METHODOLOGY TO ESTIMATE
NONROAD EQUIPMENT POPULATIONS
BY NONATTAINMENT AREAS
FINAL REPORT
Prepared for:
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
2565 Plymouth Road
Ann Arbor, Michigan 48105
Prepared by:
ENERGY AND ENVIRONMENTAL ANALYSIS
1655 North Fort Myer Drive. Suite 600
Arlington, Virginia 22209
September 30, 1991
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METHODOLOGY TO ESTIMATE
NONROAD EQUIPMENT POPULATIONS
BY NONATTAINMENT AREAS
FINAL REPORT
Prepared for:
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
2565 Plymouth Road
Ann Arbor, Michigan 48105
Prepared by:
ENERGY AND ENVIRONMENTAL ANALYSIS
1655 North Fort Myer Drive, Suite 600
Arlington, Virginia 22209
September 30, 1991
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TABLE OF CONTENTS
Page
1. INTRODUCTION L-L
2. EQUIPMENT AND ENGINE CLASSIFICATION SCHEMES 2-1
2.1 Overview 2-1
2.2 Equipment Categories 2-1
2.3 Engine Categories 2-7
3. DATA SOURCES 3-1
3.1 Overview 3-1
3.2 Power Systems Research 3-1
3.3 Other Data Sources 3-5
4. METHODOLOGY TO ESTIMATE EQUIPMENT POPULATIONS
AT THE NONATTAINMENT AREA LEVEL 4-1
4.1 Description of Methodology 4-1
4.2 Category 1: Lawn and Garden Equipment 4-4
4.3 Category 2: Airport Service Equipment 4-5
4.4 Category 3: Recreational Equipment 4-9
4.5 Category 4: Marine Equipment 4-13
4.6 Category 5: Light Commercial Equipment (< 50 HP) 4-18
4.7 Category 6: Industrial Equipment 4-21
4.8 Category 7: Construction Equipment 4-21
4.9 Category 8: Agricultural Equipment 4-24
4.10 Category 9: Logging Equipment 4-26
5. EQUIPMENT POPULATIONS BY NONATTAINMENT AREA 5-1
APPENDIX A: Equipment Classification Scheme A-l
APPENDIX B: PSR's Survey Questionnaire B-l
APPENDIX C: Agricultural Census' Equipment Populations C-l
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LIST OF TABLES
Table 1-1
Table 2-1
Table 2-2
Table 2-3
Table 4-1
Table 4-2
Table 4-3
Table 4-4
Table 4-5
Table 4-6
Table 4-7
Table 4-8
Table 4-9
Table 4-10
Table 4-11
Table 4-12
Table 4-13
Table 4-14
Table 5-1
Table 5-2
Table 5-3
Table 5-4
Table 5-5
Non-Attainment Areas
Equipment Types Included in the Study .... . .
Engine Categorization
Potential Engine Classifications Definitions
Class 1: Lawn and Garden Equipment "Best Model"
Major Air Carrier Airports by Non-Attainment Area
Class 2: Airport Service Equipment "Best" Model
Class 3: Recreational Equipment "Best" Model
Class 3: Recreational Equipment Alternative Model for
Selected Non-Attainment Areas
Number of Engines Per Boat: Length and Engine Type ....
Inland Water and Coastal Public Beach
Class 5: Light Commercial Equipment "Best" Model
Class 6: Industrial Equipment "Best" Model
Class 7: Construction Equipment "Best" Model
Class 8: Agricultural Equipment "Best" Model
Class 9: Logging Equipment SIC 241 Regressions
Class 9: Logging Equipment Biased Model ...
Logging Activity by Non-Attainment Area As Reported by SIC
241 - Logging
National Equipment Type Populations, 2-Stroke/4-Stroke
Splits, and LPG/CNG Penetration Rates (By Fuel Type) . . .
National Averages for Annual Hours of Use, Load Factor,
and Horsepower by Fuel Type
Regional Average Annual Hours of Use
Non-Attainment Areas and Their Corresponding Annual Hours
of Use Regions
Estimated Equipment Populations by Non-Attainment Area
and Fuel Type
Page
1-2
2-2
2-9
2-10
4-6
4-8
4-10
4-12
4-14
4-15-
4-17
4-L9
4-22
4-23
4-25
4-27
4-29
4-3L
5-3
5-5
5-7
5-9
5-10
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LIST OP FIGURES
Page
Figure 3-1 Typical Engine Attrition Curve 3-4
Figure 4-1 Airport Service Equipment Regression Fit and Outliers . . . 4-11
Figure 4-2 Light Commercial Equipment Regression Fit and Outliers . . 4-20
Figure 4-3 Logging Equipment Distribution of Residuals 4-30
iii
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1. INTRODUCTION
The Clean Air Act Amendments of 1990 require that EPA conduct a study to
determine the contribution of nonroad equipment to the emission inventories of
selected nonattainment areas. This contribution is determined by the
population of nonroad equipment in a given area, the average load factor at
which the equipment's engine is used, the average annual hours of use of the
equipment, the horsepower of the engine, and the emission factor attributable
to the engine. Since a nonattainment area is a conglomeration of counties'
within a state, or across states, it is necessary to estimate the county level
equipment populations. Engine sales data or equipment population data,
however, are not available at the county level, but only at the national andx
state level. Therefore, a methodology that distributes equipment populations
from these levels of aggregation to the county level must be developed. This
report presents EEA's methodology to estimate equipment populations for the 24
nonattainment areas included in EPA's study. The 24 areas are presented in
Table 1-1.
Nonatcainment area populations, however, are only one element of the emission
inventory calculation process. County specific data characterizing nonroad
engines, and their use, are not available. It is reasonable to assume that
the national averages of load factors, annual hours of use, horsepower, and
emissions factors for a nonroad engine will approximate county specific
averages for that same engine. With this caveat in mind, this report also
presents national data on load factors, annual usage, and horsepower.
Emission factors are not presented, since EPA has developed estimates
independently.
The estimation of emissions inventories involves five steps: 1) the
development of a detailed profile of the types of equipment to be considered,
2) the classification of "similar" equipment into equipment categories, 3) the
development of specifications for engine categories useful for an emissions
1-1
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Table 1-1
Non-Attainment Areas
1. BALTIMORE CMSA
Anne Arundel County, MD
Baltimore City, MD
Baltimore County, MO
Carroll County, MD
Harford County, MD
Howard County, MD
Queen Anne's County, MD
2. CHICAGO CMSA
Cook County, IL
Du Page County, IL
Grundy County, IL
Kane County, IL
Kendall County, IL
Lake County, IL
McHenry County, IL
Will County, IL
Lake County, IN
Porter County, IN
Kenosha County, WI
3. DENVER CMSA
Adams County, CO
Arapahoe County, CO
Boulder County, CO
Denver County, CO
Douglas County, CO
Jefferson County, CO
4. HOUSTON CMSA
Brazoria County, TX
Fort Bend County, TX
Calveston County, TX
Harris County, TX
Liberty County, TX
Montgomery County, TX
Waller County, TX
5. MILWAUKEE CMSA
Milwaukee County, WI
Ozaukee County, VI
Racine County, WI
Washington County, WI
Waukesha County, WI
6. BOSTON NECMA
Essex County, MA
Middlesex County, MA
Norfolk County, MA
Plymouth County, MA
Suffolk County, MA
7. HARTFORD NECMA
Hartford County, CT
Middlesex County, CT
ToLIand County, CT
8. NEW YORK CMSA
Bergen County, NJ
Essex County, NJ
Hudson County, NJ
Hunterdon County, NJ
Middlesex County, NJ
Monmouth County, NJ
Morris County, NJ
Ocean County, NJ
Passaic County, NJ
Somerset County, NJ
Sussex County, NJ
Union County, NJ
Bronx County, NY
Kings County, NY
Nassau County, NY
New York County, NY
Orange County, NY
Putnan County, NY
Queens County, NY
Richmond County, NY
Rockland County, NY
Suffolk County, NY
Westchester County, NY
Fairfield County, CT
Litchfield County, CT
New Haven County, CT
9. PHILADELPHIA CMSA
Bucks County, PA
Chester County, PA
Delaware County, PA
Montgomery County, PA
Philadelphia County, PA
Burlington County, NJ
Camden County, NJ
Cumberland County, NJ
Gloucester County, NJ
Mercer County, NJ
Salem County, NJ
New Castle County, DE
Cecil County, MD
10. SBATTLE-TACOMA CMSA
King County, WA
Pierce County, WA
Snohomish County, WA
1-2
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Table 1-1 (cont.)
Non-Attainment Areas
11. ATLANTA CMSA
Barrow County, GA
Butts County, GA
Cherokee County, GA
Clayton County, GA
Cobb County, GA
Coweta County, GA
De Kalb County, GA
Douglas County, GA
Fayette County, GA
Forsyth County, GA
Fulton County, GA
Gwtnnett County, GA
Henry County, GA
Newton County, GA
Paulding County, GA
Rockdale County, GA
Spalding County, GA
Walton County, GA
12. BATON ROUGE CMSA
Ascension Parish, LA
East Baton Rouge Parish, LA
Livingston Parish, LA
West Baton Rouge Parish, LA
13. CLEVELAND CMSA
Cuyahoga County, OH
Geauga County, OH
Lake County, OH
Lorain County, OH
Medina County, OH
Portage County, OH
Summit County, OH
14. EL PASO CMSA
El Paso County, TX
15. SAN JUAQUIN VALLEY AIR BASIN
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
Merced County, CA
San Juaquin County, CA
Stanislaus County, CA
Tulare County, CA
16. SOUTH COAST AIR BASIN
Los Angeles County, CA
Orange County, CA
Riverside County, CA
San Bernardino County, CA
17. MIAMI CMSA
Broward County, FL
Bade County, FL
18. MINNEAPOLIS-ST.PAUL CMSA
Anoka County, MN
Carver County, MN
Chisago County, MN
Dakota County, MN
Hennepin County, MN
Isanti County, MN
Ramsey County, MN
Scott County, MN
Washington County, MN
Wright County, MN
St. Croix County, WI
19. PROVO-OREM CMSA
Utah County, UT
20. SAN DIEGO AIR BASIN
San Diego County, CA
21. SPOKANE CMSA
Spokane County, WA
22. ST. LOUIS CMSA
Clinton County, IL
Jersey County, IL
Madison County, IL
Monroe County, IL
St. Clair County, IL
Franklin County, MO
Jefferson County, MO
St. Charles County, MO
St. Louis City, MO
St. Louis County, MO
23. WASHINGTON, DC CMSA
District of Columbia
Calvert County, MD
Charles County, MD
Frederick County, MD
Montgomery County, MD
Prince George's County, MD
Alexandria City, VA
Arlington County, VA
Fairfax City, VA
Fairfax County, VA
Falls Church City, VA
Loudoun County, VA
Manassas City, VA
Manassas Park City, VA
Prince William County, VA
Stafford County, VA
24. SPRINGFIELD NECMA
Hampden County, MA
Hampshire County, MA
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analysis, 4) the characterization of average load factors, annual hours of
use, horsepower, and other attributes of each equipment type's engine(s), and
5) the development of activity indices used to allocate equipment/engine
populations to the nonattainment area level.
Section 2 of this report identifies the equipment to be considered, classifies
"similar" equipment into equipment categories, and defines engine categories
useful for an emissions analysis. Section 3 describes the data sources used
in this analysis. EEA has contracted Power Systems Research, Inc. (FSR) to
provide data on engine characteristics, usage, and equipment populations at
the state and national levels. Equipment populations actually reflect engine
populations, since some equipment have more than one engine. In this
'analysis, the term 'equipment populations' refers to engine populations. A
presentation of FSR's methodology is also given in this section.
Section 4 derives EEA's methodology to distribute equipment populations to the
nonattainment areas. Finally, Section 5 presents results of the methodology
and all other data necessary to calculate emission inventories from nonroad
equipment (except for emission factors).
1-4
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2. EQUIPMENT AMD ENGINE CLASSIFICATION SCHEMES
2.1 Overview
The term "nonroad equipment" covers an exceptionally wide variety of equipment
types and engines. An "equipment type" refers to individual pieces of
machinery used in different applications. For example, a crawler tractor is
an equipment type used in construction, while a lawnmower is an equipment type
used in lawn and garden applications. For the purpose of an emissions
analysis, however, only those equipment types that use internal combustion
engines are applicable. Therefore, to accurately assess the emission
contributions of nonroad equipment it is first necessary to identify
equipment types and the physical characteristics of their engines.
The methodology to estimate equipment type populations at the local level uses
PSR's data on equipment populations. Seventy-eight equipment types1, ranging
from chainsaws with small engines (less than 7 horsepower) to excavators with
large engines (over 150 horsepower), were identified from PSR's engine
applications list as relevant to the study. These equipment are presented in
Table 2-1.
With such a wide variety, there is a need to classify equipment so that
equipment types with similar engines, uses, or operating characteristics can
be examined as a group, or as an "equipment category". For instance,
lawnmowers, chainsaws, shredders, riding mowers, and trimmers are generally
used in lawn and garden applications and have similar engines (at least across
fuel type). Likewise, asphalt pavers, rollers, compactors, trenchers, crawler
tractors, etc. are used in construction. To account for similarities in
application, nine equipment categories were defined.
1 There are really more than 78 equipment types relevant to this study •
some are included along with others in one set of reported population, annual
hours of use, load factors, etc. (e.g., other lawn & garden equipment includes
augers, sickel bar mowers, and other equipment - Appendix A)
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Table 2-1
Equipment Types Included
In the Study
2-Wheel Tractors
Aerial Lifts
Agricultural Mowers
Agricultural Tractors
Air Compressors <50 HP
Aircraft Support Equipment
All Terrain Vehicles (ATVs)
Asphalt Pavers
Balers
Bore/Drill Rigs
Cement and Mortar Mixers
Chainsaws <4 HP
Chainsaws >4 HP
Chippers/Stump Grinders
Combines
Commercial Turf Equipment
Concrete Pavers
Concrete/Industrial Saws
Cranes
Crawler Tractors
Crushing/Proc. Equipment
Dumpers/Tenders
Excavators
Fellers/Bunchers
Forklifts
Front Mowers
Gas Compressors S HP
Signal Boards
Skid Steer Loaders
Skidders
Snowblowers
Snowmobiles
Specialty Vehicles Carts
Sprayers
Surfacing Equipment
Swathers
Sweepers/Scrubbers
Tampers/Rammers
Terminal Tractors
Tillers <5 HP
Tillers >5 HP
Tractors/Loaders/Backhoes
Trenchers
Trimmers/Edgers/Brush Cutters
Vessels w/Inboard Engines
Vessels w/Outboard Engines
Vessels w/Stemdrive Engines
Welders <50 HP
Wood Splitters
2-2
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The use of equipment categories greatly simplifies Che estimation of equipment
populations at the county level, since equipment in each category can be
characterized by the same activity indicator(s) necessary to allocate
equipment populations to the nonattainment area level. Moreover, individual
equipment categories also tend to be dominated by one or two types of engines
(e.g., 2-stroke gasoline engines are predominant in recreational vehicles). A
strategy that defines emissions control categories by equipment type does not
correlate well with engine type and use. As a result, it becomes impractical
to track inventory at the equipment type level. Rather, the analytical
methodology tracks engine sales, as each equipment category uses a set of'
engines from a much smaller group of manufacturers. Fi"e categories of engine
types, with possible sub-categories, useful for an emissions analysis were
\
developed. '
• >
2.2 Equipment Categories
The types of equipment presented in Table 2-1 have been grouped into nine
categories based on intended use. The nine equipment categories and the types
of equipment that are contained in each are listed in Appendix A. An overview
of each of the categories is given below, while the types of engines used in
each category are discussed in Section 2.3.
Category 1 is defined as Lawn and Garden, which includes 14 equipment types.
This category is mostly comprised of small (less than 25 horsepower) gasoline
powered machines (such as lawnoowers, trimmers/edgers/brush cutters, and
chainsaws). Only chippers/grinders, rear engine riding mowers, wood
splitters, commercial turf equipment, and PSR's 'other lawn & garden
equipment' (a catch-all category) have some diesel populations. Commercial
turf equipment includes those equipment primarily used in commercial
applications, such as multi-spindle walk-behind mowers and riding turf mowers.
PSR's data only includes front mowers used in residential applications,
although they are also used in commercial applications. Chippers/grinders are
often used by municipalities and landscaping services, and operate with
engines typically rated above 50 horsepower. While most of the equipment
2-3
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included in the Lawn and Garden category are used throughout the nation,
snowbLowers are only used in areas where snowfall is heavy in the winter
season. Therefore, distribution of the national snowblower population will
only take place in those nonattainment areas where snowfall takes place.
Category 2 is defined as Airport Service Equipment. Various airports within
the Washington, D.C. area were contacted to determine what type of equipment
should be included in this category. Airport equipment shown in Appendix A
are generally owned by individual airlines, while the airport itself owns snow
removal equipment, construction equipment, lawn and garden equipment, and on-
road equipment (i.e., fire fighting vehicles, dump trucks, cars, vans, and
pick-up trucks). To avoid double counting of equipment across categories,
airport owned equipment are implicitly accounted for in other equipment
categories, such as Construction Equipment or Lawn and Garden Equipment. Only
equipment owned by the airlines are included in Category 2. Note that PSR
uses aircraft support equipment to capture individual equipment used in
airports, such as de-icers, ground power units, and baggage conveyors.
Although the engines that drive these equipment do not have homogeneous
physical and operational characteristics, their populations will scale with
the same activity indicator.
Category 3 is defined as Recreational Equipment and includes ATVs, minibikes,
nonroad motorcycles, golf carts, snowmobiles, and specialty vehicles/carts.
Golf carts are generally found in resorts and recreation facilities, such as
golf courses. Many golf carts are powered by electricity rather than
gasoline, but PSR's data includes only those that are powered by gasoline.
All other equipment in Category 3, except for specialty vehicles/carts, are
exclusively powered by gasoline engines. Specialty vehicles/carts include
snow grooming equipment, ice maintenance equipment, go-carts, industrial ATVs,
personnel carriers, and other equipment. Although much of the equipment
included under specialty vehicles/carts is used for recreational purposes
(such as, go-carts, snow grooming equipment and ice resurfacing machines),
other equipment (such as, industrial ATVs and personnel carriers) are not. Ic
2-4
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was decided Co include specialty vehicles/cares in Che Recreational Equipment
category* because chere was no objective way co account for the population of
go-carts, which is expected to be high relative to other equipment. Note,
also, thac snowmobiles are concencrated in areas with heavy annual snowfall.
