PRELIMINARY NON-ATTAINMENT AREA POPULATION
ESTIMATES FOR OFF-ROAD EQUIPMENT
Energy and Environmental Analysis, inc.
1655 NORTH FORT MYER DR	ARLINGTON, VIRGINIA 22209

-------
PRELIMINARY NON-ATTAINMENT AREA POPULATION
ESTIMATES FOR OFF-ROAD EQUIPMENT
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, Inc.
1655 North Fort Myer Drive
Arlington, Virginia 22209
July 5, 1991

-------
PRELIMINARY NON-ATTAINMENT AREA POPULATION
ESTIMATES FOR OFF-ROAD EQUIPMENT
1.	INTRODUCTION
This memorandum presents EEA's preliminary population estimates of off-road
equipment for each of the 23 non-attainment areas included in EPA's study on
the contribution of non-road engines to emission inventories, The estimates
were derived using EEA's methodology described in Methodology to Estimate Off-
Road Equipment Populations, submitted to EPA on May 4, 1991. In addition to
non-attainment area population estimates, this memorandum presents national
data by equipment type on load factors, usage factors, and horsepower. A
discussion regarding necessary changes to EEA's equipment classification
scheme is also presented.
County level population estimates are not presented for marine equipment,
airport service equipment, nor logging equipment. Population estimates for
marine equipment will largely be derived from each state's Department of
Natural Resources (DNR). EEA is in the process of contacting states to obtain
this data. Airport service equipment population estimates will be delivered
to EPA as an addendum to this memorandum. Population estimates for logging
equipment were to be delivered to EPA in this memorandum. However, the
methodology proved no statistical relationship between the indicators and
state populations for these equipment (see Section 3 of this memorandum). As
a result, EEA will use the backup methodology to arrive at county level
populations, and deliver them along with airport service equipment estimates.
2.	REVISIONS TO THE EQUIPMENT CLASSIFICATION SCHEME
Originally, EEA and EPA had identified over 100 equipment types that were to
be considered in the analysis. However, because EEA's methodology mostly
relies on data from Power Systems Research, it became necessary to alter the
classification scheme. The equipment types that PSR includes in its
AFTERMARKET data base did not map exactly to those originally specified by
1

-------
EEA. These mapping problems were mostly caused by EEA's specification of
certain low volume equipment as independent elements in the analysis. For
example, EEA had originally defined forage harvesters, leaf harvesters, and
fruit/nut harvesters as independent equipment types for which population
estimates were to be derived separately. This created mapping problems,
however, since PSR aggregates populations for these equipment (as well as
others) into what they define as Other Agricultural Equipment. Given that
harvesting equipment account for less than 440 nationwide unit sales per year,
aggregating populations for the purpose of emissions inventory calculations
does not present a major problem - specifically when the equipment use similar
engines.
The fact that PSR provided less detailed data caused changes in the equipment
classification scheme. The new classification scheme is shown in Appendix A.
The most striking difference between the classification scheme in Appendix A
and that presented in Methodology to Estimate Off-Road Equipment Populations
is that under the new scheme the Public Utility class no longer exists. Under
the new scheme, equipment used by municipalities is included in other
equipment classes. For example, sweepers used by municipalities are now
included in the Industrial Equipment class (Sweepers/Scrubbers), since PSR
does not differentiate between sweepers used in industrial applications from
those used by municipalities. Similarly, leaf collectors and vacuums are now
under Lawn and Garden (Leaf Blowers/Vacuums), and account for a relatively low
volume of 1,300 units per year. Snow removal equipment are also included in
Lawn and Garden (125 units per year), while highway mowing equipment are
represented by Agricultural Mowers in the Agricultural Equipment class and
Commercial Turf Equipment in the Lawn and Garden class.
Another difference between the new and old classification schemes is that the
use of sub-classes to categorize equipment by similar applications across an
equipment class has been discontinued. Sub-classes were deemed inappropriate
since they did not strengthen the statistical results. In addition, the use
of classes to characterize similar equipment was a major concern of EMI -
their argument being that many equipment are used in different applications.
2

-------
While it is not practical, from an analytical perspective, to do away
completely with the classification scheme, discontinuing the use of sub-
classes should help to subdue some of the manufacturers' concerns.
3. METHODOLOGY
The methodology to distribute equipment populations to the non-attainment area
makes use of activity indicators and state level populations for equipment
classes. State level populations for each equipment category were acquired
from PSR, while activity indicators were determined from economic data
presented in the various Census publications. PSR obtains detailed sales data
from manufacturers and dealers at the national level, and then utilizes engine
life data as well as data on hours of use per year to derive a statistical
scrappage curve, and hence estimate national populations. Given national
population estimates. PSR employs D.S. Census data. PSR's survey data, and
reports from dealers to distribute equipment to the state level.
In EEA's methodology, the relationship between specific activity indicators
and an equipment class' state population is determined by regression analysis.
In general, the model is formulated as follows:
STPOPij = b0 + b1*(AI1) + b2*(AI2) + ... + bn*(AIn) ,
where, STPOP is state i's population of equipment class j and AIX through AIn
are the activity indicators for the equipment class at the state level. The
estimated coefficients will provide the activity indices for each activity
indicator, and are defined as b% for k = 1, 2	n.
Given the statistical relationship between equipment class j's population and
the activity indicators (AIk for k = 1, 2, ..., n), non-attainment area
populations can be estimated by using activity indicators for those counties
in the non-attainment area as follows:
NONARtj - b*0 + bV(AIi) + b*2*(AI2) + ... + b*n*(AIn) ,
3

-------
where, NONARtJ is non-attainment area t's estimated population of equipment
class j , b*k are the estimated activity indices and AIk are now the activity
indicators for non-attainment area t (i.e., the sum of activity in the
counties of non-attainment area t), for k - 1, 2, ..., n.
Finally, the estimate for NONARtj must be adjusted to reflect prediction error
at area t's state level. Let ADNONAR^ be area t's adjusted population of
equipment class j. Then ADNONARtJ is defined as:
ADNONARtJ - NONARtJ * r-
where, r = actual STPOPjj	.
predicted STPOPij
For non-attainment 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 class j can
be found by applying the ratio of that type's national population relative to
the national population of class 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 non-attainment level.
This section provides population estimates derived by the above methodology.
For each equipment class, many regression models employing different activity
indicators were tested to determine the "best" model to estimate equipment
populations at the non-attainment area level. EEA used three criteria to
determine the "best" model:
4

-------
•	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 not be significantly different
from zero at a 95% 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 95% 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 class' "best" model.
3.1 Class 1: Lawn 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 that
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.
EEA used two activity indicators to distribute lawn and garden equipment from
the state level to the non-attainment area level. First, the number of single
family housing units in a given area provides an estimate of the number of
lawn 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
5

-------
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
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 Class 1 equipment as the dependent variable. The regression results are
presented in Table 1. The condition indices in Table 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 Class 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.
3.2 Class 3: Recreational Equipment
Determining an activity indicator for recreational equipment proved to be
difficult at first. EEA tested many general indicators (such as, per capita
income, population density, and percent of land that is public), but found no
significant statistical relationships. 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 2. It is clear that the model meets the first two
criteria for "best" model. Realizing that most motorcycle dealers also sell
ATV's, off-road motorcycles, minibikes, snowmobiles, and other recreational
equipment, the use of SIC 557 as the activity indicator for Class 3 also is
intuitively consistent - satisfying the third criterion.
While data for SIC 557 was available for most non-attainment 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
6

