient of eddy diffusivity.
Although Eq. (10) is not amenable to analytical solution for the
case in point, it has been shown5/ that the distribution of ground-level
concentration has the following form:
-h/px
Co(x) = A ^r- - (11)
where A = constant
vs
"'5*
The function given in Eq. (11), and hence the settling rate, reaches a
maximum at:
and then decays to zero as x>» . Values for ^maK are given in
Table B-2 based on values of p determined by comparing the two forms
of the eddy diffusivity, yielding
p = ku*/U . (13)
73
-------
.Table B-2. DISTANCES TO POINT OF MAXIMUM SETTLING,
CALCULATED FROM EQ. . (12)
" 1 ' '
Injection > Roughness
height, height,
h - z
(m) (m)
1 ' 0.01
0.05
O'.IO
0.50
3 0.01
0.05
0.10
0.50
1.00
10 0.01
0.05
0.10
0.50
1.00
Turbulence
parameter,
P
0.0347
0.0534
0.0695
0.2308
0.0281
. 0.0391
0.0470
0.0893
0.1456
0.0232
0.0302
0.0347
0.0534
0.0695
Friction
u*
(cm/ sec)
30.0
36.4
40.0
52.2
30.0
36.4
40.0
52.2
60.0
30.0
36.4
40.0
52.2
60.0
Values of a and xmax (m) by particle size
30
a
0.564
0.465
0.423
0.324
0.564
0.465
0.423
0.324
0.282
0.564
0.465
0.423
0.324
0.282
Urn
Xmax
18.4
12.8
10.1
3.27
68.3
52.4
44.9
25.4
16.1
276
226
203
141
112
20 pm
« "xmax
0.251 23.0
0.207 15.5
0.188 12.1
0.144 3.79
0.251 85.3
0.207 63.6
0.188 53.7
0.144 29.4
0.125 18.3
0.251 345
0.207 274
0.188 243
0.144 164
0.125 128
10
a
0.0625
0.0515
0.0469
0.0359
0.0625
0.0515
0.0469
0.0359
0.0312
0.0625
0.0515
0.0469
0.0359
0.0312
pm
Xmax
27.1
17.8
13.7
4.18
100.5
73.0
61.0
32.4
20.0
406
315
275
181
140
5
' a
0.0157
0.0129
0.0118
0.0090
0.0157
0.0129
0.0118
0.0090
0.0078
0.0157
0.0129
0.0118
0.0090
0.0078
pm
Xmax
28.4
18.5
14.2
4.29
105.1
75.7
63.1
33.3
20.4
424
327
285
186
143
1 pin
Of
0.00062
0.00052
0.00047
0.00036
0.00062
0.00052
0.00047
0.00036
0.00031
0.00062
0.00052
0.00047
0.00036
0.00031
Xmax
28.8
' 18.7
14.4
4.33
106.7
76.7
63.8
33.6
20.6
431
331
288
187
144
-------
The constant A in Eq. (11) may be evaluated by equating the emis-
sion rate E to the integrated settling rate.
f/~°° ~-h/px
CoVg dx = AVS / e-rF5 dx (14)
With the transformation y = b/x where b = h/p ,' the above equation
becomes
AVg /
"b« J
dy = V (15)
where !"(») is the gamma function.
Similarly it can be shown that the mass fraction K of particles
remaining suspended beyond some distance x is given by:
where the incomplete gamma function F(o/»b/x) is defined as
-b/x : !
-y y
-------
Case 3: Polydisperse particles, distributed injection height, tur-
bulent atmosphere.
This case is treated by separately analyzing the dispersion of
particles within narrow size ranges and injection height ranges and by
superimposing the results. The analytical techniques to be used are
those described above.
76
-------
REFERENCES TO
APPENDIX B
1. Lettau, H. H., "Physical and Meteorological Basis for Mathematical
Models of Urban Diffusion Processes," Chapter 2, Proceedings of
Symposium on Multiple-Source Urban Diffusion Models, U.S. Environ-
mental Protection Agency, Publication No. AP-86 (1970).
2. Davenport, A. G., "The Relationships of Wind Structure to Wind Load-
ing, in Wind Effects on Buildings and Structures," National Physi-
cal Laboratory, Symposium 16, Her Majesty's Stationery Office,
London (1965).
3. Deacon, E. L., "Vertical Diffusion in the Lowest Layers of the
Atmosphere," Quarterly J. Royal Meteorological Society, 75:89
(1949).
4. Gillette, D. A., and P. A. Goodwin, "Microscale Transport of Sand-
Sized Soil Aggregates Eroded by Wind," J. of Geophysical Research,
7JK27):4080-4084, September 20, 1974.
5. Bosanquet, C. H., and J. L. Pearson, "The Spread of Smoke and Gases
from Chimneys," Trans. Faraday Soc.. 32j 1249-1264 (1936).
6. Gillette, D. A., and I. H. Blifford, Jr., "The Influence of Wind
Velocity on Size Distribution of Aerosols Generated by the Wind
Erosion of Soils," J. Geophysical Research. 7JK27): 4068-4075,
September 20, 1974.
77
-------
TECHNICAL REPORT DATA
(Please read Instructions on The reverse before completing)
i. REPORT NO.
EPA-450/3-76-003
2.
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
Development of a Methodology and Emission Inventory
for Fugitive Dust for the Regional Air Pollution
Study
5. REPORT DATE
January, 1976
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Dr. Chatten Cowherd and Ms. Christine Guenther
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-2040
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park. North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final Report
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report outlines the methodology that was used in developing an hourly
fugitive dust emissions inventory for the Metropolitan St. Louis Air Quality
Control Region as part of the Regional Air Pollution Study (RAPS). The inventory
encompassed the following source categories: (a) unpaved roads, (b) agricultural
land tilling, (c) wind erosion of agricultural land, (d) construction sites,
(e) aggregate storage piles, and (f) unpaved airstrips.
Results presented in this report include temporal apportioning factors, county
totals of annual source extent and annual emissions for each source category.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
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Regional Air Pollution Study
Fugitive Dust Emissions
Emission Models
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