Recreational watercraft are included in a separate category - Marine Equipment
(Category 4). Category 4 includes vessels with inboard engines, vessels with
outboard engines, vessels with sterndrive engines, sailboat auxiliary inboard
engines, and sailboat auxiliary outboard engines. Personal watercraft (e.g.,
jet skis) have inboard engines, and are included in the inboard engines
equipment type. Personal watercraft were to be separated from inboard engines
to account for their unique operational and physical characteristics.
However, data specifying average horsepower, load factor, annual usage,
population, and 2-stroke/4-stroke split was not available for personal
watercraft.
Categories 5 and 6 are defined as Light Commercial and Industrial Equipment,
respectively. Light Commercial Equipment includes equipment with engines
below 50 horsepower used in various manufacturing, retailing, and wholesaling
applications. For example, small air compressors are often found in auto
repair shops, while welders are used by metalworking shops. Gas compressors,
on the other hand, are used to propel natural gas through pipelines. In
contrast, Industrial Equipment includes equipment mostly used in manufacturing
and warehousing applications, although forklifts are also used in agricultural
applications. PSR defines forklifts to include cushion and pneumatic tired
forklifts and agricultural forklifts (e.g., GEHL). Rough terrain forklifts
are included under the Construction Equipment category.
Category 7 is defined as Construction Equipment and includes 27 equipment
types. This equipment category is the most diverse in terms of the
characteristics of each equipment type. Construction equipment had been
originally separated into two sub-categories: road construction equipment
(such as asphalt pavers, rollers, and scrapers) and general construction
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equipment (such as cranes, crawler tractors, and off-highway trucks). Sub-
categories were deemed inappropriate, however, since they did not strengthen
the statistical results of the analysis. Construction equipment population
estimates at the local level responded best to total construction activity,
irrespective of application. Therefore, activity indicators specifying road
or general construction activity were not necessary, and the use of sub-
categories became redundant. In addition, the use of sub-categories to
characterize construction equipment by application was a major concern of EMI,
since many equipment are used in different construction applications. As a
result, EMI's concern was that application specific activity indicators may
bias county level population estimates for construction equipment.
Category 8 is defined as Agricultural Equipaent and is comprised of 11
equipment types, including one define as 'other agricultural equipment' to
account for specialized cultivating and harvesting equipment (e.g., cotton
strippers and cotton pickers). Specialized equipment were to be distributed
solely to those areas in the country where the equipment was used. For
example, cotton strippers are not used on farms in the northern part of the
U.S., and should not be distributed to nonattainment 'areas in the north. PSR
does not have data that separates out these low sales volume equipment types,
and the distribution of specific equipment to areas where such equipment may
not be used became inevitable. For the purpose of emissions inventory
calculations, however, this does not present a major problem since the
populations of such equipment are relatively low when compared to other
agricultural equipment.
The final category of nonroad equipment is defined as Logging (Category 9).
This category includes those chainsaws and shredders that are powered by
engines rated above 5 horsepower, skidders, and forest products (i.e.,
delimbers, fellers/bunchers, and other miscellaneous equipment). Logging
equipment will only be distributed to those areas where logging activity takes
place (e.g., nonattainment areas in Washington state).
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2.3 Engine Categories
Five categories of engine types with some possible sub-categories useful for
an emissions analysis were developed. These categories are defined as
Utility, Light Commercial, Heavy-duty, Recre»tlonal, and Marine.
Table 2-2 provides information about the engine categories that have been
defined and how they map to the equipment categories discussed in Section 2.2.
Utility engines are commonly lightweight, air-cooled, gasoline engines having
an aluminum block. They are either 2-stroke or 4-stroke, and used in lawn and
garden and light commercial applications. Recreational engines share similar
characteristics of Utility engines, but are rated at higher RPMs.
Recreational engines are used by ATV's, nonroad motorcycles, and other
recreational equipment. ,
Light Commercial engines, on the other hand, have a cast iron block, and
cooling can be through either water or air. These engines are built for
continuous use at rated horsepower, and are employed by light agricultural,
construction, and industrial equipment.
Heavy-duty engines are almost all powered by diesel, 4-stroke engines, and can
be either water or air cooled. The useful life of Heavy-duty engines is
usually greater than 5000 hours. These engines are mostly used by heavier
construction and agriculture equipment.
Different marine vessels use different engines. Vessels with inboard engines
have diesel or gasoline engines that are 4-stroke and water cooled. On the
other hand, vessels with outboard engines have engines that are 2-stroke.
Table 2-2 presents a general classification scheme for nonroad engines and how
engines map with equipment types. However, for the purpose of an emissions
analysis a more detailed engine classification scheme may be appropriate.
Potential engine categories and their attributes are presented in Table 2-3.
As can be seen, Heavy-duty engines have been disaggregated into several sub-
1 .7
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categories co account for different engine specifications and usage. These
subcategories and Light Commercial engines make up what can be referred to as
commercial engines. While gasoline engines are common in the Light Commercial
category, the Heavy-duty sub-categories are composed almost exclusively of
diesel engines. Horsepower ranges of commercial diesel engines are accounted
for by each category. Light Commercial includes diesel engines rated co 50
horsepower, while Heavy-duty includes diesel engines rated above SO
horsepower. The horsepower range of gasoline commercial engines is similarly
represented by this classification scheme.
2-8
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TABLE 2-2
ENGINE CATEGORIZATION
Category
Utility
Enqlnt Characteristics
Lightweight, Alualnui
head/block, Air-cooltd, ill
gasoline (2/4 stroke) Rated
RPH at 2800-3600 L1i1ted use-
ful lift <2000 hrs. HP < 25
Equipment Types
Lawn and Gardan, Light
Shop Equipment
Light
Cogntrciil
Haavv Duty
(All Sub-
categories)
Recreational
Marine
Rugged Construction, Cast Iron
Block, Air/water Cooled, Gas
and Diesel Rated RPN at 2800-
3600' Built for continuous
use at rated HP
HP < 40 to SO dlesel; <80 gas
Useful life > 2000, <4000 hrs.
AlMst all dlesel, 4-stroko
HP > 45
A1r/Mater cooled
Useful life > 5000 hrs.
Lightweight, A1u»1nu«,
Air/water cooled, 2/4 stroke
Rated RPN > 5000
L1i1ted useful life <2000 hrs.
2-stroke wtter cooled, Rated
RPN over 5000. L1sited useful
life <2000 hours
4-stroke, gas or dlesel water
cooled. Rated RPN at 4000 or
below. HP >150.
Useful life - 4000 hrs
Light agricultural,
light construction,
Portable Generator Sets,
Industrial Equipment
Estate maintenance
Public Utility
Construction
Agriculture
Public Utility
Snowmobl1e
Off Road m/c.
Personal watercraft,
marine vessels with
outboard engines.
Inboard
2-9
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TABLE 2-3
POTENTIAL ENGINE CLASSIFICATIONS DEFINITIONS
Category Fuel Type HP Range
Lawn and Garden Gasoline 0-25
Handheld Utility Gasoline (2-scroke)
Light-Duty Gasoline 15-80
(NGV/Propane)
Diesel 0-50
Light-Heavy Gasoline* 80-175
Diesel 50-175
Heavy-Duty Diesel 175+
Recreational Gasoline All
Marine Gasoline 0-250
Approximate
Rated RPM Useful Lifr-
<4000 300 - 500 hr
N/A 300 hrs.
<4000 1000 - 2000 h
<4000 2000-3000 hrs
<3000 4000 hrs.
<3000 6000 hrs.
<2500 8000 hrs.
>4000 <1000 hrs.
N/A N/A
Very limited sales.
2-10
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3. DATA SOURCES
3.1 Overview
To estimate the emissions from nonroad engines, it is first necessary to
determine the distribution of these equipment across relevant geographical
areas. Since the goal is to assess the contribution of nonroad equipment to
the emission inventories of nonattainment areas, the geographical areas that
are of interest are counties. Therefore, it is necessary to distribute
equipment populations to the county level. Once population estimates have
been derived at the county level, data on load factors, annual hours of use,
horsepower, and emission factors by engine category can be used to estimate
emissions.
3.2 Pover Systems Research
The principal source of national and state equipment population, annual hours
of use, load factor, horsepower, 2-cycle/4-cycle distributions, and LPG/CNG
penetrations data is Power Systems Research, Inc. (PSR). PSR is a global
market research and consulting company which provides information to
organizations in the power equipment industry; such as, engine and component
manufacturers, original equipment manufacturers, government agencies, and
financial institutions.
PSR's methodology to estimate equipment populations at the national level
relies on various sources including: dealers, product literature, the annual
reports of equipment manufacturers, and publications that provide periodic
equipment shipments. The basis for PSR's sales data, however, is through
continuous contact with original equipment manufacturers (OEMs) who provide
PSR with annual engine installations. From product literature, PSR identifies
which engines go in which equipment types, and hence estimates annual engine
sales at the national level. PSR then utilizes engine life data as well as
data on annual hours of use to derive a statistical scrappage curve and
estimate national populations based on sales. For each of the more than 1,300
•».
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engines included in their North American Engine Altermarke-c database, and
utilized in North American produced equipment and imported equipment, PSR has
included a mean lifetime expectation. This mean lifetime expectation is not
an official or documented expectation as provided by the manufacturer, but
rather the result of PSR's continuing field research regarding engine
applications in field use. Lifetime data was derived from PSR's major survey
of over 40,000 owner/operators across the country (conducted in 1986) and is
updated periodically using information from product literature and
manufacturers. The survey questionnaire is exhibited in Appendix 5.
PSR's mean lifetime expectancy is defined as the point in each engine model's
life (expressed in hours) at which the engine, when operated at full load,
would find SOX of engines originally placed in service still in operation.
Many factors contribute to variations from this norm and these are accounted
for in PSR's calculations of lifetime attrition, but the major variable is
load factor.
In order to reasonably account for the way in which engines wear out in real
life, PSR assumes that engines are consumed as a function of the time over
which they operate and the load which they carry over that time. To establish
a relationship between life expectancy and load/annual hours of operation,
each discrete application for diesel and gasoline engines is evaluated upon
typical annual hours of operation and the load factor normally experienced in
that application. The load factor is the average operating level in a given
application as a percent of the manufacturer's maximum horsepower rating. PSR
uses fuel consumption rates, obtained from their survey of over 40,000
owner/operators, versus time to calculate the load factor that is normally
experienced. For each engine in the survey, PSR calculates load factor as
follows:
Load Factor • Actual Fuel Consumption/Hour
Rated Fuel Consumption/Hour at Rated Horsepower.
3-2
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This survey is also used to estimate the average annual hours of operation by
application. Load factor and annual hours of use define the duty cycle which
is experienced in each application. The utilization rates (in terms of annual
hours) may change from year to year, sometimes having a significant effect on
population changes. Moreover, PSR maintains usage data for six regions in che
nation (north-east, south-east, south-west, north-west, north-central, and
south-central) to account for regional and seasonal variations. PSR
determines usage factors for each region through analysis of the appropriate
survey responses. For example, all responses from the south- west region of
the nation are separated out and usage profiles are calculated independently.
These region specific factors are then compared to national computations to
specify deviation from the national norm. This process is conducted for each
of the six regions. In this manner, differences between usage factors across
the regions and differences between a region and the nation are determined.
In PSR's attrition calculation, each engine in a specific application is
presumed to have operated for the average number of hours at the average load
factor. This combination (load factor x annual hours) is multiplied by the
rated horsepower of the engine. The engine is then presumed to have consumed
the resulting number of horsepower hours in the year specified. The result is
the percentage amount of engine life which has been consumed during a single
year. This is then multiplied by the number of years the engine has been in
operation. This percent of engine life is then compared to the distribution
scale shown in Figure 3-1. Following the engine attrition curve in Figure 3-1
demonstrates that at the point at which total use equals engine life, SOX of
engines will no longer be in operation. The end point of this curve - that ac
which no engines remain in service • was arbitrarily cut off at two times
expected engine life, although a few engines may survive beyond that point.
The engine attrition curve should take into account the variations in
maintenance, accidental failures, and variations around the norm in terms of
engine quality and performance. The curve has been adjusted over years of
research to assure a best fit against real life experience for reciprocating
engines. PSR continues to examine this correlation on an annual basis. For
3-3
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FIGURE 3-1
TYPICAL ENQIN1 ATTRITION CURVE
%0»UMTS
3-4
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each engine in Che database, Che calculation is made for each of the
applicacions in which it operates. This is compared co che number of years
for which che engine has been in operation. In chis manner che percentage of
engines remaining in operation is calculated. For example, assume chat 500 of
a specific engine model were placed into service in forklifts 6 years ago,
that the annual hours of operation are 600, chat che load faccor is SOX, and
Chat che engines have an average expected lifecime of 2,000 hours, or a
maximum engine life of 4,000 hours, based on che cutoff of cwo times expected
life. A 70 horsepower engine will consume 21,000 horsepower hours per year.
For a period of 6 years 126,000 horsepower hours in each of chese engines will
have been ucilized, translating into 90X of expected engine life - since
expected engine life is 140,000 horsepower hours (i.e., 2,000 hours x 100X x
70 horsepower). Therefore, the expected life of the engine not yet consumed
is 10X. Matching this to the curve in Figure 3-1, 72X of the original 500
engines (360 engines) will still be in service. The same process is performed
for engines that were placed in service 5 years ago, 4 years ago, 3 years ago,
and so on, so that a summation of the total units estimated to be in operation
provides che Cocal population for forklifts with this specific engine model.
In general, when these calculations have been made, PSR is able to arrive ac a
reasonable representation of the remaining service population for a given
equipment/engine type.
3.3 Other Data Sources
In order to validate the PSR population data, county or state level population
daca was collected from relevant crade associations, manufacturers, and state
and local governments. For example, state specific populations of marine
equipment are available from a state's Department of Natural Resources (or
equivalent), since states must provide population profiles for recreational
watercraft to the U.S. Coast Guard. Twenty-three scates relevant to the study
were contacted and each maintains population profiles by length of vessel and
propulsion type (i.e., inboard, outboard, sterndrive, and sailboat inboard and
outboard auxiliary engines). As a result, Marine Equipment populations for
3-5
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each of the 24 nonattainment areas ax® -eaKSfcaatfewfi vafUmft tt&ase data, since PSR's
state level estimates are expected to 8s® leas radiatels Chan registration data.
Data from trade associations describing equipment populations for the
nonattainment areas was submitted to EPA. Specifically, OPEI, EMI, and ITA
presented data for some Lawn and Garden, Construction, Agricultural, and
Industrial Equipment. These data were used to check population estimates at
the nonattainment area level, or PSR's national population estimates.
However, a comparison analysis of data sources is beyond the scope of this
report, and no association data on equipment populations, annual hours of use,
or load factors is presented.
Where county level data is not available, a methodology that relies on
activity indicators was derived to distribute state equipment populations by
equipment category to the county level. Data on activity indicators has been
collected from various sources including the following census publications:
County Business Patterns (1988), County and City Databook (1988), and Economic
Census (1987). The County Business Patterns is an annual series that includes
a separate report for each state, the District of Columbia, Puerto Rico, and a
U.S. summary. Each report presents state and county level mid March
employment, first quarter and annual payrolls, total number of establishments,
and number of establishments by employment-size category. The data are
tabulated by industry as defined in the Standard Industrial Classification
Manual (i.e., by SIC code). Most of the economic divisions of the Nation's
economy are covered in these reports, including: agricultural services,
mining, construction, manufacturing, transportation, public utilities,
wholesale trade, retail trade, finance, insurance, real estate, and services.
The County and City Databook provides statistics at the state, county, and
city level on housing, income, agriculture, manufacturing, construction, and
other activities. Finally, the Economic Census provides detailed data on all
economic sectors in the country.
3-6
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4. METHODOLOGY TO ESTIMATE EQUIPMENT POPULATIONS
AT THE NONATTAINMENT AREA LEVEL
4. 1 Description of Methodology
The methodology co distribute equipment populations to the nonattainmenc areas
make use of activity indicators and state level populations for equipment
categories. State level populations for each equipment category are derived
from PSR, while activity indicators are determined from economic data
presented in various census publications.
In the methodology, the relationship between specific activity indicators and
an equipment category's state population is determined by regression analysis.
In general, the model is formulated as follows:
STPOP14 - ba + b^Al!) + b2*(AI2) * ... * bn*(Aln),
where, STPOP is state i's population of equipment category j and AI: through
Aln are the activity indicators for the equipment category at the state level.
The estimated coefficients will provide the activity indices for each activity
indicator, and are defined as b*k for k - 1, 2 ..... n.
Given the statistical relationship between equipment catagory j's population
and the activity indicators (AIk for k - 1, 2, . . . , n) , nonattainment area
populations can be estimated by using activity indicators for those counties
in the nonattainment area as follows:
- b*0 + bV(Ali) * bV(AI2> + • • • + b'B*(AlB) .
where, NONARtJ is nonattainment area t's estimated population of equipment
category j , b*k are the estimated activity indices and Alk are now the
activity indicators for nonattainment area t (i.e., the sum of activity Ln the
counties of nonattainment area t) , for k - 1, 2 ..... n.
-------
Finally, the estimate for NONAR^ must be adjusted to reflect prediction error
at area t's state level. Let ADNONAR^ be area t's adjusted population of
equipment category j. Then ADNONAR^ is defined as:
ADNONARtJ - NONARtj * r,
where, r - actual STPOPU _ .
predicted STPOPU
For nonattainment areas that span more than one state, r is simply the
arithmetic average of the estimation error for each relevant state.
Given ADNONARtj, a population estimate for each equipment type in category j
can be found by applying the ratio of that type's national population relative
to the national population of category j. In this manner, when these
fractions are multiplied by ADNONARtj the results are population estimates for
each equipment type included in the study. Similarly, applying national
fractions for gasoline and diesel result with independent population estimates
by fuel type, for each equipment type, at the nonattainment level.
There is the possibility that the relationship between the activity indicators
and the state populations for category j may not be statistically significant.