-------
Table 1
Class 1: Lawn and Garden Equipment
"Best" Model
MODEL: PSRCLS1 - a + 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
1
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
DEP VAR: PSRCLS1	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
-0.685108
0.403371
1.000000
-0.898867
1.000000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.205558E+09
.551403E+07
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
2 .102779E+09 372.790208
20 .275702E+06
0.000002
7

-------
Table 2
Class 3: Recreational Equipment
"Best" Model
MODEL: PSRCLS3 - a + b(EST557)
PSRCLS3 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 3 (xlOOO)
EST557 = SIC 557 (ESTABLISHMENTS) - MOTORCYCLE DEALERS
DEP VAR: PSRCLS3	N:	23 MULTIPLE R: .919 SQUARED MULTIPLE R: 844
ADJUSTED SQUARED MULTIPLE R: .837 STANDARD ERROR OF ESTIMATE: 26.655695
VARIABLE
CONSTANT
EST557
COEFFICIENT
1.760700
0.616462
STD ERROR
8.663613
0.057767
STD COEF TOLERANCE
P(2 TAIL)
0.000000	0.20323 0.84091
0.918862 1.00000 .11E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
. 809155E+05
.149210E+05
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 .809155E+05 113.881062
21 710.526056
0.000000
8

-------
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 the
analytical approach, although it is less intuitive Regression results for
this alternative model are shown in Table 3.
3.3 Class 5: Llaht 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, in regard to applications, created difficulties in the
identification of relevant activity indicators
EEA tested many models 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. Note that while the model meets two of
the criteria for "best" model, its R2 is below 0.8 at 0.698. Figure 1 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 h. Clearly, Texas and New York are outliers
in this model; Texas' equipment population being 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 will provide
reliable estimates. Moreover, given that the methodology adjusts for
estimation errors through r, the estimates for non-attainment areas in New
York and Texas will also be reliable, although not as much so
9

-------
Table 3
Class 3: Recreational Equipment
Alternative Model for Selected Non-Attainment Areas
MODEL: PSRCLS3 - a + b(EMP55)
PSRCLS3 - PSR STATE EQUIPMENT POPULATIONS FOR CLASS 3 (xlOOO)
EMP55 - SIC 55 (EMPLOYEES) - AUTOMOTIVE DEALERS AND SERVICE STATIONS (xlOOO)
DEP VAR: PSRCLS3	N:	23 MULTIPLE R: .942 SQUARED MULTIPLE R: .887
ADJUSTED SQUARED MULTIPLE R: .881 STANDARD ERROR OF ESTIMATE: 22.744393
VARIABLE
CONSTANT
EMP55
COEFFICIENT
-9.785675
1.267529
STD ERROR
7.993347
0.098899
STD COEF TOLERANCE
P(2 TAIL)
0.000000	-1.22423 0.23442
0.941619 1.00000 .13E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.849731E+05
.108635E+05
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 .849731E+05 164.260267
21 517.307411
0.000000
10

-------
Table 4
Class 5: Light Commercial Equipment
"Best" Model
MODEL: PSRCLS5 = a + b(ESTWHSL)
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
ESTWHSL
COEFFICIENT
-2.312631
8.551879
STD ERROR
24.130211
1.226572
STD COEF TOLERANCE
P(2 TAIL)
0.000000	-0.09584 0.92456
0.835658 1.00000 6.97218 0 00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
.271575E+06
.117320E+06
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 .271575E+06 48.611247
21 5586.679562
0.000001
11

-------
Figure 1
Light Commercial Equipment
Regression Fit and Outliers
Equipment Populations (x1000)
600
0 Texa
500
400
300
N ew Yo r k
200
100
60
40
50
30
20
10
0
Wholesale Trade - Establishments (x1000)

-------
3.4	Class 6: Industrial Equipment
Industrial equipment are mostly used in various manufacturing activities As
a result, EEA made use of manufacturing activity levels at state and county
levels to distribute national populations of these equipment to the each non-
attainment area. Specifically, EEA used the number of employees engaged in
manufacturing as the activity indicator for Class 6, and regressed these data
on PSR's state populations for industrial equipment. This model met all three
criteria, as shown by Table 5.	1
3.5	Class 7: Construction Equipment
Originally, EEA had anticipated separate models for road construction
equipment and general construction equipment. Various models were formulated
for both subclasses of construction equipment using the following indicators:
SIC 161 - Road Construction, total construction activity, and general
construction activity (total minus road). Both subclasses 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, EEA decided to analyze construction equipment as
one class (road plus general) using total construction activity as the
indicator. Regression results for this "best" model are presented in Table 6.
The model exhibits excellent statistical validity while considerably
simplifying the analysis.
3.6	Class 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, and in such
circumstances EEA provided that data to EPA.
However, for the bulk of equipment in the Agricultural Equipment class, county
nor state level populations are available. Therefore, EEA tested many
combinations of activity indicators to determine their reliability in
13

-------
Table 5
Class 6: Industrial Equipment
"Best" Model"
MODEL: PSRCLS6 - a + b(EMPMFG)
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: .934
ADJUSTED SQUARED MULTIPLE R: .930 STANDARD ERROR OF ESTIMATE: 2 734937
VARIABLE
CONSTANT
EMPMFG
COEFFICIENT
-0.379266
0.020828
STD ERROR
0.927407
0.001212
STD COEF TOLERANCE
P(2 TAIL)
0.000000	-0.40895 0.68671
0.966237 1.00000 .17E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
2209.064094
157.077543
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 2209.064094 295.334044
21 7.479883
0.000000
14

-------
Table 6
Class 7: Construction Equipment
"Best" Model
MODEL ' PSRCLS7 - a + 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: .895
ADJUSTED SQUARED MULTIPLE R: .890 STANDARD ERROR OF ESTIMATE: 23.878076
VARIABLE
CONSTANT
EMPCST
COEFFICIENT
-4.566209
0.501182
STD ERROR
7.866897
0.037480
STD COEF TOLERANCE
P(2 TAIL)
0.000000	-0 58043 0.56780
0.945991 1.00000 .13E+02 0.00000
SOURCE SUM-OF-SQUARES
REGRESSION
RESIDUAL
. 101949E+06
.119734E+05
ANALYSIS OF VARIANCE
DF MEAN-SQUARE F-RATIO
1 .101949E+06 178.807047
21 570.162510
0.000000
15

-------
distributing national populations to each non-attainment 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. EEA tested various
combinations of these indicators 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, EEA tested the relationship between an adjusted SIC 07 - Agricultural
Services (Employees). 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. EEA formulated a
model using this adjusted SIC 07 as the independent variable to estimate
agricultural equipment populations. The results of this model are presented
in Table 7. Clearly, the model more than met each of the criteria for "best"
model, and given the lack of a better alternative EEA employed this model in
estimating agricultural equipment populations for each of the non-attainment
areas.
3.7 Class 9: Logging Equipment
SIC code 241 - Logging - was tested for reliability as an activity indicator
to allocate logging equipment from the national level to the each non-
attainment area using the methodology described above. SIC 241 failed to meet
two of the criteria stipified for "best" model. In fact, for both
establishments and employees, the R2's were 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 80X confidence level, indicating that
the model would not provide reliable estimates.
16

