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 ------- |