In this analysis, only the regressions for Logging Equipment (Category 9) did
not demonstrate statistical significance, and a backup methodology was
necessary to distribute national populations of Logging equipment to each
nonattainment area. In general, this back-up methodology can be described as
follows. First, the relative activity indicator between a specific county and
the nation is calculated as:
county specific activity indicator 1 - xj
' national activity indicator 1
county specific activity indicator 2 - xz
national activity indicator 2
county specific activity indicator n -
national activity indicator n
-------
Second, xlt x2 x,, are weighted by the percentage share of equipment
whose population is characterized be each indicator. These percentage shares
can be determined by anecdotal information obtained from manufacturers and
retailers of the equipment in question, as well as from data maintained by
PSR's survey of approximately 40,000 owner/operators throughout the nation.
Finally, the weighted sun of x1( xz x,, provides the activity index for
the given equipment category. This index is then multiplied by the national
populations of each equipment type in the category to estimate the population
of the equipment type in county t. Of course, this back-up methodology
becomes very straight forward if only one indicator is needed to allocate
national populations to the county level.
It is important to note' that the methodologies described above allow for
discriminatory allocation of equipment across geographical areas. For
example, this becomes necessary in the case of Lawn and Garden Equipment since
snowblowers are not used in areas where snow fall does not occur.
Specifically, once the activity index is determined for an equipment category.
the distribution of national equipment populations to the county level is an
independent process, as each equipment type is distributed separately.
The remainder of this section provides population estimates derived by the
above methodologies. For each equipment category, many regression models
employing different activity indicators were tested to determine the "best"
model to estimate equipment populations at the nonattainment area level. EEA
used three criteria to determine the "best" model:
• The model had to have a SQUARED MULTIPLE R (i.e., R2) of
greater than 0.8, allowing for a maximum of two outliers.
(SQUARED MULTIPLE R denotes the proportion of variance in the
dependent variable accounted for by the predictor.)
-------
• The model's constant term could no^g be significantly different
from zero at a 95X confidence level (i.e., accept the null
hypothesis that the constant is not significantly different
from zero), while the model's coefficient(s) had to be
significantly different from zero at a 95X confidence level
(i.e., reject the null hypothesis that the coefficient(s) of
the predictor(s) is not significantly different from zero).
• If more than one model met these criteria, the one that made
use of the more intuitive indicator was used.
What follows is a detailed discussion of the activity indicators that were
used and the regression results for each equipment category's "best" model.
4.2 Category 1: Lavn and Garden Equipment
In general, Lawn and Garden Equipment are used by households living in a
single family housing unit where such equipment have use-value and by
landscaping companies that provide lawn and gardening services to apartment
complexes, office buildings, and households. Single family housing units can
be defined as suburban-type detached 'one-family' homes typical to all
metropolitan areas. Such a definition implicitly describes housing units chat
will most likely have lawn areas where Lawn and Garden Equipment can be
applied. On the other hand, landscaping services have become a growing
industry in recent years. While such services have been traditionally
employed by office complexes and apartment buildings, smaller landscaping
companies have sprung up that provide services to suburban households.
Two activity indicators were used to distribute Lawn and Garden Equipment from
the state level to the nonattainment area level. First, the number of single
family housing units in a given area provides an estimate of the number of
Lavn and Garden Equipment that may be owned by households in that area.
Second, some households may use landscaping services and, thus, not own any
Lawn and Garden Equipment, while apartment complexes and office buildings may
rely on landscaping companies to service their lawn and garden needs. To
account for equipment owned by landscaping companies, SIC 078 • Landscape and
Horticultural Services (Employees) • was used as a complementary indicator to
4-4
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distribute equipment to the county level.
The model that met the three criteria stipulated above was a multivariate
model with both indicators as the independent variables and PSR's population
of Category 1 equipment as the dependent variable. The regression results are
presented in Table 4-1. The condition indices in Table 4-1 are the square
roots of the ratio of the largest eigenvalue to each successive eigenvalue. A
condition index greater than 15 indicates a possible problem with
collinearity, while one greater than 30 indicates a serious problem. Although
the two indicators for Category 1 are highly correlated (as shown by the
correlation matrix of regression coefficients), the low condition indices
indicate that a collinearity problem is not present and, thus, the predictor
variables do not comprise a redundant set.
4.3 Category 2: Airport Service Equipment
Appendix A demonstrates the equipment types that are included in the Airport
Service Equipment category. Aircraft support equipment includes aircraft load
lifters, baggage conveyors, de-icing equipment, ground power units, and other
equipment used by airlines in the major airports throughout the country. It
was initially planned to derive populations for each equipment type
separately. However, since PSR does not provide data at a greater level of
detail, estimates of nonattainment area populations were developed for the
general equipment types defined by PSR. This was also true for terminal
tractors, which include push-back and tow tractors.
Generally speaking, airport operators own those equipment that are required
for the maintenance of the airport's physical plant, while airlines own the
specialty equipment that is used to service the aircraft (i.e., the equipment
listed under Category 2). Most of the airport owned equipment is registered
for on-road use. As noted in Section 2, Airport owned nonroad equipment, on
the other hand, is included in the other equipment categories described in
Appendix A.
-------
TABLE 4-1
Clua I: Uvn «nd G«rd«n EquipMne
*B««e* Model
MODEL: PSRCLSl - « > b(SINHOM) + c(EMP078)
PSRCLS1 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 1 (xlOOO)
SINHOM - NUMBER OF SINGLE FAMILY HOMES IN A STATE (xlOOO)
EMP078 - SIC 078 (EMPLOYEES) - LANDSCAPING AND HORTICULTURAL SERVICES (xlOOO)
EIGENVALUES OF UNIT SCALED X'X
I
CONDITION INDICES
2.607524
1
1.000000
0.351239
2
2.724665
0.041237
3
7.951883
VARIANCE PROPORTIONS
CONSTANT
SINHOM
EMP078
0.033516
0.009615
0.013726
0.543053
0.006082
0.098443
0.423431
0.984303
0.887832
OEP VAR: PSRCLSl N: 23 MULTIPLE R: .987 SQUARED MULTIPLE R: .974
ADJUSTED SQUARED MULTIPLE R: .971 STANDARD ERROR OF ESTIMATE: 525.073022
VARIABLE
CONSTANT
SINHOM
EMP078
COEFFICIENT
•206.945392
1.205430
173.441621
STD ERROR
201.375597
0.196926
28.160237
STD COEF TOLERANCE
P(2 TAIL)
0.000000 . -1.02766 0.31638
0.504833 0.19204 6.12122 0.00001
0.507957 0.19204 6.15910 0.00001
CORRELATION MATRIX OF REGRESSION COEFFICIENTS
CONSTANT SINHOM EMP078
CONSTANT
SINHOM
EMP078
1.000000
-0.68510*
0.403371
1.000000
•0.898867
1.000000
ANALYSIS OF VARIANCE
SOURCE SUM-OF-SQUARES OF MEAN-SQUARE F-RAT10
REGRESSION .20S558E+09 2 .102779E+09 372.790208
RESIDUAL .551403E+07 20 .275702E+06
0.000002
4-6
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Given chac Category 2 includes equipment owned and operated by airlines, air
carrier operations was chosen as the activity indicator to distribute PSR's
national equipment populations to each nonattainment area in the study. The
commercial system of air transportation, consisting of the certificated air
carriers, air taxis (including commuters), supplemental air carriers,
commercial operators of large aircraft, and air travel clubs, is defined as
air carrier operations. It is, therefore, reasonable to assume that most
Airport Service Equipment (as defined in Category 2) will be used during air
carrier operations. Data on air carriers' operations from FAA's Air Traffic
Activity (FY 1989) was used to distribute Airport Service Equipment from the
national level to each nonattainment area.
The methodology that EEA employed to estimate nonattainment area populations
of Category 2 equipment consisted of the following steps:
• Identify airports within each nonattainment area that support
air carrier operations,
• Determine itinerant air carrier operations for each relevant
airport within the nonattainment area from FAA's Air Traffic
Activity (FY 1989) publication,
• Sum the activity for each airport in a nonattainment area to
obtain total activity in that nonattainment area,
• Use the statistical relationship resulting from regression
analysis at the state level.
Table 4-2 lists those airports that support air carrier operations by
nonattainment area. Note that Provo-Orem CMSA and Springfield NECMA do not
have air carrier airports. The Provo-Orem CMSA area is serviced by Salt Lake
City's international airport, while the Springfield NECMA area is serviced
mostly by Windsor Locks - Hartford's major airport. Table 4-2 also shows each
airport's total operations and air carrier operations. As expected those
airports serving the largest cities have the greatest amount of air carrier
operations (such as, Atlanta International, Los Angeles International, Logan
Airport, and airports in the New York and Houston areas). One would,
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TABLE 4-2
Major Air Carrier Airports by
Non- Attainment Area
(Itinerant Operations)
ion -Attainment Area
Baltimore CMSA
Chicago CMSA
Oenver CMSA
Houston CMSA
Milwaukee CMSA
Boston NECMA
Hartford NECMA
New York CMSA
Philadelphia CMSA
Suttla-TacoM CMSA
Atlanta CMSA
Baton Rouge CMSA
Cleveland CMSA
£1 Paso CMSA
San Juaquin Vallty Atr Basin
South Coast Air Basin
Miami CMSA
Mlnneapolls-St.Paul CMSA
Provo-Oraw CMSA
San Oiego Atr Basin
Spokant CMSA
St. Louis CMSA
Washington. OC CMSA
Springfield NECMA
Airports
Bal tt more/Washington Intarnattonal
Chicago Midway
O'Hart Intarnational
Stapleton International
Hobby Airport
Houston Intercontinental
Mitchell Airport
Logan Airport
Windsor Locks
John F. Kennedy Airport
LaGuardia Airport
Philadelphia International
Seattle Boeing
Seattle-TacoM International
Atlanta International
Baton Rouge Ryan Field
Ne» Orleans Mot sent
Cleveland Hopkins International
El Paso International
Bakersfleld Meadon
Fresno Atr Terminal
Long Beech Airport
Lo* Angeles International
Santa Ana/Orange County
Mlajri Internet tonal
Mlnneapolts-St.Paul Intem'l
No Airport in timed* ate Area
Lindburg Airport
Spokane) International
St. Louis International
OuUes Airport
Washington National
No Airport in Iimediate Area
Total
Coerations
305.537
315, 583
739.384
168.286
254.887
294.001
178.922
417,111
168.360
336.731
355,568
383.279
252.387
327.027
668.350
36.931
138.659
254.874
137,038
102,233
196.015
267.296
626.874
336.498
378.257
364.530
-
206,934
102.216
425.257
232,915
316.138
-
Air Carrier
Operations
158,792
124,258
620,090
323,165
116.274
207,163
73,655
239.281
65,217
220,467
262,784
131,343
7,934
130.145
478.290
18,890
39,949
139,252
55.435
2.014
13,200
20,048
427.419
62.302
247.356
230,656
-
134,704
27.609
283.436
132.722
135.580
«
Source: FAA Air Traffic Activity. FY 1989
4.8
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therefore, expect that these airports would have more Airport Service
Equipment than other smaller airports.
Table 4-3 demonstrates the statistical relationship between PSR's state level
populations for Airport Service Equipment and state level itinerant air
carrier operations. It is clear that the regression statistics satisfy che
three criteria stipulated in the memorandum. Utilizing the nonattainment
level air carrier itinerant operations into the model provides reliable
estimates of in-use populations for these areas. The reliability of these
estimates is depicted in Figure 4-1 which shows the scatter plot between the
variables used in the regression model and the regression line. The three
biggest outliers are California. Ohio, and Florida, but eliminating them from
the regression only increases the R2 to 0.84.
4.4 Category 3: Recreational Equipment
Determining an activity indicator for Recreational Equipment proved to be
difficult at first. Many general indicators (such as, per capita income,
population density, and percent of land that is public) were tested, but no
significant statistical relationships were found. With the premise that
supply indicates demand, EEA tested the statistical power of SIC 557 -
Motorcycle Dealers (Establishments) • in predicting Recreational Equipment
populations. The results are shown in Table 4-4. It is clear that the model
meets the first two criteria for "best" model. Realizing that most motorcycle
dealers also sell ATVs, nonroad motorcycles, minibikes, snowmobiles, and other
recreational equipment, the use of SIC 557 as the activity indicator for
Category 3 also is intuitively consistent - satisfying the third criterion.
While data for SIC 557 was available for most nonattainment areas, such data
was not available for any of the counties in the following areas: Baton Rouge
CMSA, El Paso CMSA, Provo-Orem CMSA, and Spokane CMSA. In these cases an
alternative model using SIC 55 '- Automotive Dealers and Service Stations
(Employees) - was used as a substitute. Given that SIC 557 is a subset of SIC
55, the use of SIC 55 as an indicator is not necessarily inconsistent with :he
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TABLE 4-3
2: Airport Strvica EquipMne
"B««f Modal
MODEL: PSRCLS2 - • > b(AIRPORT)
PSRCLS2 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 3 (x 1000)
AIRPORT - STATE SPECIFIC ITINERANT AIR CARRIER OPERATIONS (x 1000)
OEP VAR: PSRCLS2 N: 23 MULTIPLE R: .900 SQUARED MULTIPLE R: .809
ADJUSTED SQUARED MULTIPLE R: .300 STANDARD ERROR OF ESTIMATE: 1.019053
VARIABLE COEFFICIENT STD ERROR
CONSTANT
AIRPORT
0.460300
0.005614
STD COEF TOLERANCE
P(2 TAIL)
0.316167
0.000593
0.000000 . 1.45587 0.16022
0.899556 1.00000 9.43732 0.00000
ANALYSIS OF VARIANCE
SOURCE SUM-OF-SQUARES OF MEAN-SQUARE
F-RATIO
REGRESSION 92.489159 1 92.489159 89.062966
RESIDUAL 21.807856 21 1.038469
0.000000
4-10
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FIGURE
Airport Service Equipment
Regression Fit and Outliers
State Equipment Populations (x 1000)
CalllornU •
200
400 600 800 1000
State Itinerant Air Carrier Ops.(x 1000)
1200
1400
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TABLE 4.4
3: R«cr«*clonal Equipatnt
*B«sc" Model
MODEL: PSRCLS3 - a * b(EST557)
PSRCLS3 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 3 (xlOOO)
ESTSS7 - SIC 5S7 (ESTABLISHMENTS) - MOTORCYCLE DEALERS
OEP VAR: PSRCLS3 N: 23 MULTIPLE R: .919 SQUARED MULTIPLE R: 344
ADJUSTED SQUARED MULTIPLE R: .837 STANDARD ERROR OF ESTIMATE: 26.655695
VARIABLE COEFFICIENT STD ERROR
CONSTANT
EST557
1.760700
0.616462
STD COEF TOLERANCE
P(2 TAIL)
3.663613
0.037767
0.000000 . 0.20323 0.34091
0.918862 1.00000 .11E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.809155E+05
.149210E+03
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RAT10
1 .809135E+05 113.881062
21 710.326036
0.000000
4-12
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analytical approach, although it is less intuitive. Regression results for
this alternative model are shown in Table 4-5.
4.5 Category 4: Marine Equipment
State populations of marine vessels propelled by inboard, outboard,
sterndrive, or sailboat auxiliary engines were collected from each state
included in the study. As part of a registration program, all states annually
report to the U.S. Coast Guard the population of vessels in a report entitled
Report of Certificates of Number Issued to Boats. These reports profile
registered boats by length class (i.e., 1 to 16 ft, 16 to 26 ft, 26 to 40 ft,
40 to 65 ft, and over 65 ft) and propulsion type (i.e., inboard, outboard,
sterndrive, or sail). Some states also tabulate total boat registrations
within each county. However, since not all states do so, the nonattainment
populations of marine engines were estimated using a methodology similar to
the back-up methodology that is described earlier in Section 4-1.
All state level data by propulsion type and length of vessel were collected.
These data represent actual boat counts, rather than engine counts. To
account for the face that many of the longer vessels have more than one
engine, variou* manufacturers were contacted to acquire information as to the
expected number o£ engines by length of boat and-propulsion system- Table 4-6
presents the weighting s«Ji$ae that was used to estimate engine populations
from boat counts.1 For example, the population of engines in a given state for
boats with inboard engines between 26 and 40 ft was calculated as ((P * 0.32)
+ (2P * 0.68)), where P is defined as the state's total registrations of boacs
between 26 and 40 ft long with inboard engines. This process was performed
for all boat lengths and propulsion types, except sailboats. It is reasonable
to assume that each sailboat has one auxiliary engine, so that total sailboac
registrations in a given state represent total engine counts. Sailboat
auxiliary engines can be either outboard or inboard, and states provide
registration data for specifying auxiliary propulsion type. However, sailboa:
auxiliary inboard engines have different physical characteristics than
sailboat auxiliary outboard engines, and a distinction must be made to
4-13
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TABLE 4-5
Cl«s« 3: R«cr«*cton*l Equipacne
Ale«rruelv« Model for S«l«cc«d Hon-Act*ina«nc Ar««*
EHP55
MODEL: PSRCLS3 - a * b(EMP55)
PSRCLS3 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 3 (xlOOO)
- SIC 55 (EMPLOYEES) - AUTOMOTIVE DEALERS AND SERVICE STATIONS (xlOOO)
DEP VAR: PSRCLS3 N:
ADJUSTED SQUARED MULTIPLE
23 MULTIPLE R: .942 SQUARED MULTIPLE R: .337
.881 STANDARD ERROR OF ESTIMATE: 22.744393
VARIABLE COEFFICIENT STD ERROR
CONSTANT
EMPS5
•9.785675
1.267529
STD COEF TOLERANCE
P(2 TAIL)
7.993347
0.098899
0.000000 . -1.22423 0.23442
0.941619 1.00000 .13E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.849731E+03
.10863SE+03
ANALYSIS OF VARIANCE
OF MEAN-SQUARE F-RATIO
1 .349731E+05 164^260267
21 517.307411
P
0.000000
-------
Table 4-6
Number of Engines Per Boat: Length & Engine Type
Group
A.
B.
C.
D.
E.
Group
A.
B.
C.
D.
E.
Group
A.
B.
C.
D.
E.
Length
0-16 ft
16-26 ft
26-40 ft
40-65 ft
65 ft +
INBOARDS
Percent of
Category
100X
100X
32X
68X
100X
100X
OUTBOARDS
Length
0-16 ft
16-26 ft
26-40 ft
40-65 ft
65 ft +
Percent of
Category
100X
90X
10X
90X
10X
*
*
Number of
Engines
1
1
1
2
2
2
Number of
Engines
1
1
2
2
3
STERNDRIVE
Length
0-16 ft
16-26 ft
26-32 ft
40-65 ft
65 ft +
Percent of
Category
100X
SOX
SOX
20X
SOX
5X
95X
100X
Number of
Engines
1
1
2
1
2
2
3
3
* These two classes were determined to be barge-type vessels or "houseboats," vessels
with very limited usage levels. Therefore, they were excluded from this study.