-------
Table 7
Class 8: Agricultural Equipment
"Best" Model
MODEL PSRCLS8 - a + b(EMPA07)
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
EMPA07
COEFFICIENT
4.945921
14.819782
STD ERROR
5.916288
0.565719
STD COEF TOLERANCE
P(2 TAIL)
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+06 686.249866
21 482.564189
0.000000
17

-------
Given that SIC 241 Is a sub-category of SIC 24 - Lumber and Wood Products,
Except Furniture EEA next tested the reliability of SIC 24 as an indicator.
At first sight, the model using SIC 24 (number of employees) seemed to meet
two of the criteria, as shown in Table 8. 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 2 plots the residuals against the estimates of the
regression model in Table 8. 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 95% confidence.
Weighted least squares may solve help this problem, but requires extensive
analysis in the formulation of an appropriate model.
EEA was unable to determine an activity indicator that provided reliable
results for logging equipment using regression analysis. One possible problem
is that PSR's state level data for logging equipment is not derived
appropriately. EEA will discuss this possibility with PSR. At this stage of
the analysis, however, EEA plans to use the back up methodology (explained in
Methodology to Estimate Off-Road Equipment Populations) to distribute national
populations of logging equipment to each non-attainment area.
4. EQUIPMENT POPULATIONS BY NON-ATTAINMENT AREA
This section presents, in tabular form, the results of the estimation process
for deriving non-road equipment populations for each of the 23 non-attainment
areas included in the study. Populations are provided for all equipment types
except those included under logging, airport service, and marine.
18

-------
Table 8
Class 9: Logging Equipment
Biased Model
MODEL PSRCLS9 - a + 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-RATIO
1 366.691623 28.366681
21 12.926843
0.000028
19

-------
Figure 2
Logging Equipment
Distribution of Residuals
Regression Residuals (x 1000)
5	10	15	20
Esimated Populations (x 1000)

-------
Table 9 provides national populations, usage hours, and load factors by fuel
type for each equipment type. These data were acquired directly from PSR.
Table 10 provides equipment populations for each non-attainment area.
Snowmobiles and snowblowers were not allocated to those non-attainment areas
with mild climate where snowfall is non-existent or rare. In each data table,
those cells market by a hyphen will be filled in as data is made available
from PSR.
21

-------
a
a
8
8
8
8
8
8
8
8
8
T 9
National Populations, Load Factors, and
Hours of Use by Equipment and Fuel Type
National
X of	Dleael-
-Gaaollne-
Equipment Type*
Diesel
Populatlona—
Gasoline
Total
X
Diesel
Class
Total
Uaage
Hours
Load
Factor
Ma an
HP
Uaage
Hours
Load
Factor
Mean
BP
21.401
18
172
282
18
193
683
0
12X
17
61X
35
43
OOX
2
0
28
76
OX
1
0
0
42
733
069
42
733
069
0
oox
41
361
0
0
OOX
0
0
76
81
OX
3
0
0
2
023
786
2
025
786
0
oox
1
96X
0
0
OOX
0
0
56
90
ox
1
0
4,723
1
484
039
1
488
784
0
32X
1
44X
975
64
OOX
16
5
352
67
ox
10
0
0

134
836

134
856
0
oox
0
13X
0
0
OOX
0
0
352
67
ox
16
0
0
16
124
970
16
124
970
0
OOX
15
61X
0
0
OOX
0
0
26
92
ox
2
0
0

131
372

131
372
0
oox
0
13X
0
0
OOX
0
0
50
80
ox
5
0
0
7
693
276
7
693
276
0
oox
7
45X
0
0
OOX
0
0
43
71
ox
5
0
241,919
7
749
492
7
991
411
3
03X
7
73X
544
62
OOX
17
0
143
62
ox
18
0
79
1
330
139
1
350
238
0
01X
1
31X
265
55
OOX
58.0
76
69
ox
4
0
0
4
067
391
4
067
391
0
oox
3
94X
0
0
OOX
0
0
72
78
ox
2
0
17.087

16
791

33
878
50
44X
0
03X
465
73
OOX
76
0
488
78
ox
61
0
87,807

480
925

568
732
15
44X
0
55X
1068
59
OOX
20
0
917
61
ox
16
0
180

792
978

793
158
0
02X
0
77X
433
65
OOX
22
0
61
58
ox
3
5
0
1
312
981
1
312
981
0
oox
47
39X
0
0
OOX
0
0
135
72
ox
19
0
0

48
990

48
990
0
oox
1
77X
0
0
oox
0
0
55
62
ox
4
0
0

201
125

201
125
0
oox
7
26X
0
0
oox
0
0
120
76
ox
35
0
0

122
670

122
670
0
oox
4
43X
0
0
oox
0
0
1080
46
ox
9
0
0

776
339

776
359
0
oox
28
03X
0
0
oox
0
0
121
81
ox
28
0
3,344

303
209

308
553
1
08X
11
14X
435
65
oox
62
0
65
58
ox
7
0
198,391
2
943
286
3
141
677
6
31X
65
55X
350
65
oox
24
0
120
62
ox
8
0
61,810

631
688

713
498
8
66X
14
89X





-

-

-
13,713

176
124

191
837
8
19X
4
OOX
1105
60
001
22
0
915
60
ox
11
0
0


-


-
0
OOX

-
0
0
oox
0
0
-

-

-
100,490

330
345

451
035
22
28X
9
4IX
305
38
oox
24
0
305
35
ox
20
0
3.943

290
939

294
902
1
34X
6
15X
-

-

-
-

-

-
12.310

28
388

40
698
30
251
10
81X
384
46
oox
35
0
361
46
ox
30
0
114,178

109
474

223
652
51
05X
59
39X
858
58
oox
72
0
806
63
ox
59
0
36.977

23
892

62
869
58
821
16
69X
1220
68
oox
70
0
516
71
ox
46
0
18,366

23
724

42
090
43
64X
11
18X
878
51
oox
100
0
713
34
ox
16
0
3,238

2
036

7
294
72
09X
1
94X
421
39
oox
101
0
386
S3
ox
48
0
13,336

3
022

18
558
83
72X
0
73X
821
62
oox
105
0
392
66
ox
23
0
-

23
611

23
611

-
0
93X
-

-

-
160
55
ox
4
0
2,322

274
179

276
501
0
641
10
861
484
43
oox
8
0
166
35
ox
5
0
3,311


0

5
511
100
OOX
0
22X
821
68
oox
113
0
0
0
ox
0
0
86,818

21
999

108
817
79
7BX
4
28X
745
36
oox
80
0
621
62
ox
8
0
43,007


0

43
007
100
OOX
1
69X
914
72
oox
350
0
0
0
ox
0
0
43,613

230
810

274
425
15
89X
10
78X
622
53
oox
120
0
175
59
ox
7
5
0

30
833

30
833
0
OOX
1
21X
0
0
oox
0
0
488
49
ox
10
0
20,384

1
559

21
943
92
90X
0
86X
815
82
oox
7
0
241
76
ox
8
0
30,510

27
170

77
680
65
02X
3
05X
593
75
oox
56
0
402
66
ox
20
0
7,761

8
501

16
262
47
72X
0
64X
466
75
oox
58
0
107
79
ox
18
0
61.336


18

61
354
99
97X
2
41X
859
57
oox
152
0
378
S3
ox
80
0
133

36
900

37
035
0
36X
1
46X
580
73
oox
33
0
610
78
ox
9
0
4,016

232
152

236
168
1
70X
9
28X
275
56
oox
11
0
84
59
ox
6
0
98,337

2
541

100
898
97
48X
3
96X
806
43
oox
650
0
415
47
ox
61
0
70,043


0

70
045
100
OOX
2
75X
821
61
oox
ISO
0
0
0
ox
0
0
16,329


0

16
529
100
OOX
0
65X
1641
57
oox
330
0
0
0
ox
0
0
7,207

1
007

8
214
87
741
0
32X
955
78
oox
38
0
241
85
ox
16
0
33,833

2
217

56
070
96
05X
2
20X
662
60
oox
80
0
413
63
ox
70
0
209,434

3
433

212
887
98
39X
8
371
761
68
oox
216
0
512
71
ox
70
0
7,757


0

7
757
100
OOX
0
301
899
59
oox
333
0
0
0
ox
0
0
299.263

1
365

300
630
99
55X
11
BIX
1135
55
oox
80
0
870
48
ox
56
0
283,923


0

285
923
100
OOX
11
24X
936
64
oox
180
0
0
0
ox
0
0
130,034

27
805

177
859
84
37X
6
99X
818
55
oox
35
0
310
58
ox
37
0
38,921


0

38
921
100
OOX
1
53X
855
65
oox
233
0
0
0
ox
0
0
194

24
301

24
495
0
79X
0
96X
566
38
oox
23
0
127
41
ox
10
0
11,867

1
103

12
970
91
SOI
0
51X
606
62
oox
58
0
371
48
ox
150
0
0


-


0

-
0
OOX
0
0
oox
0
0
286
62
ox
4
0
1.929,481

3
900
1
935
3B1
99
70X
54
98X
475
70
oox
185
0
550
62
ox
45
0
-

16
023

16
023
0
OOX
0
46X
-

-

-
175
48
ox
6
0
284,846

1
843

286
689
99
36X
8
14X
ISO
70
oox
185
0
125
74
ox
60
0
9,693

72
720

82
413
11
76X
2
34X
90
58
oox
85
0
80
65
ox
18
0
2,033

31
437

33
470
6
07X
0
95X
95
58
oox
98
0
68
62
ox
37
0
89,706

45
948

135
654
66
13X
3
B5X
749
90
oox
100
0
816
65
ox
59
0
40

920
594

920
634
0
OOX
26
15X
172
78
oox


43
71
ox
5
0
50,031

32
858

82
889
60
36X
2
351
110
55
oox
80
0
95
52
ox
132
0
2,365


-

2
365
100
OOX
0
07X
790
48
oox
33
0
-



-
18,043

6
404

24
447
73
80X
0
69X
381
51
oox
58
0
124
55
01
9
0
Trlmners/Edgers/Bruab Cuttara
Lawn Mowers
Leaf Blowera/Vacuuns
Rear Engine Riding Mowera
Front Howara
Chalnaawa <4 BP
Shraddara <5 BP
Tlllara <5 BP
Lawn & Garden Tractors
Wood Spllttara
Snowblowers
Chlppars/Stimp Grlndara
Comserclal Turf Equipment
Other Lam & Gardan Equlpmant
All Tarraln Vahlclas (ATvs)
Hlnlblkas
Off-Road Motorcycles
Golf Carta
Snowmobiles
Specialty Vahlclas Carta
Generetor Sata	<30 BP
Pumps	<50 BP
Air Coaqpressors	<50 BP
Gaa Compressors	<50 BP
Waldara	<50 BP
Praaaura Haahara <50 BP
Aarlal Lifts
Forkllfta
Sweepers/Scrubbers
Othar Ganaral Industrial Equlpmant
Othar Hatarlal Handling Equlpmant
Asphalt Pavara
Tampara/Ramoara
Plata Compactors
Concrata Pavara
Rollara
Scrapara
Paving Equipment
Surfacing Equipment
Signal Boaras
Trenchara
Bora/Drill Riga
Excavators
Concrete/Industrial Saws
Camant and Mortar Mixers
Cranes
Gradera
Off-Blghway Trucks
Cruahlng/Proc Equipment
Rough Tarraln Forkllfta
Rubber Tired Loaders
Rubber Tired Doxera
Tractore/Loadars/Backhoes
Crawler Tractors
Skid Steer Loaders
Off-Blghway Tractors
Dumpers/Tenders
Othar Construction Equipment
2-Wheel Tractors
Agricultural Tractora
Agricultural Mowers
Combines
Sprayers
Balera
Irrigation Sats
Tillers >5 BP
Swathers
Hydro Power Units
Other Agricultural Equipment

-------
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
e
e
6
e
8
8
8
8
8
8
8
799
174
404
808
B91
093
842
870
658
931
030
235
743
119
680
324
330
811
134
018
995
298
513
002
151
544
098
496
455
39
41
323
752
0
301
0
159
422
21
372
116
0
505
177
35
0
0
14
30
47
0
19
0
381
0
333
15
70
190
22
862
373
545
914
390
76
labie lu
Estimated Non-Attainment Equipment
Populations by Fuel Type
0	12	3	4
Baltimore CMSA	Chicago CMSA	Denver CKSA	Houston CMSA	Milwaukee CMSA
Equipment Types
Diesel
Gasoline
Diesel
Gasoline
Diesel
Gasoline
Diesel
Gasoline
Diesel
Gasoline
Trioniers/Edgers/Brush Cutters

188
159,504
584
495,911
252
214,018
427
362,869
117
99,329
Lawn Mowers

0
375,081
0
1.166,161
0
503,273
0
853,306
0
233,577
Leaf Blowers/Vacuums

0
17,781
0
55,283
0
23,858
0
40,451
0
11,073
Rear Engine Riding Mowers

41
13,026
129
40.499
56
17.47B
94
29,634
26
8.112
Front Mowers

0
1,184
0
3,680
0
1,588
0
2,693
0
737
Chalnsaws <4 HP

0
141,534
0
440,041
0
189,906
0
321,988
0
88,138
Shredders <5 BP

0
1,153
0
3,585
0
1,547
0
2,623
0
718
Tillers <5 BP

0
67,526
0
209,945
0
90,605
0
153,622
0
42.051
Lawn & Garden Tractors
2,
,123
68,020
6,602
211,479
2.849
91.267
4,831
154.744
1,322
42.358
Wood Splitters

1
11,651
1
36,845
1
15,901
1
26,960
1
7,380
Snowblowers

0
35,701
0
110,997
0
47,902
0
0
0
22,232
Chippers/Stump Grinders

ISO
147
466
458
201
198
341
335
93
92
Commercial Turf Equipment
Other Lawn & Garden Equipment
All Terrain Vehicles CATVs)

771
4,221
2,396
13,124
1,034
5,664
1,753
9,603
480
2,629

2
6,960
5
21,640
2
9,339
4
15,834
1
4,334

0
2,358
0
13,435
0
5.837
0
11,581
0
2.274
Minlbikes

0
88
0
501
0
218
0
432
0
85
Off-Road Motorcycles

0
361
0
2,058
0
894
0
1,774
0
348
Golf Csrts

0
220
0
1,255
0
545
0
1,082
0
212
Snowmobiles

0
1,395
0
7,946
0
3,452
0
0
0
1,345
Specialty Vehicles Carts

6
548
34
3,123
15
1.357
29
2.692
6
529
Generator Sets <50 HP
1,
, 267
18,794
4,982
73,918
2,615
38,793
5,649
83,802
891
13,216
Pumps <50 HP