4-15
-------
accurately assess emissions. While data for sailboat auxiliary inboard
engines regarding horsepower, 2-stroke/4-stroke and fuel type splits, average
annual hours of use, and load factors is available from PSR, such data is not
readily available for sailboat auxiliary outboard engines. As a result,
populations for each sailboat auxiliary engine type have been estimated, but
only data on engine characteristics for sailboat auxiliary inboard engines is
provided.
Having estimated the number of engines by propulsion system in a given state,
an activity indicator to distribute state engine populations to the county
level was used. This activity indicator describes the water covered surface
area of a given county relative to the water covered surface area of that
county's state. Water covered surface area includes both inland water
(expressed in square miles) and miles of public beach (an approximation of
coastline miles). To assure that the units between inland water and public
beach are the same, the assumption that most recreational boating takes place
within a mile from the coastline was made. Therefore, if an area has 10 miles
of public beach, then the surface area attributable to those 10 miles is
simply 10 square miles. State and county level data on miles of public beach
was collected from the National Oceanic and Atmospheric Administration's
National Estuarine Inventory: Data Atlas. 1988. while data on inland water
covered surface area was derived from Census' Area Measurements Reports. GE-20
No. 1. 1970. Table 4-7 shows these data for each of the 24 nonattainment
areas in the study.
To arrive at an estimate of populations at the county level, the ratio of a
county's watered covered surface area to the county's state watered covered
surface area was multiplied by the county's state population of engines.
Therefore, if STPOF is defined as the total population of marine engines in a
given state (i.e., inboard + outboard + sterndrive + sailboat auxiliary
inboard + sailboat auxiliary outboard) and V is defined as the ratio of a
county's watered covered surface area to the county's state watered covered
surface area, then W * STPOP equals the total population of marine engines ir.
A.I
-------
Table 4-7
Inland Water and Coastal Public Beach
Inland Coastal
Non Attainment Area
Nation
Baltimore CMSA
Chicago CMSA
Denver CMSA
Houston CMSA
Milwaukee CMSA
Boston NECMA
Hartford NECMA
New York CMSA
Philadelphia CMSA
Seattle-Tacoma CMSA
Atlanta CMSA
Baton Rouge CMSA
Cleveland CMSA
El Paso CMSA
San Juaquin Valley Air
South Coast Air Basin
Miami CMSA
Minneapolis -St. Paul CMS
Provo-Orea CMSA
San Diego Air Basin
Spokane CMSA
St. Louis CMSA
Washington, DC CMSA
Springfield NECMA
Water
74,212
138
46
28
402
39
166
33
712
128
202
50
68
19
0
90
123
68
241
127
52
19
97
129
29
Beach
3,010
11
-
-
-
148
3
207
24
50
-
:
.
64
38
-
49
5
Total
77 ,222
149
46
28
402
39
314
36
919
151
252
50
69
19
0
90
188
106
241
127
101
19
97
134
29
4-17
-------
che county. To calculate the split between propulsion type, state level
distributions were held constant across the state's counties.
National marine engine populations by propulsion system were derived from
Coast Guard data. The Coast Guard supplied the total number of boats
registered in the U.S. and the distribution of boat length by inboard and
outboard propulsion types. Coast Guard data did not, however, disaggregate
further to specify the distributions of boats with sterndrive engines and
sailboats with inboard and outboard auxiliary engines. To derive the number
of national sterndrive boats and sailboat auxiliary inboard and outboard
engines by length, weighted average distributions from the state level data
that was gathered for this study were multiplied by total boat registrations
in the country. To estimate the number of engines by propulsion system, the
data shown in Table 4-6 was utilized.
4.6 Category 5: Light Commercial Equipment « 50 HP)
Light Commercial Equipment are generally used in light manufacturing, and
various wholesaling and retailing activities. The all encompassing nature of
these equipment, with regard to applications created difficulties in the
identification of relevant activity indicators.
Many models were tested employing various SIC codes for explanatory power, but
the model that resulted with the best statistical results used total wholesale
activity (number of 'establishments) as the indicator. Regression results for
this model are presented in Table 4-8. Note that while the model meets two of
the criteria for "best" model, its R2 is below 0.8 at 0.698.
Figure 4-2 shows the scatter plot between PSR' state light commercial
equipment populations and wholesale trade at the state level. It also shows
the regression line calculated by the model in Table 4-8. Clearly, Texas and
New York are outliers in this model; Texas' equipment population being
/, .1 a
-------
TABLE 4-8
Class 5: Light CoauMrcial Equipment
•B«sc" Modal
MODEL: PSRCLS5 - a * b(ESTVHSL)
PSRCLS5 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 5 (xlOOO)
ESTWHSL - TOTAL WHOLESALE TRADE (ESTABLISHMENTS) (xlOOO)
DEP VAR: PSRCLS5 N: 23 MULTIPLE R: .836 SQUARED MULTIPLE R: .698
ADJUSTED SQUARED MULTIPLE R: .684 STANDARD ERROR OF ESTIMATE: 74.744094
VARIABLE
CONSTANT
ESTVHSL
COEFFICIENT
•2.312631
8.551879
STD ERROR
24.130211
1.226572
STD COEF TOLERANCE
P(2 TAIL)
0.000000 . -0.09584 0.92456
0.83S658 1.00000 6.97218 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.271575E+06
.117320E+06
ANALYSIS OF VARIANCE
OF MEAN-SQUARE F-RATIO
1 .271575E+06 4i.611247
21 5586.679562
0.000001
4-19
-------
FIGURE 4-2
Light Commercial Equipment
Regression Fit and Outliers
Equipment Populations (xlOOO)
ro
O
600
500
400
300
200
100
0
0
N«v tofk
10 20 30 40
Wholesale Trade - Establishments (xlOOO)
i
50
60
-------
underestimated, while New York's overestimated. Eliminating the two outliers
from the model resulted with an R2 of 0.902, other statistics not changing
significantly. The close scatter of other states, nine states actually on the
regression line, indicates that this model meets the assumption, needed for
hypothesis tests, of homogeneity of variance in the residuals across different
values of the independent variable. This in turn suggests that the model can
provide reliable estimates. Given that the methodology adjusts for estimation
errors through r, the estimates for nonattainment areas in New York and Texas
will reflect the state populations.
4.7 Category 6: Industrial Equipment
Industrial Equipment are mostly used in various manufacturing activities. As
a result, manufacturing activity levels at state and county levels was the
indicator used to distribute national populations of these equipment to the
each nonattainment area. Specifically, the number of employees engaged in
manufacturing was used as the activity indicator for Category 6, and regressed
these data on PSR's state populations for industrial equipment. This model
met all three criteria, as shown by Table 4-9.
4.8 Category 7: Construction Equipment
Originally, separate statistical models for road construction equipment and
general construction equipment were considered. Various models were
formulated for both sub-categories of construction equipment using the
following indicators: SIC 161 - Road Construction, total construction
activity, and general construction activity (total minus road). Both sub-
categories responded best to total construction activity (number of employees)
as the indicator. Due to this, and EMI's specific objections regarding
disaggregation of construction equipment by applications, it was decided to
treat Construction Equipment as one category (road plus general) using total
construction activity as the indicator of local equipment populations.
Regression results for this "best" model are presented in Table 4-10. The
model exhibits excellent statistical validity.
-------
TABLE 4-9
Class 6: Industrial Equipaanc
"Base" Modal-
MODEL: PSRCLS6 - a + b(EMFMFC)
PSRCLS6 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 6 (xlOOO)
EMPMFG - TOTAL MANUFACTURING ACTIVITY (EMPLOYEES) (xlOOO)
DEP VAR: PSRCLS6 N: 23 MULTIPLE R: .966 SQUARED MULTIPLE R
ADJUSTED SQUARED MULTIPLE R: .930 STANDARD ERROR OF ESTIMATE: 2.734937
VARIABLE COEFFICIENT STD ERROR
CONSTANT
EMPMFG
•0.379266
0.020828
STD COEF TOLERANCE
P(2 TAIL)
0.927407
0.001212
0.000000 . -0.40895 0.68671
0.966237 1.00000 .17E+ 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
2209.064094
157.077343
ANALYSIS OF VARIANCE
OF MEAN-SQUARE F-RATIO
I 2209.064094 293.334044
21 7.479883
0.000000
4-22
-------
TABLE 4-10
C1*J« 7: Construction Equipment
*B««c* Model
MODEL ' PSRCLS7 - • * b(EMPCST)
PSRCLS7 - PSR STATE EQUIPMENT POPULATIONS FOR ALL CONSTRUCTION EQUPMENT (xlOOO)
EMPCST - TOTAL CONSTRUCTION ACTIVITY (EMPLOYEES) (xlOOO)
DEP VAR: PSRCLS7 N: 23 MULTIPLE R: .946 SQUARED MULTIPLE R: .395
ADJUSTED SQUARED MULTIPLE R: .890 STANDARD ERROR OF ESTIMATE: 23.373076
VARIABLE COEFFICIENT STD ERROR STD COEF TOLERANCE
P(2 TAIL)
CONSTANT
EMPCST
-4.566209 7.866897 0.000000 . -0.58043 0.56780
0.501182 0.037480 0.945991 1.00000 .13E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.101949E+06
.119734E+03
ANALYSIS OF VARIANCE
OF MEAN-SQUARE F-RATIO
1 .L01949E+06 178.307047
21 570.162510
0.000000
4-23
-------
4.9 Category 8: Agricultural Equipment
Activity indicators for the Agricultural Equipment category were derived from
data in the 1987 Census of Agriculture. County level populations were
available for some equipment types (such as cotton gins and cotton pickers)
from the Geographic Area Series, State and County Data. Census equipment
counts are presented in Appendix C by nonattainment area. EEA's population
estimates do not incorporate Census data for those equipment types in Appendix
B, since for the bulk of equipment in the Agricultural Equipment category,
county nor state level populations are available.
Many combinations of activity indicators were tested to determine their
reliability in distributing national populations to each nonattainment area.
Data on the number of farms, average farm size, total farmed acreage, average
farm revenue, the estimated market value of all machinery and equipment
(average per farm), and the average expenditure per farm on petroleum products
were collected at the national, state, and county level. Various combinations
of these indicators were tested to determine the indicators that best explain
equipment populations. In each circumstance the models failed to meet one or
more of the criteria outlined for "best" model.
Next, the relationship between an adjusted SIC 07 - Agricultural Services
(Employees) was investigated. SIC 07 includes the following: Soil Preparation
Services (SIC 071), Crop Services (SIC 072), Veterinary Services (SIC 074),
Other Animal Services (SIC 075), Farm Labor and Management Services (SIC 076),
and Landscape and Horticultural Services (SIC 078). SIC 07 was, therefore,
adjusted to exclude Landscaping and Horticultural Services, since SIC 078 is
used in estimation of Lawn and Garden Equipment populations. A model using
this adjusted SIC 07 as the independent variable was formulated to estimate
Agricultural Equipment populations. The results of this model are presented
in Table 4-11. Clearly, the model more than met each of the criteria for
"best" model.
-------
TABLE 4-11
Cl*s« 8: Agricultural Equipment
"B«*C* Model
MODEL PSRCLS8 - « > b(EMFA07)
PSRCLS8 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 8 (xlOOO)
EMPA07 - SIC 07 MINUS SIC 078 (EMPLOYEES) (xlOOO)
DEP VAR: PSRCLS8 N: 23 MULTIPLE R: .985 SQUARED MULTIPLE R: .970
ADJUSTED SQUARED MULTIPLE R: .969 STANDARD ERROR OF ESTIMATE: 21.967344
VARIABLE
CONSTANT
EMFA07
COEFFICIENT STD ERROR
4.945921
14.819782
STD COEF TOLERANCE
P(2 TAIL)
5.916288
0.565719
0.000000 . 0.83598 0.41258
0.985042 1.00000 .26E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.331160E+06
.101338E+05
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 .331160E+06 684.249866
21 482.564189
0.000000
4-25
-------
However, upon review of che population estimates derived from this model for
the nonattainment areas, it became clear that the model overestimated
Agricultural Equipment populations in each nonattainment area and for each
equipment type. Overestimation was caused by adjusted SIC 07's inclusion of
services that are not farm related, like Veterinary Services (SIC 074) and
Other Animal Services (SIC 075). Resource and time constraints did not allow
for reformulation of the model and the run of new regressions using a new
adjusted SIC 07, which accounts for only Farm Labor and Management Services
(SIC 076) and Crop Services (SIC 072). The estimation procedure was adjusted
by calculating the ratio of SIC 072 plus SIC 075 to SIC 07 at the state level,
and applying this ratio to each state's county. This allowed for a more
representative indicator of agricultural activity that uses the equipment
described in Category 8. (Note that data for SIC 072 and SIC 075 was not
available at the county level.) This new indicator was then plugged in to the
model shown in Table 4-11 to determine populations for each agricultural
equipment type at the nonattainment area level.
4.10 Category 9: Logging Equipment
Many activity indicators were tested for the Logging Equipment category.
However, in each case the statistical relationship between PSR's state level
data and activity indicators was not significant. EEA believes that while
PSR's national population estimates for such equipment are representative of
the true in-use populations, the activity indicators that PSR employs to
distribute such equipment to the state level are not necessarily indicative of
true state populations. This is best shown by the fact that SIC 241
characterizes logging activity in a given area, and when PSR's state data for
Category 9 equipment was regressed on state data for SIC 241 no statistical
relationship was found, as shown by Table 4-12. In fact, for both
establishments and employees, the R2's are below 0.1 indicating no linear
relationship between Logging Equipment populations and logging activity as
defined by SIC 241. Moreover, the t-statistics for the SIC 241 coefficient in
each model were significant at only an SOX confidence level, indicating chat
che model would not provide reliable estimates.
-------
TABLE 4-12
Class 9: Logging Equipment
SIC 241 Regression*
MODEL PSRCLS9 - « * b(EST24l)
PSRCLS9 - PSa STATI EQUIPMENT POPULATIONS TOR CUSS 9 (xlOOO)
EST241 - SIC 241 (ESTABLISHMENTS) • LOGGING
OEF VAR: PSRCLS9 M: 23 MULTIPLE R: .277 SQUARED MULTIPLE R- 3V
ADJUSTED SQUARID MULTIPLE R: .033 STANDARD ERROR Of ESTIMATE: 5.297427
VARIABLE COEFFICIENT STD ERROR
CONSTANT
EST241
4.423292
0.003957
STO COEF TOLERANCE
P(2 TAIL)
1.493411
0.004513
0.000000 . 2.96173 0.007^4
0.276641 1.00000 1.31921 0.20120
ANALYSIS OF VARIANCE
SOURCE S
REGRESSION
RESZOUAL
UM-OF- SQUARES
44.838036
389.317281
OF
1
21
MIAN -SQUARE
48.838036
28.062728
F- RATIO
1.740317
P
0.201303
MODEL PSRCLS9 - « * b(EMP24i)
PSRCLS9 • PS& STATS EQUIPMENT POPULATIONS FOR CLASS 9 (xlOOO)
EMP241 - SIC 241 (EMPLOYEES) • LOGGING
OEP VAR: PSRCLS9 N: 23 MULTIPtl R: .282 SQUARID MULTIPLE R: 079
ADJUSTED SQUARID MULTIPLE R: .033 STANDARD ERROR OP ESTIMATE: 5.239363
VARIABLE COEFFICIENT STD ERROR
CONSTANT
EMP241
4.731047
0.629443
STD COIF TOLERANCE
P(2 TAIL)
1.337902
0.463164
0.000000 . 3.33618 0.00196
0.281349 1.00000 1.34461 0.19310
SOURCI SUM-OP-SQCARIJ
RIGRISSIOV
RESIDUAL
30.386393
387 346922
ANALYSIS OP VARXANCI
DP MEAN-SQUARE f-RATIO
1
21
30.386393
27.979472
1.807982 0.193093
4-27
-------
SIC 241 Is a sub-category of SIC 24 (Lumber and Wood Products, Except
Furniture), the reliability of SIC 24 as an indicator was tested. At first
sight, the model using SIC 24 (number of employees) seemed to meet two of the
criteria, as shown in Table 4-13. The fact that the R2 is below 0.8 would not
cause significant problems if only a few outliers were driving it to 0.575.
However, closer examination of the residuals showed problems with
heteroscedasticity. Figure 4-3 plots the residuals against the estimates of
the regression model in Table 4-13. Notice the fan shaped, or
heteroscedastic, distribution of the residuals. This violates the assumption
of homogeneity of variance in the residuals across different values of the
independent variable, indicating that the model will not provide statistically
reliable estimates, although the t-stat of the coefficient is significant at
over 95Z confidence. Weighted least squares may solve help this problem, but
requires extensive analysis in the formulation of an appropriate model.
Given that SIC 241 is such an obvious indicator of logging activity and, chus,
Logging Equipment populations in a given area, it was decided to employ the
back-up methodology to distribute national PSR populations to the county
level. This back-up methodology uses the ratio of county logging activity co
national logging activity. This ratio is then multiplied by the national
populations of each equipment type in Category 9 to estimate county, or
nonattainment area, populations of these equipment. Table 4-14 shows SIC 241
by nonattainment area (establishments and employees) and the relative activity
as defined by the ratio of nonattainment area activity to national activity.
Conceptually either indicator can be used to distribute national Logging
Equipment populations to the nonattainment area level. However, using SIC 241
(establishments) will result with lower population estimates. Assuming chat
when Logging Equipment are used the capital/labor ratio is less than or equal
to one, meaning that there is at least one person operating each piece of
equipment when equipment is in use, then SIC 241 (employees) becomes the more
representative activity indicator. Therefore, SIC 241 (employees) relative
activity was used as the indicator to distribute national equipment
populations to each nonattainment area included in the study.