395
4.161
1,552
16,367
815
8,589
1,760
18,555
278
2,926
Air Compressors <50 HP

100
1,125
395
4,423
207
2,321
447
5,015
71
791
Gas Compressors <50 BP

0
-
0
-
0
-
0
-
0
-
Welders <50 HP

642
2,238
2, 524
8,804
1.324
4.620
2,861
9,981
451
1.574
Pressure Washers <50 HP

25
1,858
99
7,307
52
3.835
112
8,284
18
1.306
Aerial Lifts

72
166
469
1,082
119
275
177
408
107
246
Forklifts

666
639
4,352
4,173
1,107
1,062
1,642
1,574
989
948
Sweepers/Scrubbers

216
151
1,409
987
359
251
532
372
320
224
Other Generel Industrial Equipment

107
138
700
904
178
230
264
341
159
205
Other Material Handling Equipment

31
12
200
78
51
20
76
29
46
18
Asphslt Pavers

141
27
454
88
195
38
475
92
78
15
Tampers/Ranmers
Plate Compactors

-
214
-
690
-
296
-
722
-
119

21
2,480
68
8,009
29
3,435
71
8,385
12
1,381
Concrete Pavers

50
0
161
0
69
0
169
0
28
0
Rollers

785
199
2.536
643
1,088
276
2,655
673
437
111
Scrapers

389
0
1,256
0
539
0
1,315
0
217
0
Paving Equipment

394
2,087
1,274
6,742
546
2,891
1,334
7,058
220
1.162
Surfacing Equipment
Signal Boards

0
184
279
14
0
595
901
46
0
255
386
20
0
623
943
48
0
103
155
8
Trenchers

457
246
1.475
794
633
340
1,545
831
254
137
Bore/Drill Rigs

70
77
227
248
97
106
237
260
39
43
Excavators

555
0
1,792
1
768
0
1,876
1
309
0
Concrete/Industrial Saws

1
334
4
1,078
2
462
4
1,128
1
186
Cement and Mortar Mixers

36
2,100
117
6,781
50
2,908
123
7,099
20
1,169
Cranes

890
23
2,873
74
1,232
32
3,008
78
495
13
Graders

633
0
2.046
0
877
0
2,142
0
353
0
Off-Highway Trucks

149
0
483
0
207
0
505
0
83
0
Crushing/Proc Equipment
Rough Terrain Forkllfts
Rubber Tired Loaders

65
9
211
29
90
13
220
31
36
5

487
20
1,573
65
675
28
1,647
68
271
11
1,
,894
31
6,118
100
2,624
43
6,405
105
1,055
17
Rubber Tired Dozers

70
0
227
0
97
0
237
0
39
0
Trsctors/Losders/Bsckhoes
2,
,706
12
8,741
40
3,749
17
9,152
42
1,507
7
Crawler Tractors
2,
,586
0
8,352
0
3,582
0
8,744
0
1,440
0
Skid Steer Losders
1,
,357
251
4,383
812
1,880
348
4,589
850
756
140
Off-Highway Tractors

352
0
1,137
0
488
0
1,190
0
196
0
Dumpers/Tenders

2
220
6
710
2
304
6
743
1
122
Other Construction Equipment

107
10
347
32
149
14
363
34
60
6
2-Wheel Tractors

0
-
0
-
0
-
0
-
0

Agricultural Tractors
16.
,121
49
33,372
102
20,506
63
19,934
61
5.161
16
Agricultural Mowers

-
134
-
277
-
170
-
166
-
43
Combines
2,
,380
15
4,927
32
3,027
20
2,943
19
762
5
Sprayers

81
608
168
1,258
103
773
100
751
26
194
Balers

17
263
35
544
22
334
21
325
5
84
Irrigation Sets

749
384
1,552
795
953
488
927
475
240
123
Tillers >5 HP

0
7,691
1
15,923
0
9,784
0
9,511
0
2,462
Swathers

418
275
865
568
532
349
517
339
134
68
Bydro Power Units

20
-
41
-
25
-
24
-
6
~
Other Agricultural Equipment

151
54
312
Ill
192
68
186
66
48
17

-------
8
8
8
8
8
8
8
8
8
8
8
578
664
966
906
264
370
257
062
172
643
0
33
942
553
880
145
594
363
0
902
194
257
610
214
008
45
175
41
38
3
18
140
629
0
131
0
371
183
9
161
51
0
219
379
15
0
0
6
13
20
0
8
0
165
0
144
7
6
17
2
75
33
48
956
34
7
Tauic 10 , COIlt .
Estimated Non-Attainment Equipment
Populations by Fuel Type
10
Equipment Types
Hartford NECMA
New York CMSA
Philadel CMSA
Seat -
Tae CMSA
Atlanta CMSA
Diesel
Gasoline
Diesel
Gssoline
Diesel
Gssoline
Diesel
Gasoline
Diesel
Gasoline
100
84,998
1,229
1,043,535
515
437,416

245
208,116
226
192,005
0
199,878
0
2,453,927
0
1,028,606

0
489,397
0
451,510
0
9,475
0
116,330
0
48,762

0
23,200
0
21,404
22
6.941
271
85,221
114
35,722

54
16,996
50
15,680
0
631
0
7,744
0
3,246

0
1,544
0
1.425
0
75,422
0
925,969
0
388,136

0
184,670
0
170,373
0
614
0
7,544
0
3,162

0
1,505
0
1,388
0
35,984
0
441,783
0
165,181

0
88,107
0
81,286
1,132
36,247
13.892
445,011
5,823
186,534
2,
771
88,750
2,556
81,880
1
6,315
1
77,532
1
32,499

1
15.463
1
14.266
0
19,025
0
233,568
0
97,904

0
4,658
0
0
80
79
981
964
411
404

196
192
181
177
411
2,249
5,042
27,617
2, 114
11,576
1,
006
5,508
928
5,081
1
3,709
10
45,536
4
19,087

2
9,082
2
8,378
0
3,745
0
16,183
0
5,109

0
8.349
0
4.539
0
140
0
604
0
191

0
312
0
169
0
574
0
2,479
0
783

0
1,279
0
695
0
350
0
1,512
0
477

0
780
0
424
0
2,215
0
9,571
0
3,022

0
494
0
0
10
871
41
3.762
13
1,188

21
1.941
12
1,055
693
10,278
13,433
199,293
3,584
53,177
1,
518
22.526
1,709
25.352
216
2,276
4,185
44,126
1,117
11,774
473
4,988
532
5,613
55
615
1,064
11,926
284
3,182

120
1,348
135
1,517
0
-
0
-
0
-

0
-
0
-
351
1,224
6,804
23,736
1,816
6,333

769
2,683
866
3,019
14
1,016
267
19,701
71
5,257

30
2,227
34
2,506
86
199
939
2,165
288
663

100
231
79
182
802
769
8,707
8,348
2,668
2,558

928
890
733
703
260
182
2,820
1,974
864
605

301
210
238
166
129
167
1,401
1,809
429
554

149
193
118
152
37
14
401
155
123
48

43
17
34
13
79
15
1,018
198
310
60

175
34
185
36
-
120
-
1,547
-
471

-
267
-
282
12
1,389
152
17,961
46
5,474

26
3,096
28
3,271
28
0
361
0
110
0

62
0
66
0
440
111
5,687
1,441
1, 733
439

980
248
1,036
262
218
0
2,817
0
859
0

486
0
513
0
221
1,169
2,857
15,120
871
4,608

492
2,606
520
2,753
0
156
0
2,020
0
616

0
348
0
368
103
8
1,335
102
407
31

230
18
243
19
256
138
3,309
1,780
1,008
542

570
307
603
324
39
43
508
557
155
170

88
96
93
101
311
0
4,018
1
1,225
0

693
0
732
0
1
187
9
2,417
3
737

2
417
2
440
20
1,176
263
15,208
80
4,635

45
2,621
48
2,769
498
13
6,443
166
1,964
51
1,
111
29
1.173
30
355
0
4.588
0
1,398
0

791
0
836
0
84
0
1,083
0
330
0

187
0
197
0
37
5
472
66
144
20

81
11
86
12
273
11
3,528
145
1,075
44

608
25
642
26
1,061
17
13,721
225
4,182
69
2,
365
39
2,499
41
39
0
508
0
155
0

88
0
93
0
1.516
7
19,604
89
5,975
27
3,
379
15
3,570
16
1,448
0
18,730
0
5, 70S
0
3.
228
0
3,411
0
760
141
9,830
1,821
2,996
555
1,
694
314
1,790
332
197
0
2,550
0
777
0

439
0
464
0
1
123
13
1,592
4
485

2
274
2
290
60
6
777
72
237
22

134
12
142
13
0
-
0
-
0
-

0
-
0
-
9,472
29
105,586
323
34,472
105
14,
871
45
18,476
56
-
79
-
877
-
286

-
123
-
153
1,398
9
15,587
101
5,089
33
2.
195
14
2,728
18
48
357
530
3,979
173
1,299

75
560
93
696
10
154
111
1,720
36
562

16
242
19
301
440
226
4,909
2,514
1,603
821

691
354
859
440
0
4,519
2
50,377
1
16,447

0
7,095
0
8,815
246
161
2,738
1,798
894
587

386
253
479
315
12
-
129
-
42
-

18
-
23
-
89
31
987
350
322
114

139
49
173
61
Trlmners/Edgers/Brush Cutters
Lawn Hcmers
Leaf Blowers/Vacuums
Rear Engine Riding Mowers
Front Mowers
Chainaawa <4 BP
Shredders <5 BP
Tillers <5 BP
Lawn & Garden Tractors
Wood Splitters
Snowblowers
Chippers/Stump Grinders
Commercial Turf Equipment
Other Lawn & Garden Equipment
All Terrain Vehicles (ATVs)
Minlbikes
Off-Road Motorcycles
Golf Carts
Snowmobiles
Specialty Vehicles Carts
Generator Sets	<50 HP
Pumps	<50 BP
Air Compressors	<50 BP
Gas Compressors	<50 HP
Welders	<50 HP
Pressure Washers <50 HP
Aerial Lifts
Forklifts
Sweepers/Scrubbers
Other General Industrial Equipment
Other Material Handling Equipment
Asphalt Pavers
Tampers/Rammers
Plate Compactors
Concrete Pavers
Rollers
Scrapers
Paving Equipment
Surfacing Equipment
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/Bsckhoes
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 BP
Swathers
Bydro Power Units
Other Agricultural Equipment

-------
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
8
8
8
8
8
8
8
8
8
8
8
167
126
740
26*
11*
249
086
574
038
157
611
139
974
553
337
125
511
312
974
776
252
370
451
888
397
384
479
350
321
28
33
257
983
0
239
0
511
335
17
296
92
0
401
526
28
0
0
11
24
37
0
15
0
303
0
264
12
34
94
11
425
184
268
377
192
37
Table 10, cont.
Estimated Non-Attainment Equipment
Populations by Fuel Type

12

13

14

15


16


Cleveland CMSA
El Paso CMSA
San Jq
Val. AB
South
Coast AB

Mismi
CMSA
Equipment Types
Diesel
Gasoline
Diesel
Gasoline
Diesel
Gasoline
Diesel
Gssoline
Diesel
Gasoline
Triamers/Edgers/Brush Cutters
219
186,093
35
29,403
159
134,924
1,405
1,193,197

224
190,400
Lawn Mowers
0
437,609
0
69.142
0
317,280
0
2.605,866

0
447,735
Leaf Blowers/Vacuums
0
20,745
0
3,278
0
15,041
0
133,014

0
21,225
Rear Engine Riding Mowers
48
15,198
8
2,401
35
11.019
310
97,444

50
15,549
Front Mowers
0
1,381
0
218
0
1,001
0
8.855

0
1,413
Chainsaws <4 BP
0
165,128
0
26,090
0
119,723
0
1,058,770

0
168,949
Shredders <5 HP
0
1,345
0
213
0
975
0
8,626

0
1,376
Tillers <5 BP
0
78,783
0
12,448
0
57,120
0
505,143

0
80,606
Lawn & Garden Tractors
2,477
79,359
391
12,539
1,796
57,538
15,684
508,834
2,
535
81,195
Mood Splitters
1
13,826
1
2,185
1
10,025
1
88,652

1
14,146
Snowblowers
0
41,652
0
0
0
0
0
0

0
0
Chippers/Stump Grinders
175
172
28
27
127
125
1,122
1,103

179
176
Comnerciel Turf Equipment
899
4,925
142
778
652
3,571
5,765
31,578

920
5,039
Other Lawn & Garden Equipment
2
8,121
0
1,283
1
5,888
12
52,067

2
8,308
All Terrain Vehicles (ATVs)
0
2,035
0
3,567
0
2,941
0
52,132

0
7,938
Minibikes
0
76
0
133
0
110
0
1,945

0
296
Off-Road Motorcycles
0
312
0
546
0
450
0
7,986

0
1,216
Golf Carta
0
190
0
333
0
275
0
4,871

0
742
Snowmobiles
0
1,203
0
0
0
0
0
0

0
0
Specialty Vehicles Carts
5
473
9
829
7
684
133
12.118

20
1,845
Generator Sets <50 HP
1,997
29,630
626
9,293
1,253
18,586
9,579
142,109
2,
007
29,775
Pumps <50 HP
622
6,560
195
2,058
390
4,115
2,984
31,465
625
6,593
Air Compressors <50 HP
158
1,773
50
556
99
1,112
759
8,504

159
1,782
Gas Compressors <50 HP
0
-
0
-
0
-
0
-

0
-
Welders <50 HP
1,012
3,529
317
1, 107
635
2,214
4,852
16,925
1,
017
3, 546
Prassure Hashers <50 HP
40
2,929
12
919
25
1,837
190
14,048
40
2.943
Aerial Lifts
219
SOS
37
86
67
154
813
1,874

104
240
Forklifts
2,032
1,949
348
333
618
593
7,538
7,227

966
927
Sweepers/Scrubbers
658
461
113
79
200
140
2.441
1,709

313
219
Other General Industrial Equipment
327
422
56
72
99
128
1,213
1,566

155
201
Other Material Handling Equipment
94
36
16
6
28
11
347
134

44
17
Asphalt Pavers
156
30
40
8
141
27
857
167

152
30
Tampers/Rammers
Plate Compactors
-
237
-
61
-
215
-
1.303

-
231
23
2,730
6
714
21
2.494
128
15,133

23
2.684
Concrete Pavers
55
0
14
0
50
0
304
0

54
0
Rollers
871
221
226
57
790
200
4,792
1,214

850
215
Scrapers
431
0
112
0
391
0
2,374
0

421
0
Paving Equipment
437
2,315
114
601
397
2,099
2,407
12,739

427
2,259
Surfacing Equipment
Signal Boards
0
204
309
16
0
53
80
4
0
185
280
14
0
1.125
1,702
86