-------
TABLE 4-13
Cltss 9: Logging Equipment
Biased Model
MODEL PSRCLS9 - « > b(EMP24)
PSRCLS9 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 9 (xlOOO)
EMP24 - SIC 24 (EMPLOYEES) - LUMBER AND WOOD PRODUCTS. EXCEPT FURNITURE
DEP VAR: PSRCLS9 N: 23 MULTIPLE R: .758 SQUARED MULTIPLE R: .575
ADJUSTED SQUARED MULTIPLE R: .554 STANDARD ERROR OF ESTIMATE: 3.595392
VARIABLE
CONSTANT
EMP24
COEFFICIENT
0.891069
0.277289
STD ERROR
1.180717
0.052063
STD COEF TOLERANCE
P(2 TAIL)
0.000000 . 0.75468 0.45882
0.758032 1.00000 5.32604 0.00003
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
366.691623
271.463694
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RAT10
1
21
366.691623
12.926843
28.366681
0.000028
4-29
-------
FIGURE 4-3
Logging Equipment
Distribution off Residuals
Regression Residuals (x 1000)
12
10
s
6
-2
-6
6 10 16
Esim ed Populations (x 1000)
20
-------
TABLE 4-14
Logging Activity by Non-Attainment Are*
As Reported by SIC 241 - Logging
SIC 241 SIC 241
(Logging) Relative* (Logging) Relative*
Non-Attainment Area Establishments Activity Employees Activity
* Relative to national activity.
Source: U.S. Census' County Business Patterns. 1987
4-31
National Total 11,378 - 87,926
Baltimore CMSA 0 OX 0 OX
Chicago CMSA 0 OX 0 OX
Denver CMSA 0 OX 0 OX
Houston CMSA 26 0.23X 229 0.26X
Milwaukee CMSA 0 OX 0 OX
Boston NECMA 0 OX 0 OX~
Hartford NECMA 0 OX 0 OX
New York CMSA 0 OX 0 OX
Philadelphia CMSA 0 OX 0 OX
Seattle-Tacom* CMSA ; 151 1.33X 1565 1.78X
Atlanta CMSA 0 OX 0 OX
Baton Rouge CMSA 12 0.11X 129 0.15X
Cleveland CMSA 0 OX 0 OX
El Paso CMSA 0 OX 0 OX
San Juaquin Valley Air Basin 9 0.08X 132 0.15X
South Coast Air Basin 4 0.04X 67 0.08X
Miami CMSA 0 OX 0 OX
Minneapolis-St.Paul CMSA 0 OX 0 OX
Provo-Orea CMSA 0 OX 0 OX
San Diego Air Basin 0 OX 0 OX
Spokane CMSA 0 OX 0 OX
St. Louis CMSA 0 OX 0 OX
Washington, DC CMSA 0 OX 0 OX
Springfield NECMA 0 OX 0 OX
-------
Note chat only five of the 24 nonattainment areas have logging activities:
Houston CMSA, Seattle-Tacoma CMSA, Baton Rouge CMSA, San Juaquin Valley Air
Basin, and South Coast Air Basin. Intuitively, one would not expect high
levels of logging being performed in proximity of a large metropolitan area,
and this is reflected by SIC 241 data.
4-32
-------
5. EQUIPMENT POPULATIONS BY NONATTAINMENT AREA
This section presents, in tabular form, the results of the estimation process
for deriving nonroad equipment populations for each of the 24 nonattainment
areas included in the study and national data, by fuel type, on equipment
populations, load factors, annual hours of use, 2-cycle/4-cyole distributions,
and population weighted horsepowers. In addition, LPG/CNG penetration rates
are also included for relevant equipment types. These rates are applicable
only to gasoline powered equipment.
Table 5-1 provides national populations and 2-cycle/4-cycle distributions by
fuel type, and LPG/CNG penetration rates for each equipment type that is
included in the study; except sailboat auxiliary outboard engines, for which
only the total national population is provided.
Table 5-2 provides national data, by fuel type, for average annual hours of
.use, average load factors, and population weighted average horsepowers. PSR's
regional averages for annual hours of use are presented in Table 5-3, and the
relationships between the 24 nonattainment areas and the six regions is given
in Table 5-4.
Finally, Table 5-5 presents equipment population estimates, by fuel type, for
each of the 24 nonattainment areas. The reader should be cautioned, however,
that while nonattainmenc area populations for most equipment types were
r
estimated directly thr.ough the methodologies described in Section 5, the
national populations of some equipment types changed after the estimation
process was completed for each nonattainment area, These post-facto changes
in national populations required adjustments to the nonattainment area
estimates. Due to time and resource constraints, new sets of regressions
based on the new national populations were not developed. As a result,
nonattainment area populations for those equipment types whose national
populations had changed were adjusted by the fractional change at the national
-------
Level. Moreover, snowmobiles and snowblowers were noc allocated to those
nonattainmenc areas with mild climate where snowfall is non-existent or rare
5-2
-------
Table 5-1
National Equipment Type Populations.
2-Stroke/4-Stroke Splits, and LPC/CNG
Penetration Rates (By Fuel Type)
Class Equipment Types
Triamers/Edgers/Brush Cutters
Lawn Mowers
Leaf Blowers/ Vacuums
•ear Engine Riding Mowers
Front Mowers
Chainsaws <4 HP
Shredders <5 HP
Tillers <5 HP
Lawn t Garden Tractors
Wood Splitters
Snowblowers
Chippers/Stump Grinders
Commercial Turf Equipment
Other Lawn i Garden Equipment
Aircraft Support Equipment
Terminal Tractors
All Terrain Vehicles (ATVs)
Ninibikes
Off -Road Motorcycles
Golf Carts
3 Snotawbiles
3 Specialty Vehicles Carts
4 Vessels M/ Inboard Engines
4 Vessels M/Outboard Engines
4 Vessels M/Sterndrive Engines
4 Sailboat Auxiliary Inboard Engines
4 Sailboat Auxiliary Outboard Engines
5 Generator Sets <50 HP
5 Puaps <50 HP
5 Air Compressors <50 HP
5 Gas Compressors <50 HP
5 Welders <50 HP
S Pressure Washers <50 HP
6 Aerial Lifts
6 Forklifts
6 Sweepers/Scrubbers
6 Other General Industrial Equipment
6 Other Haterial Handling Equi patent
Diesel
0
0
0
8.713
0
0
0
0
211.631
79
0
17.087
87.807
180
9.529
64.598
0
0
0
0
0
3.344
72.818
15.521
286.405
397.513
254
198.391
61.810
15.713
0
100.490
3.943
12.310
160.583
36.977
18.366
5.258
National
Populations
Gasoline
18.172.282
35.764.096
2.025,786
1.575.407
257.880
16.124.970
107.322
3.812.000
5.903.369
502.181
4.782.000
16.791
480.925
396,454
2.767
6.S16
1,312.981
48.990
201.125
122.670
776.559
266.096
617.060
8.843.966
2.427.015
156.307
144,636
2.943,286
651.688
176,124
436
350.545
290.959
28.388
182.482
25,892
23.724
2.036
Total
18.172.282
35,764.096
2.025,786
1.584.120
257.880
16,124.970
107,322
3,812.000
6.115.000
502.260
4.782.000
33.878
568.732
396.634
12.296
71.114
1.312.981
48.990
201.125
122.670
776.559
269,440
689.878
8.859.487
2.713.420
553.820
144.890
3,141.677
713.498
191.837
436
451.035
294.902
40,698
343,065
62,869
42,090
7.294
X
Diesel
O.OOX
O.OOX
O.OOX
O.S5X
O.OOX
O.OOX
O.OOX
O.OOX
3.46X
0.02X
O.OOX
50.44X
1S.44X
0.05X
77.50X
90.84X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
1.24X
10.56X
0.18X
10.56X
71.78X
0.18X
6.31X
8.66X
8.19X
O.OOX
22.28X
1.34X
30.25X
46.81X
58.82X
43.64X
72.09X
X of
Class ---Diesel
Total 2-cycle
20.14X
39.63X
2.24X
1.76X
0.29X
17.87X
0.12X
4.22X
6.78X
0.56X
5.30X
0.04X
0.63X
0.44X
14.74X
85.26X
48.06X
1.79X
7.36X
4.49X
28.43X
9.86X
5.32X
68.35X
20.93X
4.27X
1.12X
65.54X
14.89X
4.00X
0.01X
9.41X
6.1SX
8.20X
69.16X
12.67X
8.49X
1.47X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
11.46X
O.OOX
O.OOX
41.57X
4.30X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
14.95X
12.87X
O.OOX
12.87X
O.OOX
N/A
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
2.52X
2.96X
2.54X
66.08X
29.19X
Si % \
(X) — uasoi i
t-cycle 2-cycle
O.OOX
O.OOX
O.OOX
100. OOX
O.OOX
O.OOX
O.OOX
O.OOX
100. OCX
100.00*
O.OOX
88.54X
100. OCX
100. OCX
58.43X
95.70X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
85.05X
87.13X
100.00X
87.13X
100.00X
N/A
100.00X
100.00X
100. OOX
O.OOX
100.00X
100.00X
97.48X
97.04X
97.46X
33.92X
70.81X
99.88X
10.05X
99.99X
O.OOX
O.OOX
100.00X
18.84X
0.46X
O.OOX
O.OOX
26.03X
O.OOX
3.42X
27.89X
O.OOX
O.OOX
10.13X
O.OOX
68. SOX
23.49X
94.03X
65.79X
1.02X
97.42X
1.02X
O.OOX
N/A
0.75X
7.97X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
8.31X
O.OOX
X of
ne (X)-- LPG or
t-cycle CHG
0.12X
89.95X
0.01X
100. OCX
100. OOX
O.OOX
81 . 16X
99.54X
100. OOX
100. OOX
73.97X
100. OOX
96.58X
72.1 IX
100. OOX
100. OOX
89.87X
100. OOX
31. SOX
76.51X
5.97X
34.21X
98.98X
2.96X
98.98X
100. OOX
N/A
99.25X
92.03X
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
91.69X
100. OOX
OX
OX
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
2X
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
N/A
9X
14X
ox
100X
ox
ox
12X
45X
35X
OX
ox
-------
Table 5-1, cont.
National Equipment Type Populations.
2-Stroke/4-Stroke Splits, and LPG/CNG
Penetration Rates (iy Fuel Type)
t\
i
Class
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
9
9
9
9
Equipment Types
Asphalt Pavers
Tempers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
Signal Board*
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saus
Cement and Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crush ing/Proc. Equipment
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Tractors/Loeders/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Equipment
2-Uheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Irrigation Sets
Tillers >5 HP
Suathers
Hydro Power Units
Other Agricultural Equipment
Chain&aMS ><• HP
Shredders >5 HP
Skicklen,
f el lerk/Buncher*
Diesel
15,536
0
2.322
5.511
36.300
26,700
43.615
0
20.36%
50.510
7.761
61.336
135
4.016
98.357
70.045
16.529
7.207
53,853
209.454
7.757
299,265
285.923
150,054
38,921
194
11.867
0
2.519,203
0
284.846
9.693
4,260
89.706
40
50,031
2.365
18.043
0
0
50,911
15,581
National
ipulations
Gasoline
3.022
23.611
145.233
0
21.999
0
230,810
30.833
1.559
27.170
8.501
18
36,900
232,152
2.541
0
0
1,007
2,217
3.433
0
1,365
0
27.805
0
24.301
1,103
13.802
5.900
16.023
1.843
72.720
0
45.948
783.140
32.858
15.043
6,404
51,775
100,271
0
0
Total
18,558
23.611
147.555
5.511
58.299
26,700
274,425
30,833
21.943
77,680
16,262
61,354
37.035
236.168
100,898
70,045
16.529
8,214
56,070
212.887
7.757
300,630
285,923
177.859
38.921
24.495
12.970
13.802
2,525,103
16.023
286.689
82.413
4.260
135.654
783.180
82.889
17.408
24.447
51.775
100,271
30.911
15,581
X
Diesel
83.72X
O.OOX
1.57X
100. 00*
62.27X
100. OOX
15.89X
O.OOX
92.90X
65.02X
47.72X
99.97X
0.36X
1.70X
97.48X
100.00X
100.00X
B7.74X
96.05X
98.39X
100.00X
99.S5X
100.00X
84.37X
100.00X
0.79X
91. SOX
O.OOX
99.77X
O.OOX
99.36X
11.76X
100.00X
66.13X
0.01X
60.36X
13.59X
73. BOX
O.OOX
O.OOX
100. OCX
100. OOX
X of
Class ---Dieselt
Total 2-cycle *
0.79X
1.01X
6.28X
0.23X
2.48X
1.14X
11.6BX
1.31X
0.93X
3.31X
0.69X
2.61X
1.58X
10.05X
4.30X
2.98X
0.70X
0.35X
2.39X
9.06X
0.33X
12.80X
12.17X
7.57X
1.66X
1.04X
O.S5X
0.35X
63.57X
0.40X
7.22X
2.07X
0.11X
3.42X
19.72X
2.09X
0.44X
0.62X
26.0BX
50.50X
15.57X
7.B5X
18.58X
O.OOX
O.OOX
6.17X
38.45X
21.38X
32.87X
O.OOX
O.OOX
5.72X
30.25X
39.1 IX
O.OOX
O.OOX
51.93X
22.S7X
23.62X
13.82X
3.40X
6.01X
21.71X
0.59X
3.18X
O.OOX
9.71X
O.OOX
26.24X
O.OOX
0.09X
O.OOX
O.OOX
11.26X
O.OOX
9.70X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
21. UX
8.23X
>/ w
<*) — — uasoiu
>-cycle 2-cycle i
81.42X
O.OOX
100. OOX
93.83X
61.55X
78.62X
67.13X
O.OOX
100. OOX
94.28X
69.75X
60.89X
100. OOX
100.00X
48.07X
77.43X
76.38X
86.18X
96.60X
93.99X
78.29X
99.41X
96.82X
100. OOX
90.29X
100. OOX
73.76X
100. OOX
99.91X
O.OOX
100. OOX
BB.74X
100. OOX
90.30X
100. OOX
100. OOX
100. OOX
100. OOX
O.OOX
O.OOX
78.86X
91.77X
O.OOX
95.57X
19.09X
O.OOX
O.OOX
O.OOX
S.14X
O.OOX
O.OOX
O.OOX
1.25X
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
O.OOX
0.20X
O.OOX
O.OOX
O.OOX
O.OOX
100. OOX
O.OOX
O.OOX
O.OOX
X of
» (X)-- LPG or
t-cycle CMC
100. OOX
4.43X
80.91X
O.OOX
100. OOX
O.OOX
94.86X
100. OOX
100. OOX
100. OOX
98,75%
100. OOX
100. OOX
100. OOX
100. OOX
O.OOX
O.OOX
100. OOX
100. OOX
100. OOX
O.OOX
100. OOX
O.OOX
100. OOX
O.OOX
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
100. OOX
O.OOX
99. BOX
100. OOX
100. OOX
100. OOX
100. OOX
O.OOX
100. OOX
O.OOX
O.OOX
ox
ox
ox
ox
ox
ox
oi
ox
fi»
m
m
m
m
6%
OK
m
m
m.
m
ox
ox
Oi
m
m.
&a
il&
68
ox
ox
ox
ox
ox
ox
92X
ox
ox
ox
ox
ox
ox
ox
ox
-------
lable 5-3
National Averages for Annual Hours of Use,
Load Factor, and Horsepower by fuel Type
.
i
t_n
Mass Equipment Types
Diesel Gasoline
Usage Load Weighted Usage Load Weighted
Hours Factor Ave. HP Hours Factor Ave. HP
Trimmers/Edgers/Brush Cutlers
Lawn Mowers
Leaf Blowers/Vacuums
Rear Engine Riding Mowers
Front Mowers
Chamsaws <4 HP
Shredders <5 HP
Tillers <5 HP
Lawn & Garden Tractors
Wood Splitters
S no wb lowers
Chi ppers/S tump Grinders
Commercial Turf Equipment
Other Lawn & Garden Equipment
? Aircraft Support Equipment
2 Terminal Tractors
3 All Terrain Vehicles (ATVs)
3 Minibikes
3 Off -Road Motorcycles
3 Golf Carts
3 Snowmobiles
3 Specialty Vehicles Carts
4 Vessels w/ Inboard Engines
4 Vessels w/Outboard Engines
4 Vessels w/Sterndrive Engines
4 Sailboat Auxiliary Inboard Engines
4 Sailboat Auxiliary Outboard Engines
S Generator Sets <50 HP
5 Pumps <50 HP
5 Air Compressors <50 HP
5 Gas Compressors <50 HP
5 Welders <50 HP
5 Pressure Washers <50 HP
6 Aerial Lifts
6 Forklifts
6 Sweepers/Scrubbers
6 Other General Industrial Equipment
6 Other Material Handling Equipment
0
0
0
39
0
0
0
0
266
77
0
46S
1068
163
732
1257
0
0
0
0
0
435
200
200
200
68
N/A
338
403
BIS
0
643
145
384
1700
1220
878
421
0.
0.
0.
32.
0.
0.
0.
0.
28
27
0
36
27
32
51
82
0
0
0
0
0
65
85
85
85
B2
H
74
74
48
0
45
30
46
30
66
51
59
OX
OX
OX
OX
ox
ox
ox
ox
5X
5X
OX
5X
5X
SX
OX
OX
OX
OX
OX
OX
ox
ox
ox
ox
ox
ox
'A
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
0
0
0
17
0
0
0
0
16
58
0
99
24
18
137
96
0
0
0
0
0
209
236
27
236
30
N/A
22
23
37
0
35
21
43
83
97
107
111
9
29
6
39
39
18
5
IS
51
22
a
488
733
23
681
827
135
55
120
1080
121
65
100
100
too
62
N/A
115
221
484
8500
208
115
361
1800
516
713
386
34
35
75
33
33
38
40
35
31
34
78
39
30
29
56
78
72
62
76
46
81
58
83
55
83
83
N
68
69
56
60
51
85
46
30
71
54
53
OX
OX
OX
SX
5X
5X
OX
5X
OX
5X
OX
OX
OX
OX
OX
OX
OX
OX
OX
ox
ox
ox
ox
ox
ox
ox
IK
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
ox
1
4
2
9
15
2
4
4
12
5
6
62
13
3
48
82
20
4
35
9
26
9
170
50
170
12
N/A
11
7
9
30
19
7
36
62
39
19
51
-------
Table 5-2. cont.