0
200
302
15
Trenchers
507
273
131
71
4 59
247
2,786
1.500

494
266
Bore/Drill Rigs
78
8S
20
22
71
77
428
469

76
83
Excavators
615
0
160
0
558
0
3,385
1

600
0
Concrete/Industrial Saws
1
370
0
96
1
336
7
2,037

1
361
Cement and Mortar Mixers
40
2,328
10
604
37
2. Ill
222
12,813

39
2,272
Cranes
986
25
256
7
895
23
5,429
140

963
25
Gradera
703
0
182
0
637
0
3,866
0

686
0
Off-Highway Trucks
166
0
43
0
150
0
912
0

162
0
Crushing/Proc Equipment
Rough Terrain Forklifts
72
10
19
3
66
9
398
56

71
10
540
22
140
6
490
20
2,972
122

527
22
Rubber Tired Loaders
2,101
34
545
9
1,905
31
11,560
169
2.
050
34
Rubber Tired Dozers
78
0
20
0
71
0
428
0

76
0
Tractors/Loaders/Backhoes
3,001
14
779
4
2,722
12
16,517
75
2.
929
13
Crawler Trectors
2,868
0
744
0
2,600
0
15,781
0
2.
798
0
Skid Steer Loadera
1,505
279
391
72
1.365
253
8,282
1.535
1,
469
272
Off-Highway Tractors
390
0
101
0
354
0
2,148
0

381
0
Dumpers/Tenders
2
244
1
63
2
221
11
1,341

2
238
Other Construction Equipment
119
11
31
3
108
10
655
61

116
11
2-Nheel Tractors
0
-
0
-
0
-
0
-

0
-
Agricultural Trectora
16,938
52
3,094
9
83,527
255
103,929
318
17,
470
53
Agricultural Mowers
-
141
-
26
-
694
-
863

-
145
Combines
2,501
16
457
3
12.331
80
15.343
99
2.
579
17
Sprayers
85
638
16
117
420
3.148
522
3,917

88
658
Balers
IB
276
3
50
88
1,361
110
1,693

18
285
Irrigation Sets
788
403
144
74
3,883
1,989
4,832
2,475

812
416
Tillers >5 HP
0
8.082
0
1,476
2
39,852
2
49,586

0
8,335
Swathers
439
288
80
53
2.166
1,422
2,695
1,770

4 53
298
Hydro Power Units
21
-
4
-
102
-
127
-

21
-
Other Agricultural Equipment
158
56
29
10
781
277
972
345

163
58

-------
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
6
e
e
e
e
e
8
e
e
e
e
093
378
2*7
111
283
802
275
127
245
830
526
35
008
662
550
58
237
145
917
360
817
066
288
574
476
78
302
71
65
6
5
38
446
0
36
0
376
50
3
44
14
0
60
378
4
0
0
2
4
6
0
2
0
45
0
40
2
11
30
3
135
58
85
711
61
12
luuie 111, cont.
Estimated Non-Attairunent Equipment
Populations by Fuel Type
_ 18	19	20	21	22
_ .	 . _	^rov^'°lea	„ Son Diego AB	Spokane CMSA	St Louis CMSA Washington DC CMSA
Equipment Types	Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline
Trimmers/Edgers/Brush Cutters	36
Lawn Mowers	0
LeaC Blowers/Vacuums	0
Rear Engine Riding Mowers	8
Front Mowers	0
Chainsaws <4 BP	0
Shredders <5 BP	0
Tillers <5 BP	0
Lawn & Garden Tractors	410
Hood Splitters	1
Snowblowers	0
Chippers/Stump Grinders	29
Comnercial Turf Equipment	149
Other Lawn & Garden Equipment	0
All Terrain Vehicles (ATvs)	0
Minibikes	0
Off-Road Motorcycles	0
Golf Carta	0
Snowmobiles	0
Specialty Vehicles Carts	3
Generator Sets <50 HP	98
Pumps <50 BP	30
Air Compressors <50 HP	8
Gas Compressors <50 BP	0
Welders <50 BP	49
Pressure Hashers <50 BP	2
Aerial Lifts	8
Forklifts	76
Sweepers/Scrubbers	25
Other General Industrial Equipment	12
Other Material Handling Equipment	4
Asphalt Pavers	10
Tampers/Ramnera
Plate Compactors	1
Concrete Pavera	3
Rollers	54
Scrapers	27
Paving Equipment	27
Surfacing Equipment	0
Signal Boards	13
Trenchers	31
Bore/Drill Rigs	5
Excavators	38
Concrete/Industrial Saws	0
Cement and Mortar Mixers	2
Cranes	61
Graders	43
Off-Highway Trucks	10
Crushlng/Proc Equipment	4
Rough Terrain Forklifts	33
Rubber Tired Loaders	130
Rubber Tired Dozers	5
Tractora/Loaders/Backhoes	185
Crawler Tractors	177
Skid Steer Loaders	93
Off-Highway Tractors	24
Dumpers/Tenders	0
Other Construction Equipment	7
2-Wheel Tractors	0
Agricultural Tractors 5,669
Agricultural Mowers
Combines	837
Sprayers	28
Balers	6
Irrigation Sets	264
Tillers >5 HP	0
Swathers	147
Hydro Power Units	7
Other Agricultural Equipment	53
30,829
282
239,144
30
72,497
0
562,360
0
3,437
0
26,659
0
2,518
62
19,530
7
229
0
1,775
0
27,356
0
212,202
0
223
0
1,729
0
13,052
0
101,242
0
13,147
3,184
101,982
333
2,291
1
17,768
1
6,900
0
0
0
28
225
221
24
816
1,156
6,329
121
1,345
2
10,435
0
1,218
0
14,436
0
45
0
539
0
187
0
2,211
0
114
0
1,349
0
720
0
0
0
283
37
3.356
4
1,449
1,190
17,653
219
321
371
3,909
68
87
94
1,056
17
-
0
-
0
173
603
2,103
111
143
24
1,745
4
19
82
189
9
73
759
728
87
17
246
172
28
16
122
158
14
1
35
14
4
2
180
35
16
15
-
273
-
170
27
3,173
2
0
64
0
6
14
1,005
255
92
0
498
0
46
143
505
2.671
46
19
0
357
0
1
236
18
22
17
585
314
54
5
90
98
8
0
710
0
65
23
2
427
0
144
46
2,687
4
2
1.138
29
104
0
811
0
74
0
191
0
18
1
83
12
8
1
623
26
57
2
2,424
40
222
0
90
0
8
1
3,463
16
318
0
3,309
0
304
17
1,737
322
159
0
450
0
41
15
2
281
0
1
137
13
13
-
0
-
0
17
16,636
51
2,410
47
-
138
-
5
2,456
16
356
214
84
627
12
92
18
271
3
135
773
396
112
2,705
0
7,937
0
97
431
283
62
-
20
-
3
19
156
55
23
25.051
201
170,359
324
275,497
58,909
0
400,607
0
647.845
2,793
0
18,991
0
30.711
2,046
44
13,913
72
22,499
186
0
1,264
0
2,044
22,229
0
151,166
0
244,459
181
0
1,232
0
1,992
10,605
0
72,122
0
116,632
10,683
2,268
72,649
3,668
117,484
1,861
1
12.657
1
20,469
561
0
38,130
0
15,416
23
160
157
259
255
663
823
4,509
1,331
7,291
1,093
2
7,434
3
12,022
1,664
0
2,981
0
7,219
62
0
111
0
269
255
0
457
0
1,106
155
0
279
0
674
98
0
1,763
0
1,067
387
8
693
18
1,678
3,252
1,445
21.436
1,502
22,279
720
450
4,746
468
4,933
195
114
1,283
119
1,333
-
0
-
0
-
387
732
2,553
761
2,653
321
29
2,119
30
2,202
22
160
369
60
138
84
1.485
1,424
557
534
20
481
337
180
126
18
239
309
90
116
2
68
26
26
10
3
182
35
271
53
25
-
277
-
411
291
27
3,212
40
4,776
0
65
0
96
0
23
1,017
258
1. 512
383
0
504
0
749
0
245
511
2,704
760
4,020
33
0
361
0
537
2
239
18
355
27
29
592
318
880
473
9
91
100
135
148
0
718
0
1,068
0
39
2
432
2
643
246
47
2,719
70
4,044
3
1,152
30
1,713
44
0
820
0
1,220
0
0
194
0
288
0
1
84
12
126
18
2
631
26
938
39
4
2,453
40
3,648
60
0
91
0
135
0
1
3,505
16
5,213
24
0
3,349
0
4,980
0
30
1,758
326
2,614
484
0
456
0
678
0
26
2
285
3
423
1
139
13
207
19
-
0
-
0
-
7
11,619
36
26,129
80
20
-
96
-
217
2
1,715
11
3,857
25
91
58
438
131
985
39
12
189
28
426
57
540
277
1,215
622
1,150
0
5,544
1
12,467
41
301
198
678
445
-
14
-
32
-
8
109
39
244
87