National Averages for Annual Hours of Use,
Load factor, and Horsepower by Fuel Type
01
Class Equipnent Types
-- Diesel Gasoline
Usage Load Weighted Usage Load Weighted
Hours Factor Ave. HP Hours Factor Ave. HP
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
a
a
e
a
a
a
a
a
a
a
9
9
9
9
Asphalt Pavers
Tampers/Ranmers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipnent
Signal Boards
Trenchers
Bore/Drill Rigs
Excavators
Concrete/Industrial Saws
Cement and Mortar Mixers
Cranes
Graders
Off-Highway Trucks
Crushing/Proc. Equipment
Rough Terrain Forklifts
Rubber Tired Loaders
Rubber Tired Dozers
Tractors/Loaders/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Highway Tractors
Dumpers/Tenders
Other Construction Equipment
2 -Wheel Tractors
Agricultural Tractors
Agricultural Mowers
Combines
Sprayers
Balers
Irrigation Sets
Tillers >5 HP
Swathers
Hydro Power Units
Other Agricultural Equipment
Chainsaws >4 HP
Shredders >5 HP
Skidders
Fellers/Bunchers
821
0
484
821
745
914
622
0
815
593
466
859
580
275
806
821
1641
955
662
761
B99
1135
936
818
855
566
606
544
475
0
150
90
95
749
172
110
790
381
0
0
1308
1276
62. OX
O.OX
43. OX
68. OX
56. OX
72. OX
53 OX
O.OX
82. OX
75. OX
75 OX
57. OX
73. OX
56. OX
43. OX
61. OX
57. OX
78 OX
60. OX
68. OX
59. OX
55. OX
64. OX
55. OX
65. OX
38. OX
62. OX
62. OX
70 OX
O.OX
70 OX
SB. OX
SB. OX
90. OX
7B.OX
55. OX
48. OX
51. OX
O.OX
O.OX
74. OX
71. OX
91
0
8
130
99
311
99
0
6
60
209
183
56
11
194
172
489
127
93
158
356
77
157
42
214
23
161
9
98
0
152
92
74
114
7
79
35
57
0
0
150
183
392
160
166
0
621
0
175
488
241
402
107
378
610
64
415
0
0
241
413
512
0
870
0
310
0
127
371
286
550
175
125
80
0
816
43
95
450
124
200
240
0
0
66. OX
55. OX
55. OX
O.OX
62. OX
O.OX
59. OX
49. OX
76. OX
66. OX
79. OX
53. OX
78. OX
59. OX
47. OX
O.OX
O.OX
85. OX
63. OX
71. OX
O.OX
48. OX
0 OX
58. OX
O.OX
41. OX
48. OX
62. OX
62. OX
48. OX
74. OX
65. OX
O.OX
65. OX
71. OX
52. OX
56. OX
55. OX
92. OX
BO. OX
O.OX
0 OX
31
4
5
0
17
0
7
a
8
27
54
80
13
7
55
0
0
60
88
67
0
63
0
33
0
9
150
7
87
11
131
24
0
109
7
106
14
55
6
8
0
0
-------
Table 5-3
Regional Average Annual
Hours of Use
Cl*»* Equipment Type*
North-Cot North-Cot South-Cot South-Cot South-Wot South-Welt North-Wot North-Wot Great-lake* Grot-LOo Pacific Pictftc
Dla*el Gatollna Dla*et Gatollne Dloel Goo Una Dletel Goo I In. Plate) Goolln. Dtetel Goolln*
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
3
]
3
3
3
4
4
4
4
4
5
5
5
5
5
5
6
6
6
6
6
...
Lawn Mower*
Leaf 81owert/Vacuua»
Rear Engine Hiding Mower*
Front Mower*
Ch*ln**w* <4 HP
Shredder* <5 HP
filler* <5 HP
Lawn » Garden Tractor*
Wood Splitter*
Snowblowert
Chtppert/Stuav Grinder*
CoBMrctal Turf EqutpMnt
Other Lewn A Garden Equipment
Aircraft Support Equipment
Tenalnel Tractor*
All Terrain Vehicle* (ATV*)
Mlnlblket
Off-Road Motorcycle*
Golf Cart*
SnoMBObl la*
Ve»»el* w/Outboard Engine*
Vettel* w/Sterndrlve Engine*
Sailboat Aualltary Inboard Engine*
Sailboat Auxiliary Outboard Engine*
Generator Set* <5O HP
f>ua*» <50 HP
Air Co>pre**or» <5O HP
Gat Ceaf>r«**or* <50 HP
Welder* «5O HP
Preoure Wathert <5O HP
Aerial Lift*
Fork lift*
Sweeper * /Scrubber*
Other Material Handling Equipment
0
0
0
2)
0
O
0
0
181
95
0
381
630
101
673
111*
0
0
0
O
0
383
114
108
114
33
NA
483
330
619
O
437
97
276
1564
1257
383
5
16
3
23
26
13
3
11
35
27
12
4OO
432
14
627
736
90
26
65
670
57
72
57
3O
NA
164
181
368
7565
141
77
260
1656
531
351
Q
0
0
50
0
0
0
0
340
67
0
493
1356
106
791
1282
0
0
0
O
0
457
3O8
201
308
93
NA
358
459
872
0
630
183
392
1649
1232
1O27
429
40
a
50
46
19
5
16
65
19
5
517
931
15
735
844
142
62
137
1231
77
68
154
134
154
85
NA
122
252
518
9O10
204
145
368
1746
521
834
394
0
0
0
48
0
0
. 0
O
322
73
O
525
1260
116
856
1«33
0
O
0
0
0
496
264
197
264
79
NA
382
488
954
O
759
171
407
1751
1318
IfVAQ
I DOW
463
10
41
8
48
45
19
5
16
62
21
6
551
865
16
7*7
943
138
67
13*
1166
80
132
131
132
72
NA
130
267
666
8755
245
136
383
1854
557
04)4
425
0
0
0
46
0
0
0
0
237
64
0
474
108*
17*
761
1232
0
0
0
0
0
422
216
168
216
73
NA
34
43*
83*
0
604
155
36*
1581
1183
983
413
9
26
5
46
44
21
5
13
45
18
8
4*8
748
25
708
810
131
52
115
»»4
160
63
inA
IUO
112
108
67
NA
117
241
4«*
8585
1*6
123
347
1674
501
799
378
0
0
0
22
O
0
0
0
173
92
0
367
5*8
187
651
1081
0
0
0
0
O
370
110
103
110
31
NA
463
318
5*5
0
418
»3
265
1513
1208
571
366
5
15
3
22
25
13
3
11
33
26
11
386
410
26
6O6
711
88
25
•2
637
182
55
55
69
55
29
NA
158
175
353
731O
135
74
249
1602
511
463
336
0
0
0
48
O
0
0
O
317
72
0
516
123*
1*7
842
14O8
O
O
0
O
0
487
194
260
78
NA
375
480
937
O
746
168
399
1717
1293
1071
455
10
41
7
48
44
19
5
16
61
20
6
542
8 SO
28
783
926
135
65
137
1145
79
73
1 3O
129
130
71
NA
128
263
557
8585
241
133
375
1818
547
87O
417
-------
Table 5-3, cont.
Regional Average Annual
Hours of Use
data EqulpMnt typei
orlh-Eatt North-fail South-Eaat South-tail South-Watt South-Wot North-Wait North-Watt Greet-Lakea Great-Lakea Pacific Pacific
Otatel Ga»oltne Olaie) Gatolln* Otatal Gatoltna Dletal Gatotln* Olatal Gaaallna Dlaial Gaaolln*
7
7
7
7
7
7
7
7
7
7
7
7
7
. 7
7
7
j" 7
» 7
7
7
7
7
7
7
7
7
7
a
0
•
a
a
a
a
a
a
a
a
8
»
9
9
Aaphalt Pavan
I«ap*rt/lla«B»ar>
Plata Coiu>actor>
Coocrata Pavan
Roll art
Scrapert
Paving Equipment
Surfacing EqulpMnt
Signal toardt
Irenchart
•ore/Drill fttgt
Eacavatort
Concrata/lnduitrlal Sax*
Caaant and Nortar Ml»ar>
Cranai
Gredart
Of*-Hlgh«ay Iruckt
Cruthtng/Proc. Equtpawnt
Hough larraln forkllftt
Rubber tired Loaders
Rubber llrad Doiart
Tractort/Loadart/keckhoet
Crawler Tractort
Skid Staar Loadara
Off-Hlghmy Iractor»
Divapert/ tenders
Other Cenatryctlon Equipment
2-Wheel Irtctort
Agricultural Ho-era
Coabtnet
Sprayer*
•alart
Irrigation Salt
ItlUra >5 HP
Svathart
Hydro Power Untti
Othar Agricultural Equipment
Chalntant >4 HP
Shraddert >S HP
Sklddari
Fal lart/Bunchart
»sa
0
MM
ssa
477
•»S
Ml
O
4ai
427
27S
416
418
162
•S3
• 16
lisa
• 11
424
647
674
ao«
683
448
804
277
4O6
348
323
O
77
S6
ss
442
12?
54
624
244
O
O
1033
919
267
115
103
0
397
0
102
2aa
' 142
2*9
63
272
43*
SO
336
0
0
1S4
264
43S
0
618
0
208
O
62
249
183
374
S6
64
SO
39
481
32
47
3S6
79
148
163
O
O
80S
0
S47
772
77S
9S1
6S3
O
•37
617
5O3
876
SS7
281
7 SO
7SS
iaos
IO7O
7IS
799
962
1203
1011
646
• IS
662
S»4
658
499
0
171
121
130
861
186
123
766
41S
0
0
1282
1302
384
170
iaa
0
646
O
184
4S4
277
418
116
386
S86
86
386
0
0
270
446
538
0
•22
O
24S
0
149
364
346
S78
2SO
143
107
•3
•38
46
106
437
13S
216
2S2
O
0
846
0
610
854
760
1024
722
0
978
6S2
550
• 11
603
MS
814
837
1871
1165
77S
8*0
1034
116*
1067
8S»
•92
685
624
642
542
0
186
112
142
981
187
139
830
453
O
O
1413
1467
404
186
20»
0
633
0
203
S12
289
442
126
401
634
•3
419
0
0
2*4
483
S99
O
89*
0
326
0
1S4
382
337
627
184
1SS
••
101
104»
47
120
473
148
210
247
0
0
772
0
411
739
74S
• 14
628
0
897
593
48S
842
S39
270
717
722
1723
1031
69S
76»
•26
lisa
•73
703
881
S43
S70
522
48O
O
114
109
64
•24
1S1
75
743
396
0
0
1243
1238
368
149
141
O
621
O
177
434
26S
402
111
370
S67
82
36*
O
0
260
434
S17
0
887
0
267
O
122
34*
27S
SS6
16S
111
•7
46
898
38
65
423
129
228
218
0
O
S34
0
286
S34
4S4
667
348
0
0
409
261
593
400
1S7
629
591
1149
592
410
624
647
772
6SS
S24
778
266
388
337
309
0
74
S3
52
41*
122
S2
too
236
0
O
994
HBO
2SS
110
98
0
379
0
98
278
O
277
60
261
421
48
324
0
0
149
2S6
420
O
592
O
198
0
60
237
177
3S8
82
61
47
37
4S7
31
45
342
77
142
156
O
0
829
0
6OO
837
74S
1005
70*
0
•62
640
S41
893
592
3OO
798
821
1838
1146
761
87 S
1016
1146
1048
843
•75
662
612
631
S32
0
183
no
139
966
184
136
814
446
0
O
1386
1442
39*
182
208
0
621
O
199
SO3
284
434
124
393
622
92
411
0
O
289
47S
S89
0
879
0
319
O
149
375
332
616
ISO
1S3
•8
»•
10S3
46
118
464
145
2O6
242
0
O
-------
Table 5-4
Non-Attainment Areas and Their Corresponding
Annual Hours of Use Regions
AREA
1. Baltimore CMSA
2. Chicago CMSA
3. Denver CMSA
4. Houston CMSA
5. Milwaukee CMSA
6. Boston NECMA
7. Hertford NECMA
8. New York CMSA
9. Philadelphia CMSA
10. Seattle-Tacoma
CMSA
11. Atlanta CMSA
REGION
North East
Great Lakes
South West
South Vest
Great Lakes
North East
North East
North East
North East
North West
South East
12. Baton Rouge CMSA South East
AREA
13. Cleveland CMSA
14. El Paso CMSA
15. San Juaquin Air
Basin
16. South Coast Air
Basin
17. Miami CMSA
18. Minneapolis -
St.Paul CMSA
Great Lakes
South West
Pacific
Coast
Pacific
Coast
South East
Great Lakes
19. Provo-Orem CMSA North West
20. San Diego Air
Basin
21. Spokane CMSA
22. St. Louis CMSA
Pacific
Coast
North West
Great Lakes
23. Washington D.C. South East
CMSA
24. Springfield North East
NECMA
5-9
-------
Table Si
Citlaatad Equlpeunt Population*
by Non-Attainment Araa and rual Tjrp*
Cla*t
1
*
>
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
a
8
a
a
a
8
a
8
8
u
H
*t
•t
.,
t
Equlpawnt Type*
Trlmmer./edger./Bru.h Cutter*
Lawn Mower*
Leaf Blowan/Vacuum*
Rear Engine Riding Mower*
Front Mower*
Chelntew* <4 HP
Shredder* «5 HP
Tlllera <5 HP
Lawn e Garden Tractor*
Wood Splitter*
Snowblower*
Chlpper*/Stuatp Grinder.
Commercial Turf Equipment
Other Lawn 6 Garden Equipment
Aircraft Support Equl patent
Terminal Tractor*
All Terrain Vehicle* (AlVt)
Mlnlblke*
Off-Road Motorcycle*
Golf Cart*
SnoMaoblle*
Specialty Vehicle* Cart*
Ve**elt w/ Inboard Engine*
Ve»el* w/Outboard Engine*
Vettel* w/Sterndrlve Engine*
Sailboat Auxiliary Inboard Cnglne*
Sailboat Auxiliary Outboard Engine*
Generetor Set* <5O HP
Pua,,. <50 HP
Air Comprai*or* i HP
S_.ih..»
t)th«i Ay. It u 1 1 ui a 1 Iqulpment
< h« 1 1> v«*>h >4 MM
Mn •>I.I«| t '^ HP
'.» 1 iljm • »
t • 1 !•• V fO '••• v
Baltimore CMSA
Dleael Gaaolln*
O
0
O
76
O
0
0
0
1.847
1
0
ISO
771
2
179
1.214
0
0
0
0
0
4
549
40
l.OSO
l.«27
2
1.267
39ft
100
0
642
25
72
934
216
107
31
141
0
21
SO
330
241
394
O
184
4S7
70
555
1
36
890
633
149
65
487
1.894
70
2.706
2 586
T.357
3S2
2
107
O
1.206
O
136
5
2
43
O
24
I
9
0
O
u
o
159.504
313.943
17.781
13.850
2.261
141. 534
942
42.S42
51.541
4.4O8
3S.70I
147
4.221
3.480
52
123
2.358
M
361
220
1.39ft
478
4.653
22.971
a.*9*
1.422
18.794
4.161
1.12ft
3
2.238
1 658
166
1.O63
151
138
12
27
214
1.313
0
199
0
2.087
279
14
246
77
o
334
2.100
23
0
0
9
20
31
0
12
0
291
0
220
10
7
3
8
1
35
O
22
375
16
7
0
O
0
0
Chicago CMSA
Diesel Ge»ollne
0
O
0
237
O
O
0
0
ft. 744
2
0
466
2.394
ft
514
3.488
O
O
0
O
0
34
S94
S3
320
•a
0
4.982
1.592
39S
0
2.524
99
469
6.114
1.409
700
200
454
0
68
161
1.O6S
779
1.274
0
59S
1.475
227
1.792
4
117
2,873
2.046
403
211
1.S73
6.118
227
8.741
8 352
4^383
1.137
6
347
0
4.653
O
663
23
10
2O9
0
116
6
42
0
0
O
0
495.911
976.077
55,281
43.060
7.029
440.041
2.929
104.490
160,251
13.706
130.976
458
13.124
1O.820
149
352
13.435
SOI
2,058
1.25S
7.946
2.723
9.0S3
30.068
2.783
27
140
73.918
16.367
4.423
11
8.804
7 .307
4! 940
987
904
78
68
69O
4.242
0
643
0
6.742
901
46
794
248
1
1.078
6.781
74
0
0
29
6ft
100
0
4O
o
812
0
710
32
32
14
37
4
169
0
107
1.824
76
35
15
0
O
0
O
Denver CMSA
Dlaael Ga.ollna
0
0
0
102
0
0
0
0
2.479
0
201
1.034
2
151
1.024
0
0
0
0
0
IS
41
2
417
9
0
2.615
81ft
207
0
1.324
52
119
1.556
359
178
51
195
0
29
69
457
334
S46
0
255
633
97
768
2
50
1.232
877
207
90
675
2,624
97
3.749
3,582
liaao
488
2
149
0
2.828
0
403
14
6
127
0
71
26
0
O
0
0
214.018
421.240
23.858
18.581
3.034
189.906
1.264
45.094
69.1S9
S.91S
S6.S2S
198
5.664
4.670
44
103
5.837
218
894
S4S
3.4S2
1.183
350
934
3.S3I
4
3
38.793
B. 589
2.321
4
4.620
3 835
'275
1.764
251
230
20
38
294
1.819
O
274
0
2.891
384
20
340
104
0
462
2.908
32
0
O
13
28
43
0
17
Q
148
0
304
14
20
8
23
3
103
0
65
1.1O8
46
? 1
£ 1
9
0
0
0
0
Houaton CMSA
Dleael Ga.ollna
0
0
0
174
0
0
0
0
4.203
2
0
341
1.753
4
210
1.424
0
0
0
O
0
29
344
80
l.OSO
25
0
5.649
1.760
447
0
2.861
1 12
177
2.304
532
264
76
475
O
71
169
1.115
815
1.334
0
623
1.545
237
1 .876
4
123
3.008
2.142
SOS
220
1.647
6.405
237
9.152
• YmlJ
a 9 *»*•
4.589
1.190
6
363
O
6.512
0
736
25
11
232
0
129
47
0
O
61
41
362.869
714.217
40.451
31.508
5.143
121.988
2.143
76.457
117.260
10.029
0
33S
9.603
7.917
61
144
11.581
432
1.774
1.082
0
2.347
2.918
45.713
a. 902
10
158
83.802
18.555
5. 015
12
9.981
a 284
*4oa
2.618
372
341
29
92
722
4.441
O
673
0
7.O58
943
48
831
260
1.128
7.099
78
0
0
31
68
105
0
42
Q
850
0
743
34
36
IS
41
5
188
0
119
2.025
85
39
17
115
261
0
0
Milwaukee CMSA
Dleael Gaaollne
0
0
0
48
0
0
0
0
1.150
0
0
93
480
1
120
814
0
0
0
0
0
6
130
ia
0
3
0
891
278
71
0
4S1
18
107
1.389
320
159
46
78
0
12 '
28
184
134
220
0
103
254
39
309
1
20
495
353
83
36
271
LOSS
39
1.507
1 440
'756
194
1
60
O
311
O
35
1
1
11
0
6
2
0
0
0
0
99.329
195.504
11.073
a. 625
1.408
88.118
587
20.929
32.131
2.745
26.234
92
2.629
2.167
35
82
2.274
85
348
212
1.345
461
1.106
10.343
0
1
36
13.216
2.926
791
2
1.574
1 3O4
'246
1.577
224
205
18
IS
119
731
0
111
0
1.162
155
a
137
43
0
186
1.169
13
0
0
5
11
17
0
7
Q
140
0
122
6
2
1
2
0
9
0
6
97
4
2
0
O
0
0
Bo. ton NECMA
Ola.al Gaaollne
O
0
0
122
0
0
0
0
2.951
1
0
240
1.231
3
245
1.662
0
O
0
0
0
22
1.157
83
3.612
89
O
2.831
882
224
0
1.434
54
234
3.074
709
352
101
213
0
32
75
499
365
597
0
279
691
1O6
839
2
55
1.346
959
226
99
737
2.866
106
4.095
3 913
2! 053
533
3
162
0
2.428
0
274
9
4
U6
0
48
2
17
0
0
0
0
254.799
501.509
28.404
22.124
3.612
226.093
1.505
53.687
82.338
7.042
67.296
235
6.743
S.559
71
168
8,680
324
1.330
811
5.114
1.759
9.805
47.O31
30.607
35
158
41.99ft
9.298
2.S13
6
S.O02
4 151
*544
3.490
496
455
39
41
323
1.987
0
301
0
3.159
422
21
372
116
0
505
3.177
35
0
0
14
3D
47
0
19
o
381
0
313
IS
13
6
15
2
7O
O
44
755
12
J Q
6
0
0
0
0
Hartford NECMA
Dleael Geaeltne
0
0
0
41
0
0
O
0
984
0
0
80
411
1
1.198
0
O
0
0
0
10
302
21
418
949
1
691
216
55
O
351
86
1.126
26O
129
37
79
0
12
28
185
135
221
0
103
256
39
311
1
2O
498
355
84
37
273
1.061
39
1.516
1 448
'760
197
1
6O
O
429
0
49
2
1
15
0
9
o
3
0
0
0
0
84.998
167.298
9.475
7.380
1.205
75.422
502
17. MM
27.447
2.349
22.449
79
2.249
1.85S
51
121
3.745
140
574
35O
2.215
759
2.558
12.250
3.538
373
654
10.278
2.276
615
2
1.224
Iniet
»**!•
199
1.279
182
147
14
IS
120
734
O
111
O
1.169
154
a
138
43
o
187
1.174
13
O
O
5
11
17
0
7
141
0
123
6
2
1
3
O
12
0
a
113
6
j
1
0
O
0
0
-------
Table 5-5. cont.