-------
Appendix A
New 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 	70 (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 (1,200 to 1,400 unit sales per year)
•	Riding Turf Mowers
•	Thatchers/Aerators
•	Other Misc. Equipment
2)	Other Lawn and Garden Equipment includes the following:
•	Augers
•	Sickel Bar Mowers
•	Other Misc. Equipment
Al

-------
CLASS 2
AIRPORT SERVICE EQUIPMENT
Equipment Types
1.	Aircraft Support Equipment
2.	Terminal Tractors	
PSR Code
81
16
Notes:
1)	Aircraft Support Equipment includes the following:
•	Aircraft Load Lifters
•	De-icing Equipment/Heat and Start Units (about 630 unit sales
per year)
•	Ground Power Units
•	Utility Service Equipment
Baggage Conveyors (about 880 units/year) and Airport Service Vehicles (95
units/year) are also included in Airport Service Equipment.
2)	Terminal Tractors includes the following:
•	Push-Back Tractors
•	Tow Tractors
•	Yard Spotters
Aircraft Towing Tractors (480 unit sales per year) and Baggage Towing Tractors
(roughly 2,300 units/year) are included in Terminal Tractors.
A2

-------
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 Snow Grooming Equipment (300 units/year)
and Ice Maintenance Equipment (225 units/year).
A3

-------
CLASS 4
MARINE EQUIPMENT
Equipment Types
1.	Inboard Boards <250 HP
2.	Outboard Motors
3.	Personal Watercraft
Notes:
1) This category will basically use DNR registrations data
A4

-------
CLASS 5
LIGHT COMMERCIAL EQUIPMENT <50 HP
Equipment Types
1.	Generator Sets..
2.	Pumps	
3.	Air Compressors.
4.	Gas Compressors.
5.	Welders	
6.	Pressure Washers
PSR Code
9	(0-50 HP)
11 (0 - 50 HP)
10	(0 - 50 HP)
89 (0 - 50 HP)
17 (0 - 50 HP)
58 (0 - 50 HP)
Notes:
1) Generator Sets includes the following:
•	Baseload generators
•	Co-Generation generators
•	Marine generators
•	Military generators
•	Peaking generators
•	Portable generators
•	RV generators
•	Stand-by generators
A5

-------
CLASS 6
INDDSTRIAL EQUIPMENT
Equipment Types	PSR Code
1.	Aerial Lifts	64
2.	Forklifts	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 are also included in Aerial Lifts.
2)	Forklifts include those that are cushion tired and pneumatic tired.
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
5)	Other Material Handling Equipment includes Conveyors and Other Misc.
Material Handling Equipment.
A6

-------
CLASS 7
CONSTRUCTION EQUIPMENT
Equipment Types	PSR Code
1.	Asphalt Pavers	41
2.	Tampers/Rammers	95
3.	Plate 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.	Off-Highway Tractors	68
26.	Dumpers/Tenders	60
27.	Other Construction Equipment	36
A7

-------
Notes:
1)	Tampers/Rammers	are the same as Compactors.
2)	Concrete Pavers	include Slip-Form Pavers. Curb Pavers (about 540 unit
sales per year) 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
•	Other Misc. Paving Equipment
5)	Surfacing Equipment includes the following:
•	Asphalt/Gravel Planers
•	Asphalt Mixers/Agitators
•	Crack/Joint Routers
•	Pumper Kettles/Melters
•	Other Misc. Surfacing Equipment
Soil Stabilizers (about 35 units sold per year), Road Reclaimers and Pavement
Profilers (together comprising about 130 unit sales per year), and Roofing
Equipment are also included in Surfacing Equipment. Note that Cold Planers
are the same as Pavement Profilers.
6)	Trenchers include the following:
•	Portable/Walk-Behind Trenchers
•	Riding Trenchers
Cable Layers (about 260 units sold per year) and Wheel Trenchers (about 20
units/year) are also included in 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
A8

-------
Cranes include the following:
•	Pedestal Cranes
•	Rough Terrain Cranes
•	Shovel-Type Cranes
•	Straddle Cranes
•	Truck Mounted Cranes
Other Construction Equipment includes the following
•	Concrete Pumps (about 660 units sold per year)
•	Other Misc. Construction Equipment
A9

-------
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 (about 150 units sold per year).
2)	Sprayers includes the following:
•	Back Pack Sprayers
•	Self Propelled Sprayers
•	Towable/Tractor-Mounted Sprayers
Fertilizer Spreaders (about 2,000 units sold per year) are included in
Sprayers.
3)	Other Agricultural Equipment includes the following:
•	Harvesters
•	Specialized Cultivating Equipment
•	Specialized Harvesting Equipment
•	Other Misc. Agricultural Equipment
Frost/Wind Mills (about 100 units sold per year) are included in Other
Agricultural Equipment, as well as Forage Harvesters, Leaf Harvesters,
Fruit/Nut Harvesters, Orchard Pruners, Detasslers, Cotton Strippers, and
Cotton Pickers (all together only 440 units sold per year).
A10

-------
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 (about 55 units sold per year) are the same as Fellers/Bunchers.
2)	Portable Saw Mills (about 10 units sold per year) are included in
Concrete/Industrial Saws in the Construction - General Applications class.
All

-------