Eitlmated EqulpaMiU Population*
by Non-Attainment Are*, end rue I Type)
Naw >ork CMSA
Phi lad*I. CNSA
_. . , . „. , , ,. Seat.-Tec. CMSA Atlanta CNSA Baton Doug* CMSA Cl.v.l.nd CMSA £1 Paao CMSA
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Lawn Mowera
leaf 810Mera/VecuiM«»
Raar Engine Riding Mo»er»
Front Hoxart
Chalnae-a <4 HP
Shreddera Grinder.
Coee»erctel Turf Equipment
Othar La«n a. Cardan Equipment
Aircraft Support Equipment
Terminal Tractora
All larr.ln Vehicle. (ATVa)
Minlotka.
Off-Road Motorcycle.
Golf Carta
SnoMeoblle.
Specialty Vehicle. Cart.
Va»al> »/Outboard Engine.
Veaaole «/Starndrlva Engine.
Sailboat Au«lltary Inboard Englnat
Sailboat Auilllary Outboard Englnat
Generator Sata <50 HP
p—f. re»»or. S HP
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12.086
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2.542
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41
4.773
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9.1S1
3.776
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13.433
4. IBS
1.064
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6.6O4
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939
12.232
2.620
1.401
401
1.018
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152
361
2.389
1.747
2.657
0
1.335
3.309
soa
4.018
9
263
6.443
4.588
1.083
472
3.528
13.721
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19.6O4
18.730
9.830
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90.610
14.791
925.969
6.163
219.875
337.216
28.842
275.610
964
27.617
22.768
1O9
256
16.183
604
2.479
1.512
9.571
3.280
4O 451
1*4.571
77.643
1.485
5.297
199. 2*3
44.126
11.926
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23.736
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2.165
13.685
1.974
1.809
155
198
1.547
9.514
0
1.441
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15.120
2.020
102
1.78O
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209
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0
0
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5.066
2
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411
2.114
4
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1.2OO
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1 .315
107
1.434
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3.584
1.117
284
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288
3.749
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42*
123
110
0
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110
728
532
871
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1.008
155
1 ,225
3
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1.964
1.398
330
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1.075
4.182
155
5.975
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299
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437.416
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388,136
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12.O90
115.527
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11.576
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51
121
5.109
191
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3.022
1.035
11 139
61.245
12.156
140
1.220
53.177
11.774
3.182
a
6.333
5 257
663
4.255
605
554
48
60
471
2.9OO
O
439
0
4.608
616
31
542
17O
0
737
4.635
51
0
O
2O
44
69
0
27
0
555
O
485
22
15
6
17
2
76
0
48
824
95
16
7
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0
0
u
o
0
o
100
0
o
o
0
2.410
1
0
196
1.0O6
2
US
782
O
O
0
0
O
21
1 . 407
35
O
407
1
1.518
473
120
0
769
3O
100
1.304
301
149
43
175
O
26
62
412
301
492
O
23O
570
88
693
2
45
1.111
791
187
81
608
2.365
88
3.379
3.228
1.694
439
2
1 34
O
«£OA
i 3tU
0
1.076
37
16
339
O
189
9
t.8
0
0
ibO
211
2O8.I16
4O9.625
23.200
18.071
2.95O
184 .670
1.229
41.851
67.252
5.752
5.497
192
5.508
4.541
33
79
8.349
312
1.279
7 BO
494
1.692
1 1 919
19i672
0
16O
819
22.526
4.988
1.348
3
2.683
2 227
'231
1.48O
210
193
17
34
267
1.640
O
248
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2.606
348
18
307
96
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417
2.621
29
0
0
11
25
39
0
is
O
314
0
274
12
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22
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275
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174
2.960
124
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24
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0
0
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92
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0
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2.224
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181
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2
177
1.202
0
0
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12
155
27
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9
0
1.7O9
532
135
0
866
34
79
1.030
238
118
34
185
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28
64
435
318
520
0
243
603
93
712
2
48
1.173
836
197
86'
642
2.499
93
3.570
3.411
1.790
464
2
142
0
5.379
0
608
21
9
191
0
107
5
39
O
0
0
0
192. OOS
377.914
21.404
16.672
2.721
170.373
1.134
40.456
62.046
5.307
0
177
5.081
4.189
51
121
4. 539
169
695
424
0
920
1 315
IS] 147
2.580
3
53
25.352
5.613
1.517
4
3.019
2%A£
• 9wv
182
1.170
164
152
13
36
282
1.732
0
262
0
2.753
368
19
324
101
Q
440
2.769
30
0
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12
26
41
0
16
0
332
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290
13
29
13
34
4
155
0
98
1.672
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32
14
0
0
0
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259
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24
584
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35.578
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3.966
3.089
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31.570
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7.496
11.497
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33
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38
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145
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363
0
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0
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10.194
2.257
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292
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0
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at SI
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1.505
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2
119
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0
186. O93
366.278
20.745
16.158
2.638
165.128
1.099
39.210
60.136
6.143
49. ISO
172
4.925
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63
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2.035
76
312
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1.203
412
3 . 82 S
19.178
6.283
26
128
28.630
6.560
1.773
4
3.529
2 . 929
505
3.241
461
422
36
30
237
1.457
0
221
0
2.315
3O9
16
273
as
Q
370
2.328
25
0
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1O
22
34
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14
0
279
0
244
9
4
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1
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0
29
497
21
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341
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195
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317
12
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0
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256
182
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19
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545
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744
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0
114
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1
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0
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57 872
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2.553
417
26.O9O
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6.195
9. SOI
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o
27
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642
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25
3,567
133
646
333
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723
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0
0
o
9.293
2.058
556
1
1.107
a i o
y i v
86
554
79
72
6
8
61
378
0
57
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6O1
80
4
71
22
Q
96
604
7
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O
3
6
9
0
4
O
72
0
63
3
6
2
6
1
29
O
18
314
13
£
3
O
0
0
0
-------
Table S-S. coot.
EltlaVeted CqulpaMOt Population*
by Mon-AtteliuMat Are. and fuel Typ*
San Jq. Val. AB South Co..I AB HI tail CMSA Mln.-St.Paul CMSA Provo-Orem CMSA San Diego AB Spokane CMSA
Cla» Equipment Type* ..?!?!!!..?!!?!!?? ?!?"!..^!!?!!??... °'"«' <»•••» |«« °'*!"!__6"°"M "«•••' Geaol Ine Oteael Gaaollne Dleael Gaaellne
r
7
7.
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
a
a
e
8
8
8
a
a
8
a
9
1
•J
Irlae»era/Cdgara/6ru>h Cutlera
Latin Moxert
Laaf ilo.ar»/Vacuu»i
Rear Engine Riding Ho»era
front Ko»ar»
Cheln*ana «4 HP
Shreddora Grinder*
Coeaaerclal Turf Equipment
Othar Laxn a Garden Equipment
Aircraft Support Equipment
Temlnal Tractor*
All terrain Vehicle* (AIV.)
Mtnlbtke*
Off-Road Motorcycle*
Golf Cert*
SnoMMobt 1e*
Specialty Vehicle* Cartt
Va»al> •/ Inboard Englnea
Venal* x/Outboard Engine*
Veaael* «/Steri>drlve Englnea
Sailboat Auxiliary Inboard Englnea
Sailboat Aunlllary Outboard Engines
Generetor Seta 5 HP
Swather*
Hydro Power Untt&
Uthar Agricultural equipment
^hi •UUarft >5 HP
",» IdJ.i I
I.I l«. i/buf.t h.. I
0
0
0
65
0
0
0
0
1.S6J
0
127
•62
11
66
0
0
0
0
0
7
674
20
78*
611
0
1.2S1
190
99
0
*3S
2S
67
868
200
99
28
141
0
21
SO
332
243
397
O
IBS
459
71
ssa
1
37
895
637
ISO
66
49O
1.905
' 71
2.722
2. MO
1.36S
3S4
2
ioa
0
MeUI4
i 4UV
0
5.811
198
87
1.830
1
1.O2I
48
368
Q
0
46
2)
1 34. 924
265.563
IS. 041
11.715
1.912
119.723
797
28.429
43.6OO
1.729
0
12S
3.571
2.944
4
9
2.941
110
4SO
27ft
O
596
4.864
11.411
• .MS
24*
27*
18. SM
4. US
1.112
1
2.214
1.837
1S4
9M
140
12*
11
27
2IS
1.121
O
200
0
2AQO,
,uw
280
14
247
77
0
336
2.111
23
0
0
9
20
11
0
12
0
253
0
221
1O
282
1 9ft
laCU
327
38
1.484
0
937
15.983
670
307
131
78
151
0
0
0 1
0 2
O
S71
0
0 1
0
0
13.819
S
0
1.122
S.76S
12
42S
2.681
0
0
O
0
0
113
1.1M
42
1.411
1.3O6
1
9.S79
2.964
759
O
4.652
190
611
10.590
2.441
1.211
147
6S7
O
126
104
2.011
1.472
2 407
O
1.125
2.766
428
3.36S
7
222
5.429
3.866
912
196
2.972
11 . 560
428
16.517
IS. 761
6.262
2.146
11
655
0
63 966
0
7.231
246
108
2.277
1
1.270
60
458
Q
O
24
12
193.197
348.510
131.014
103.605
16.912
058.770
7.047
251.410
385.579
12.978
0
1.103
31.578
26.034
123
291
S2.132
1.945
7.986
4.871
0
10.56S
10.086
23.702
13.619
SIS
S77
142.109
31.465
8.504
21
16.925
14.046
1.674
12.021
1.709
I.S66
134
167
1.303
6.016
0
1.214
0
12 739
>!?02
66
l.SOO
469
1
2.037
12.613
140
0
0
56
122
169
0
75
0
1.535
0
1.341
61
350
ISO
407
47
1.846
O
1.166
19.887
834
382
163
39
76
0
0
0
0
0
91
0
0
0
0
2.205
1
0
179
920
2
106
718
0
0
0
0
0
20
248
21
233
13S
0
2.007
625
159
0
1.017
40
1O4
1.3S6
313
15S
44
152
0
21
54
357
261
427
0
200
494
76
60O
1
39
961
— . 666
162
71
527
2.050
76
2.929
2.796
1.469
361
2
116
0
10 482
0
1.185
40
18
373
O
208
1O
75
Q
O
0
o
190.400
374.754
21.22S
16.532
2.699
166.949
1.125
40.118
61.527
S.262
0
176
S.039
4.1S4
11
72
7.936
296
1.214
742
0
1.609
2.106
11.636
1.972
SI
146
29.77S
6.S91
1.762
4
1.54*
2.941
246
I.S4I
219
201
17
3O
211
1.421
0
21S
O
2 259
'102
IS
266
61
0
361
2.272
25
O
0
1O
22
14
O
11
0
272
0
236
11
57
67
a
303
O
191
3.259
137
63
27
O
0
O
O
0
0
0
72
o
0
0
0
1.739
1
0
141
726
1
ies
1.2S7
0
0
0
O
0 .
9
160
46
274
42
0
I.63S
S09
129
0
626
32
166
2.167
500
246
71
169
0
25
60
397
290
47 S
O
222
SSO
64
6*7
1
44
1.O70
762
160
76
566
2.279
64
3.256
1.111
1.611
421
2
129
0
11**l
a Jwl
0
153
5
2
48
0
27
1
10
O
0
O
150.167
295.566
16.740
13.039
2.126
131.249
687
11.641
46.S26
4. ISO
39.661
139
3.974
1.276
54
127
3.337
12S
511
312
1.974
676
1.156
26.156
2.126
17
138
24.2S2
5.370
1.4S1
4
2.888
2.397
364
2.460
ISO
121
26
31
257
1.580
0
239
0
2 511
*335
17
296
92
0
401
2.S26
28
0
0
11
24
17
0
IS
0
303
0
264
12
7
3
9
1
39
0
25
420
18
8
3
Q
0
0
0
0
0
0
IS
0
0
o
0
357
0
O
29
149
0
0
O
0
0
0
0
0
3
159
3
0
S
0
98
30
8
0
49
2
6
107
25
12
4
10
0
1
3
23
17
27
0
13
31
S
38
O
2
61
41
10
4
33
130
S
IBS
177
93
24
0
7
O
1 139
0
129
4
2
41
0
23
1
8
O
O
O
O
30.829
60.680
3.437
2.677
437
27 . 356
182
6.496
9.962
652
8.142
28
816
673
0
0
1.218
45
187
114
720
247
1.351
1.SS7
O
2
1
1.449
321
87
0
173
143
19
121
17
16
1
2
15
90
O
14
0
143
19
1
17
5
0
23
144
2 '
0
0
1
1
2
0
1
0
17
0
IS
1
6
3
7
1
))
0
21
354
15
7
3
Q
0
O
o
0
0
0
114
0
O
0
0
2.770
1
0
225
1.156
2
112
762
O
0
0
O
0
37
640
22
877
704
1
1.190
371
94
0
6O3
24
62
1.067
246
122
35
160
0
27
64
422
309
SOS
0
236
sas
90
710
2
• " 46
1.138
811
191
83
623
2.424
90
3.463
3.309
1.717
450
2
137
0
10,239
0
1.157
39
17
364
0
2O3
10
73
Q
0
O
O
239.144
470.695
26.659
20.765
3.390
212. 2O2
1.412
3:2*
6.61O
0
221
6.329
5.216
33
77
14.436
539
2.211
1.349
0
2.926
5.426
12.751
7.434
277
310
17.653
3.909
1.056
3
2.103
1.745
189
1.211
172
158
14
35
273
1.661
0
255
O
2 671
'357
18
314
98
0
427
2.687
29
0
0
12
26
40
O
16
0
322
0
281
13
56
24
65
7
295
0
187
3.183
134
61
26
A
0
0
0
0
0
0
12
O
0
0
0
290
0
0
24
121
0
17
US
0
0
O
O
O
4
104
1
0
30
0
219
68
17
0
111
4
9
123
28
14
4
16
O
2
6
39
28
46
O
22
54
a
65
O
4
104
74
18
8
57
222
a
316
304
159
41
0
13
0
1 543
0
174
6
3
55
O
31
1
11
Q
O
0
o
25.051
49.3O7
2.791
2.17S
155
22.229
146
S.276
6.09S
692
442
21
661
647
5
12
1.664
62
2SS
1SS
96
117
660
1.4S2
O
12
61
1.252
720
195
O
387
321
22
139
20
16
2
3
25
154
O
23
O
245
33
2
29
9
0
39
246
3
O
O
I
2
4
0
1
O
3O
O
24
1
6
4
10
1
45
O
28
48O
2O
9
4
o
O
O
0
-------
Tabl* J-5. cone.
E«tlm«t«d Cqulpunt Population*
by Non-Att«lnajanc Area end Viul Typa
U)
Class
1
I
I
1
1
1
1
1
1
I
1
1
1
1
2
2
3
3
3
3
3
3
4
4
4
4
4
5
S
S
S
S
5
6
t
c
•
6
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
8
a
a
a
a
a
a
a
a
a
9
9
9
9
Equipment lypes
IrlaMrt/Edgers/erush Cutters
Lawn Mowers
Leef Blowers/Vacuums
f)««r Engine Hiding Mower »
Front Mowers
Chatnaaws <4 HP
Shredders
Sailboat Auxiliary Outboard Engines
Gonarator Set* i> HP
SM«thor»
Hydro Pooar Unltt
Other Agricultural Equipment
Ch«tn»«» >4 HP
Shiaddari >S HP
Sk Iddar*
Fal I«r>/Buoth.r »
St. Louts CMSA Washington DC CHSA Springfield MCCHA
Diesel Gasoline Diesel Gasoline Diesel Gasoline
0
O
0
82
0
0
O
0
1 , 97 1
i
0
160
823
2
134
• 10
O
0
0
O
0
• 8
583
70
S72
59
0
1.44S
450
114
O
732
29
160
2.086
481
239
68
182
0
27
65
427
312
511
0
239
592
91
718
47
1.152
820
194
84
631
2.453
91
3.5O5
3 . 349
1.758
456
2
139
0
i.aas
0
213
7
3
67
0
37
2
13
0
0
0
0
170.359
335.308
18.991
14.792
2.415
151.166
1.006
35,895
55.O51
4.708
44.994
157
4.509
3.717
39
92
2.981
111
457
279
1.763
604
4.939
39.105
4.843
23
110
21.436
4.746
1.283
3
2.553
2. lit
369
2.368
337
309
26
35
277
1.701
0
258
O
2.704
361
18
318
100
O
at 19
•t Jd£
2.719
30
0
O
12
26
40
0
16
0
326
O
285
13
10
4
12
1
54
0
34
566
25
11
5
0
0
0
0
0
0
O
132
O
0
0
0
3.191
1
0
259
1.331
3
237
1.6O4
0
0
0
0
0
18
388
36
933
1.844
2
1.502
468
119
0
761
30
60
782
180
90
2«
271
0
40
96
635
464
760
O
355
88O
135
1.068
2
7O
1.713
1.220
288
126
938
3.648
135
5.213
4.980
2.614
678
3
207
O
1.889
0
214
7
3
67
0
38
2
14
O
O
O
O
275.497
542.246
30.711
23.921
3. 90S
244.459
1.627
58.048
89 . 026
7.614
18.191
255
7.291
6. Oil
69
162
7.219
269
1.106
674
1.067
1.463
3.292
20.525
7.905
725
1.107
22.279
4.933
1.333
3
2.653
2.202
138
887
126
116
10
53
411
2.530
0
383
0
4.020
537
27
473
148
0
dell
O* J
4.044
44
0
0
18
39
60
0
24
O
484
0
423
19
10
4
12
1
55
0
34
587
25
11
5
0
0
0
0
0
O
O
18
0
0
0
0
441
0
0
36
184
0
0
O
0
O
O
0
O
4
108
8
336
»
O
325
101
26
0
164
«
34
442
102
51
14
25
0
4
9
59
43
71
0
33
82
13
10O
A
7
160
114
27
12
88
341
13
487
46S
244
63
0
19
0
381
O
43
1
1
14
O
a
o
3
0
0
0
0
38.093
74.977
4.247
3.308
540
33.802
225
8.026
12. 310
1.053
1O.061
35
1.008
831
0
0
1.550
58
237
145
917
314
912
4.378
2.849
3
15
4.817
1.066
288
|
574
476
78
502
71
65
6
5
38
236
O
36
0
376
50
3
44
14
O
6O
378
4
0
0
2
4
6
0
2
0
45
0
4O
2
2
1
2
O
11
0
7
MB
5
2
1
0
0
0
0
-------
APPENDIX A
Equipment Classification Scheme
-------
CLASS 1
LAWN AND GARDEN EQUIPMENT
Equipment Types PSR Code
1. Trimmers/Edgers/Brush Cutters 53
2 . Lawn Mowers 65
3 . Leaf Blowers/Vacuums 66
4. Rear Engine Riding Mowers 82
5. Front Mowers 88
6. Chainsaws <4 HP 7C (0 - 4 HP)
7. Shredders <5 HP 96 (0 - 5 HP)
8 . Tillers <5 HP 59 (0 - 5 HP)
9 . Lawn and Garden Tractors 63
10. Wood Splitters 75
11. Snowblowers 56
12 . Chippers/Stump Grinders 26
13 . Commercial Turf Equipment 67
14. Other Lawn and Garden Equipment 76
Notes:
1) Commercial Turf Equipment includes the following:
• Hydro/Seeders Mulchers
• Riding Turf Mowers
• Thatchers/Aerators
• Walk-Behind Multi-Spindle Mowers
• Other Misc. Equipment (Catch All)
2) Other Lawn and Garden Equipment includes the following:
• Augers
• Sickel Bar Mowers
• Other Misc. Equipment (Catch All)
A-l
-------
CLASS 2
AIRPORT SERVICE EQUIPMENT
Equipment Types PSR Code
L. Aircraft Support Equipment 81
2. Terminal Tractors 16
Notes:
1) Aircraft Support Equipment includes the following:
• Aircraft Load Lifters
• De-icing Equipment/Heat and Start Units
• Ground Power Units
• Utility Service Equipment
• Baggage Conveyors
• Airport Service Vehicles
2) Terminal Tractors includes the following:
• Push-Back Tractors
• Tow Tractors
• Yard Spotters
-------
CLASS 3
RECREATIONAL EQUIPMENT
Equipment Types PSR Code
1. All Terrain Vehicles (ATVs) 91
2 . Minibikes 93
3. Off-Road Motorcycles 92
4. Golf Carts 94
5. Snowmobiles 71
6. Specialty Vehicles/Carts 62
Notes:
1) ATVs include 3-wheeled and 4-wheeled vehicles.
2) Specialty Vehicles/Carts includes the following:
• Snov Grooming Equipment
• Ice Maintenance Equipment
• Go-carts
• Personnel Carriers
• Industrial ATVs
-------
CLASS 4
MARINE gQOIPMENT
Equipment Types PSR Code
1. Vessels With Inboard Engines 50
2 . Vessels With OutboarM Engines 99
3. Vessels With Sterndrive Engines N/A
4. Sailboat Auxiliary Inboard Engines 79
5. Sailboat Auxiliary Outboard Engines N/A
Notes:
1) This category uses DNR registrations data and Coast Guard data for
population estimates, and PSR data for engine characteristics.
-------
CLASS 5
LIGHT COMMERCIAL EQUIPMENT
-------
CLASS 6
INDUSTRIAL EQUIPMENT
Equipment Types PSR Code
1. Aerial Lifts .64
2. Forklif ts 18
3. Sweepers/Scrubbers 21
4. Other General Industrial Equipment 74
5. Other Material Handling Equipment 19
Notes:
1) Aerial Lifts include the following:
• Boom Lifts
• Scissor Lifts
• Self Propelled Elevating Platforms
2) Forklifts include those that are cushion tired and pneumatic tired, used
in agricultural and industrial applications.
3) Sweepers/Scrubbers equipment type includes Municipal Sweepers, Industrial
Sweepers, and Scrubbers.
4) Other General Industrial Equipment includes the following:
• Abrasive Blasting Equipment
• Industrial Blowers/Vacuums
• Industrial Scrapers/Stripers
• Marine/Industrial Winches and Hoists
• Multipurpose Tool Carriers
• Other Misc. Industrial Equipment (Catch All)
5) Other Material Handling Equipment includes Conveyors and Other Misc.
Material Handling Equipment (Catch All)
-------
CLASS 7
CONSTRUCTION EQUIPMENT
Equipment Types PSR Code
1. Asphalt Pavers 41
2. Tampers/Rammers 95
3. Place Compactors 61
4. Concrete Pavers 22
5 . Rollers 39
6. Scrapers 29
7 . Paving Equipment 35
8. Surfacing Equipment 23
9. Signal Boards 72
10. Trenchers 42
11. Bore/Drill Rigs. .- 37
12. Excavators 28
13. Concrete/Industrial Saws 77
14. Cement and Mortar Mixers 57
15 . Cranes 27
16 . Graders 30
17. Off-Highway Trucks 40
18 . Crushing/Proc. Equipment 34
19. Rough Terrain Forklifts 84
20. Rubber Tired Loaders 33
21. Rubber Tired Dozers 32
22. Tractors/Loaders/Backhoes 43
23. Crawler Tractors 31
24. Skid Steer Loaders 38
25 . Of f-Highway Tractors 68
26 . Dumpers/Tenders 60
27 . Other Construction Equipment 36
4.7
-------
Notes:
1) Tampers/Rammers are the same as Compactors.
2) Concrete Pavers include Slip-Form Pavers. Curb Pavers are included in
Concrete Pavers.
3) Rollers include the following:
• Landfill Compactors
• Static and Vibratory Rollers
4) Paving Equipment includes the following:
• Concrete Finishers
• Concrete Vibrators
• Soil Stabilizers
• Road Reclaimers
• Pavement Profilers
• Other Misc. Paving Equipment (Catch All)
5) Surfacing Equipment includes the following:
• Asphalt/Gravel Planers
• Asphalt Mixers/Agitators
• Crack/Joint Routers
• Pumper Ketcles/Melters
• Roofing Equipment
• Other Misc. Surfacing Equipment (Catch All)
6) Trenchers include the following:
• Portable/Walk-Behind Trenchers
• Riding Trenchers
• Cable Layers
• Wheel Trenchers
7) Bore/Drill Rigs include the following:
• Horizontal Boring Machines
• Self Propelled Drills
• Truck-Mounted Drills
-------
8) Excavators Include the following:
• Dragline Excavators
• Hydraulic Excavators
9) Cranes include the following:
• Pedestal Cranes
• Rough Terrain Cranes
• Shovel-Type Cranes
• Straddle Cranes
• Truck Mounted Cranes
10) Other Construction Equipment includes the following:
• Concrete Pumps
• Other Misc. Construction Equipment (Catch All)
A-9
-------
CLASS 8
AGRICULTURAL EQUIPMENT
Equipment Types PSR Code
1. 2-Wheel Tractors 98
2. Agricultural Tractors 45
3. Agricultural Mowers 55
4. Combines 47
5 . Sprayers 69
6. Balers 49
7. Irrigation Sets 44
8. Tillers >5 HP 59 ( > 5 HP)
9. Swathers 48
10. Hydro Power Units 85
11. Other Agricultural Equipment 46
Notes:
1) Agricultural Tractors include 2-wheel and 4-wheel drive tractors, as well
as Track-Type Agricultural Tractors. Low horsepower Agricultural Tractors and
high horsepower Garden Tractors are differentiated by a horsepower cucpoint.
Tractors above 20 horsepower are included in Agricultural Tractors and
tractors below 20 horsepower are included in Lawn and Garden Tractors.
2) Sprayers includes the following:
• Back Pack Sprayers
• Self Propelled Sprayers
• Towable/Tractor-Mounted Sprayers
• Fertilizer Spreaders
-------
3) Other Agricultural Equipment includes the following:
• Harvesters
• Detasslers
• Cotton Strippers/Pickers
• Orchard Pruners
• Leaf Harvesters
• Fruit/Nut Harvesters
• Forage Harvesters
• Frost/Wind Mills
• Other Misc. Agricultural Equipment (Catch All)
A-ll
-------
CLASS 9
LOGGING EQUIPMENT
Equipment Types PSR Code
1. Chainsaws >4 HP 70 ( > 4 HP)
2. Shredders >5 HP 96 ( > 5 HP)
3 . Skidders 25
4. Fellers/Bunchers 24
Notes:
1) Delimbers are included under Fellers/Bunchers.
2) Portable Saw Mills are included in Concrete/Industrial Saws in the
Construction Equipment class.
-------
APPENDIX B
PSR's Survey Questionnaire
-------
POWER PRODUCTS PARTS SURVEY
********************************************************
Please check the types of engine-powered products which you operate:
Agricultural Machinery Industrial Equipment
. Construction Machinery Boat
Material Handling Equipment Automobiles/Trucks/RVs
Lawn & Garden Other ._.
o On the following table, please complete Columns B and C and then
select specific engine makes and models that you operate. Enter the
Identification for a different engine at the head of each remaining column.
(Note: If you operate more than eight engines, please select several
typical engines.)
o On Line 1, please fill In the name of the manufacturer of the product in
which this engine is installed.
o On Line 2, enter the name of the engine manufacturer.
o On Line 3, enter the engine model designation.
o On Line 4, please enter a description of the application for each engine
from the list shown on page 4 which most closely describes your
application.
o On Line 5. enter your best estimate of the hours of operation for this
engine in 1986.
o On Line 6, please indicate the engine age in years.
o For each of the parts or components listed, pleasa provide your
expenditure for these parts on this engine during 1986.
Please make any necessary corrections on your address. One drawing entry and one
operating comparison will bo produced for each engine reported.
B-l
-------
•A. -B.
Engine Components For each of the parts or When you purchase each >
components nsted In Column A, components Ueted In Column A.
please write In the brand name please Indicate the source you would
you have purchased most
frequently during the past year.
»***««•••••»«••••••*•«*•*•••»•»«••••
n wMah «(gM pradud wt«
6 • Oinftutar tor th« ««Me ««^N« Imnd
R • A«atar, weft •• auto pent or hvdwara itor*
O.Ottw
••••••«•**•*••••••**«•••••«•••«•••••
Pistons
Piston Ring*
Cylinder Un«n
Main A Connecting Rod Baaring*
Exhaust A lntak» VaJvM
Spaik Plugs
(njtotlon Nozzias
Fual Fiften
Gaskets A Seals
Oil Rlters
Air Flleni
Fuel Pump (Gasoline)
Fuel Injection Pump (Diesel)
Water A Oil Pump
Thermostats
Ignition Points
Condenser
Mufflers
All Other Parts
B-2
-------
2) a^GINE MAKE
3) ENGINE
MODEL
4) Af PUCAT1ON
CODE
«)A6E
-------
For Una 4 on Pag* 3, pitas* enter the code number next to the application
listed below which most doseiy describes the products in which each engine is
Installed.
Cade Application
01 Truck Class S
02 Truck CUM a
03 Truck Class 7
04 Truck Claw •
OS Truck Class 1 and 2
09 Truck Class 3 and 4
07 Can
08 Buses
09 Generator Sets
10 Compressor!
11 Pumps
12 0(1 Field Equipment
13 Mining Equipment
14 Refrigeration
15 Tac/Mililary Vehicles
16 Ind Tractor
17 Welders
18 Forklifts
19 Other Material Handling
20 Locomotive
21 Scrubber/Sweeper
22 Concrete Paw
23 Roofing Equipment
24 Fell/Bunch
25 SJdddsrs
20 Chlppers
27 Cranes
28 Excavators
29 Scrapers
30 Graders
31 Crawler Tractor
32 R/T Dozer
33 R/T Loader
34 Ct/Pr Equipment
35 Paving Equipment
36 Other Construction
37 B/D Rigs
38 3/3 Loader
39 RoUer/Comp
40 Off-Highway Trucks
41 Asphalt Paver
42 Trenchers
Code Application
43 Backhoee
44 Irrigation Sets
45 Ag Tractor
46 Other Ag Equipment
47 Combines
48 Swathera
49 Balers
50 Powerboats
51 Marine Com
52 Olst Loose
53 Trimmers
54 Loose Exports
55 Mowers
56 Snowblowe*
57 C/M Mixers
58 Pres Washer
59 Tillers
60 Dumpers
61 Tampers
62 Spec Vehicles
63 Lawn A Garden Tractor
64 AerUft
65 Lawn Mowers
66 LfBlw/Vac
67 CommTurf
68 Sprayers
69 Chainsaws
70 Snowmobile
71 Signal Brd
72 U Plants
73 Other General Industrial
74 Wood Splitter
75 Other Lawn A Garden
76 Vehlde Re
77 Sailboat A.UH
78 Railway Malnt
79 Aircraft Sot
80 Marine Mil
81 Golf Cars
82. AerUft
B-4
-------
POWER PRODUCTS PERFORMANCE FOLLOW-UP
Thank you for your help In our POWER PRODUCTS PERFORMANCE
SURVEY. Your name has been entered in our Power Products Sweepstake*
- ones for each model submitted. Here's a chance to extend your chances I!
For some of the equipment you operate (Indlcatsd below) we would like to
Identify fuel consumption data. Please provide complete the Information for
the following and we will enter your name once again tor each of these
models.
For each of the following models you operate please provide fuel consumption
data and Indicate whether the data is from actual records or an estimate.
MAJd MOOB. HOiM/ FUtL P0I ACTUALOft
YEAH CONSUME HOUWCAH EffTIMATB
(04om) (otttaon*
HraVr. AollM
HnYr_ ActfiM
PIMM ralum in tfM •«
B-5
-------
APPENDIX C
Agricultural Census' Equipment Populations
-------
Agricultural Equipment Populations
From 1987 Agricultural Census
By Nonattainment Area
Nonattainment Area
Nation
Baltimore CMSA
Chicago CMSA
Denver CMSA
Houston CMSA
Milwaukee CMSA
Philadelphia CMSA
Seattle -Tacoma CMSA
Boston NECMA
Hartford NECMA
New York CMSA
Atlanta CMSA
Baton Rouge CMSA
Cleveland CMSA
El Paso CMSA
San Juaquin Valley AB
South Coast AB
Miami CMSA
Minneapolis -St. Paul CMSA
Provo-Orem CMSA
San Diego AB
Spokane CMSA
St. Louis CMSA
Washington, DC CMSA
Springfield NECMA
Wheel
Tractors
4,609,388
10,702
21,951
4,891
12,246
10,629
20,231
5,907
3,964
3,367
19,270
6,952
2,259
10,365
730
70,865
11,771
3,599
29,528
2,629
5,195
3,027
22,871
12.588
2,497
Grain/
Bean
Combines
667,128
827
3,916
410
825
1,116
1,265
68
0
48
663
235
128
1,411
30
1,297
153
10
3,864
102
55
741
4,737
784
10
Cotton- Mower
Pickers Conditioners
42.914
0
0
0
368
0
0
0
0
0
0
3'
o:
0
137
4,115
57
0
0
0
0
0
0
0
0
652,193
1,984
2,016
788
2,395
1,846
2,573
733
456
512
2,583
1,276
370
1,906
77
3,197
674
537
5,681
618
617
798
2,818
2,281
357
Pickup
Balers
822,927
2,087
2,335
943
1,431
1,952
2.650
560
47L
487
2,467
1.2-91
330
2,077
100
1,369
373
29
6,481
631
87
821
3,985
2.4-0
^22
Source: 1987 Agricultural Census, Geographic Area Series (Table 8 - County Data)
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